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Merge branch 'develop' into pr/Blackhawke/3764

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Matthias 2020-09-12 08:33:46 +02:00
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4
.github/workflows/ci.yml vendored
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@ -88,7 +88,7 @@ jobs:
run: |
cp config.json.example config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --print-all
- name: Flake8
run: |
@ -150,7 +150,7 @@ jobs:
run: |
cp config.json.example config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --print-all
- name: Flake8
run: |

2
Dockerfile.armhf
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@ -1,7 +1,7 @@
FROM --platform=linux/arm/v7 python:3.7.7-slim-buster
RUN apt-get update \
&& apt-get -y install curl build-essential libssl-dev libatlas3-base libgfortran5 sqlite3 \
&& apt-get -y install curl build-essential libssl-dev libffi-dev libatlas3-base libgfortran5 sqlite3 \
&& apt-get clean \
&& pip install --upgrade pip \
&& echo "[global]\nextra-index-url=https://www.piwheels.org/simple" > /etc/pip.conf

1
README.md
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@ -123,7 +123,6 @@ Telegram is not mandatory. However, this is a great way to control your bot. Mor
- `/help`: Show help message
- `/version`: Show version
## Development branches
The project is currently setup in two main branches:

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config.json.example
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@ -7,7 +7,6 @@
"timeframe": "5m",
"dry_run": false,
"cancel_open_orders_on_exit": false,
"trailing_stop": false,
"unfilledtimeout": {
"buy": 10,
"sell": 30

1
config_binance.json.example
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@ -7,7 +7,6 @@
"timeframe": "5m",
"dry_run": true,
"cancel_open_orders_on_exit": false,
"trailing_stop": false,
"unfilledtimeout": {
"buy": 10,
"sell": 30

1
config_kraken.json.example
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@ -7,7 +7,6 @@
"timeframe": "5m",
"dry_run": true,
"cancel_open_orders_on_exit": false,
"trailing_stop": false,
"unfilledtimeout": {
"buy": 10,
"sell": 30

81
docs/backtesting.md
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@ -157,17 +157,32 @@ A backtesting result will look like that:
| ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 | 0 | 0 |
| LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 | 0 | 0 |
| TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 | 0 | 0 |
=============== SUMMARY METRICS ===============
| Metric | Value |
|-----------------------+---------------------|
| Backtesting from | 2019-01-01 00:00:00 |
| Backtesting to | 2019-05-01 00:00:00 |
| Total trades | 429 |
| First trade | 2019-01-01 18:30:00 |
| First trade Pair | EOS/USDT |
| Total Profit % | 152.41% |
| Trades per day | 3.575 |
| Best day | 25.27% |
| Worst day | -30.67% |
| Avg. Duration Winners | 4:23:00 |
| Avg. Duration Loser | 6:55:00 |
| | |
| Max Drawdown | 50.63% |
| Drawdown Start | 2019-02-15 14:10:00 |
| Drawdown End | 2019-04-11 18:15:00 |
| Market change | -5.88% |
===============================================
```
### Backtesting report table
The 1st table contains all trades the bot made, including "left open trades".
The 2nd table contains a recap of sell reasons.
This table can tell you which area needs some additional work (i.e. all `sell_signal` trades are losses, so we should disable the sell-signal or work on improving that).
The 3rd table contains all trades the bot had to `forcesell` at the end of the backtest period to present a full picture.
This is necessary to simulate realistic behaviour, since the backtest period has to end at some point, while realistically, you could leave the bot running forever.
These trades are also included in the first table, but are extracted separately for clarity.
The last line will give you the overall performance of your strategy,
here:
@ -196,6 +211,58 @@ On the other hand, if you set a too high `minimal_roi` like `"0": 0.55`
(55%), there is almost no chance that the bot will ever reach this profit.
Hence, keep in mind that your performance is an integral mix of all different elements of the strategy, your configuration, and the crypto-currency pairs you have set up.
### Sell reasons table
The 2nd table contains a recap of sell reasons.
This table can tell you which area needs some additional work (e.g. all or many of the `sell_signal` trades are losses, so you should work on improving the sell signal, or consider disabling it).
### Left open trades table
The 3rd table contains all trades the bot had to `forcesell` at the end of the backtesting period to present you the full picture.
This is necessary to simulate realistic behavior, since the backtest period has to end at some point, while realistically, you could leave the bot running forever.
These trades are also included in the first table, but are also shown separately in this table for clarity.
### Summary metrics
The last element of the backtest report is the summary metrics table.
It contains some useful key metrics about performance of your strategy on backtesting data.
```
=============== SUMMARY METRICS ===============
| Metric | Value |
|-----------------------+---------------------|
| Backtesting from | 2019-01-01 00:00:00 |
| Backtesting to | 2019-05-01 00:00:00 |
| Total trades | 429 |
| First trade | 2019-01-01 18:30:00 |
| First trade Pair | EOS/USDT |
| Total Profit % | 152.41% |
| Trades per day | 3.575 |
| Best day | 25.27% |
| Worst day | -30.67% |
| Avg. Duration Winners | 4:23:00 |
| Avg. Duration Loser | 6:55:00 |
| | |
| Max Drawdown | 50.63% |
| Drawdown Start | 2019-02-15 14:10:00 |
| Drawdown End | 2019-04-11 18:15:00 |
| Market change | -5.88% |
===============================================
```
- `Total trades`: Identical to the total trades of the backtest output table.
- `First trade`: First trade entered.
- `First trade pair`: Which pair was part of the first trade.
- `Backtesting from` / `Backtesting to`: Backtesting range (usually defined with the `--timerange` option).
- `Total Profit %`: Total profit per stake amount. Aligned to the TOTAL column of the first table.
- `Trades per day`: Total trades divided by the backtesting duration in days (this will give you information about how many trades to expect from the strategy).
- `Best day` / `Worst day`: Best and worst day based on daily profit.
- `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
- `Max Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced).
- `Drawdown Start` / `Drawdown End`: Start and end datetimes for this largest drawdown (can also be visualized via the `plot-dataframe` sub-command).
- `Market change`: Change of the market during the backtest period. Calculated as average of all pairs changes from the first to the last candle using the "close" column.
### Assumptions made by backtesting
Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:

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docs/bot-usage.md
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@ -5,6 +5,9 @@ This page explains the different parameters of the bot and how to run it.
!!! Note
If you've used `setup.sh`, don't forget to activate your virtual environment (`source .env/bin/activate`) before running freqtrade commands.
!!! Warning "Up-to-date clock"
The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
## Bot commands
```

35
docs/configuration.md
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@ -55,9 +55,9 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `process_only_new_candles` | Enable processing of indicators only when new candles arrive. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `minimal_roi` | **Required.** Set the threshold as ratio the bot will use to sell a trade. [More information below](#understand-minimal_roi). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
| `stoploss` | **Required.** Value as ratio of the stoploss used by the bot. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Float (as ratio)
| `trailing_stop` | Enables trailing stoploss (based on `stoploss` in either configuration or strategy file). More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Boolean
| `trailing_stop_positive` | Changes stoploss once profit has been reached. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Float
| `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0` (no offset).* <br> **Datatype:** Float
| `trailing_stop` | Enables trailing stoploss (based on `stoploss` in either configuration or strategy file). More details in the [stoploss documentation](stoploss.md#trailing-stop-loss). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Boolean
| `trailing_stop_positive` | Changes stoploss once profit has been reached. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-custom-positive-loss). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Float
| `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-only-once-the-trade-has-reached-a-certain-offset). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0` (no offset).* <br> **Datatype:** Float
| `trailing_only_offset_is_reached` | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `unfilledtimeout.buy` | **Required.** How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
| `unfilledtimeout.sell` | **Required.** How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
@ -278,24 +278,13 @@ This allows to buy using limit orders, sell using
limit-orders, and create stoplosses using market orders. It also allows to set the
stoploss "on exchange" which means stoploss order would be placed immediately once
the buy order is fulfilled.
If `stoploss_on_exchange` and `trailing_stop` are both set, then the bot will use `stoploss_on_exchange_interval` to check and update the stoploss on exchange periodically.
`order_types` can be set in the configuration file or in the strategy.
`order_types` set in the configuration file overwrites values set in the strategy as a whole, so you need to configure the whole `order_types` dictionary in one place.
If this is configured, the following 4 values (`buy`, `sell`, `stoploss` and
`stoploss_on_exchange`) need to be present, otherwise the bot will fail to start.
`emergencysell` is an optional value, which defaults to `market` and is used when creating stoploss on exchange orders fails.
The below is the default which is used if this is not configured in either strategy or configuration file.
Not all Exchanges support `stoploss_on_exchange`. If an exchange supports both limit and market stoploss orders, then the value of `stoploss` will be used to determine the stoploss type.
If `stoploss_on_exchange` uses limit orders, the exchange needs 2 prices, the stoploss_price and the Limit price.
`stoploss` defines the stop-price - and limit should be slightly below this.
This defaults to 0.99 / 1% (configurable via `stoploss_on_exchange_limit_ratio`).
Calculation example: we bought the asset at 100$.
Stop-price is 95$, then limit would be `95 * 0.99 = 94.05$` - so the stoploss will happen between 95$ and 94.05$.
For information on (`emergencysell`,`stoploss_on_exchange`,`stoploss_on_exchange_interval`,`stoploss_on_exchange_limit_ratio`) please see stop loss documentation [stop loss on exchange](stoploss.md)
Syntax for Strategy:
@ -663,24 +652,28 @@ Filters low-value coins which would not allow setting stoplosses.
#### PriceFilter
The `PriceFilter` allows filtering of pairs by price. Currently the following price filters are supported:
* `min_price`
* `max_price`
* `low_price_ratio`
The `min_price` setting removes pairs where the price is below the specified price. This is useful if you wish to avoid trading very low-priced pairs.
This option is disabled by default, and will only apply if set to <> 0.
This option is disabled by default, and will only apply if set to > 0.
The `max_price` setting removes pairs where the price is above the specified price. This is useful if you wish to trade only low-priced pairs.
This option is disabled by default, and will only apply if set to <> 0.
This option is disabled by default, and will only apply if set to > 0.
The `low_price_ratio` setting removes pairs where a raise of 1 price unit (pip) is above the `low_price_ratio` ratio.
This option is disabled by default, and will only apply if set to <> 0.
This option is disabled by default, and will only apply if set to > 0.
For `PriceFiler` at least one of its `min_price`, `max_price` or `low_price_ratio` settings must be applied.
Calculation example:
Min price precision is 8 decimals. If price is 0.00000011 - one step would be 0.00000012 - which is almost 10% higher than the previous value.
Min price precision for SHITCOIN/BTC is 8 decimals. If its price is 0.00000011 - one price step above would be 0.00000012, which is ~9% higher than the previous price value. You may filter out this pair by using PriceFilter with `low_price_ratio` set to 0.09 (9%) or with `min_price` set to 0.00000011, correspondingly.
These pairs are dangerous since it may be impossible to place the desired stoploss - and often result in high losses.
!!! Warning "Low priced pairs"
Low priced pairs with high "1 pip movements" are dangerous since they are often illiquid and it may also be impossible to place the desired stoploss, which can often result in high losses since price needs to be rounded to the next tradable price - so instead of having a stoploss of -5%, you could end up with a stoploss of -9% simply due to price rounding.
#### ShuffleFilter

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docs/data-download.md
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@ -15,61 +15,91 @@ Otherwise `--exchange` becomes mandatory.
### Usage
```
usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [-p PAIRS [PAIRS ...]]
[--pairs-file FILE] [--days INT] [--dl-trades] [--exchange EXCHANGE]
usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[-p PAIRS [PAIRS ...]] [--pairs-file FILE]
[--days INT] [--dl-trades]
[--exchange EXCHANGE]
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]]
[--erase] [--data-format-ohlcv {json,jsongz}] [--data-format-trades {json,jsongz}]
[--erase]
[--data-format-ohlcv {json,jsongz,hdf5}]
[--data-format-trades {json,jsongz,hdf5}]
optional arguments:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Show profits for only these pairs. Pairs are space-separated.
Show profits for only these pairs. Pairs are space-
separated.
--pairs-file FILE File containing a list of pairs to download.
--days INT Download data for given number of days.
--dl-trades Download trades instead of OHLCV data. The bot will resample trades to the desired timeframe as specified as
--timeframes/-t.
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no config is provided.
--dl-trades Download trades instead of OHLCV data. The bot will
resample trades to the desired timeframe as specified
as --timeframes/-t.
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
config is provided.
-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]
Specify which tickers to download. Space-separated list. Default: `1m 5m`.
--erase Clean all existing data for the selected exchange/pairs/timeframes.
--data-format-ohlcv {json,jsongz}
Storage format for downloaded candle (OHLCV) data. (default: `json`).
--data-format-trades {json,jsongz}
Storage format for downloaded trades data. (default: `jsongz`).
Specify which tickers to download. Space-separated
list. Default: `1m 5m`.
--erase Clean all existing data for the selected
exchange/pairs/timeframes.
--data-format-ohlcv {json,jsongz,hdf5}
Storage format for downloaded candle (OHLCV) data.
(default: `json`).
--data-format-trades {json,jsongz,hdf5}
Storage format for downloaded trades data. (default:
`jsongz`).
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are: 'syslog', 'journald'. See the documentation for more details.
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`). Multiple --config options may be used. Can be set to `-`
to read config from stdin.
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
### Data format
Freqtrade currently supports 2 dataformats, `json` (plain "text" json files) and `jsongz` (a gzipped version of json files).
Freqtrade currently supports 3 data-formats for both OHLCV and trades data:
* `json` (plain "text" json files)
* `jsongz` (a gzip-zipped version of json files)
* `hdf5` (a high performance datastore)
By default, OHLCV data is stored as `json` data, while trades data is stored as `jsongz` data.
This can be changed via the `--data-format-ohlcv` and `--data-format-trades` parameters respectivly.
This can be changed via the `--data-format-ohlcv` and `--data-format-trades` command line arguments respectively.
To persist this change, you can should also add the following snippet to your configuration, so you don't have to insert the above arguments each time:
If the default dataformat has been changed during download, then the keys `dataformat_ohlcv` and `dataformat_trades` in the configuration file need to be adjusted to the selected dataformat as well.
``` jsonc
// ...
"dataformat_ohlcv": "hdf5",
"dataformat_trades": "hdf5",
// ...
```
If the default data-format has been changed during download, then the keys `dataformat_ohlcv` and `dataformat_trades` in the configuration file need to be adjusted to the selected dataformat as well.
!!! Note
You can convert between data-formats using the [convert-data](#subcommand-convert-data) and [convert-trade-data](#subcommand-convert-trade-data) methods.
You can convert between data-formats using the [convert-data](#sub-command-convert-data) and [convert-trade-data](#sub-command-convert-trade-data) methods.
#### Subcommand convert data
#### Sub-command convert data
```
usage: freqtrade convert-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[-p PAIRS [PAIRS ...]] --format-from
{json,jsongz} --format-to {json,jsongz}
[--erase]
{json,jsongz,hdf5} --format-to
{json,jsongz,hdf5} [--erase]
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]]
optional arguments:
@ -77,9 +107,9 @@ optional arguments:
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Show profits for only these pairs. Pairs are space-
separated.
--format-from {json,jsongz}
--format-from {json,jsongz,hdf5}
Source format for data conversion.
--format-to {json,jsongz}
--format-to {json,jsongz,hdf5}
Destination format for data conversion.
--erase Clean all existing data for the selected
exchange/pairs/timeframes.
@ -94,9 +124,10 @@ Common arguments:
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`).
Multiple --config options may be used. Can be set to
`-` to read config from stdin.
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
@ -112,23 +143,23 @@ It'll also remove original json data files (`--erase` parameter).
freqtrade convert-data --format-from json --format-to jsongz --datadir ~/.freqtrade/data/binance -t 5m 15m --erase
```
#### Subcommand convert-trade data
#### Sub-command convert trade data
```
usage: freqtrade convert-trade-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[-p PAIRS [PAIRS ...]] --format-from
{json,jsongz} --format-to {json,jsongz}
[--erase]
{json,jsongz,hdf5} --format-to
{json,jsongz,hdf5} [--erase]
optional arguments:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Show profits for only these pairs. Pairs are space-
separated.
--format-from {json,jsongz}
--format-from {json,jsongz,hdf5}
Source format for data conversion.
--format-to {json,jsongz}
--format-to {json,jsongz,hdf5}
Destination format for data conversion.
--erase Clean all existing data for the selected
exchange/pairs/timeframes.
@ -140,13 +171,15 @@ Common arguments:
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`).
Multiple --config options may be used. Can be set to
`-` to read config from stdin.
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
##### Example converting trades
@ -158,21 +191,21 @@ It'll also remove original jsongz data files (`--erase` parameter).
freqtrade convert-trade-data --format-from jsongz --format-to json --datadir ~/.freqtrade/data/kraken --erase
```
### Subcommand list-data
### Sub-command list-data
You can get a list of downloaded data using the `list-data` subcommand.
You can get a list of downloaded data using the `list-data` sub-command.
```
usage: freqtrade list-data [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--userdir PATH] [--exchange EXCHANGE]
[--data-format-ohlcv {json,jsongz}]
[--data-format-ohlcv {json,jsongz,hdf5}]
[-p PAIRS [PAIRS ...]]
optional arguments:
-h, --help show this help message and exit
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
config is provided.
--data-format-ohlcv {json,jsongz}
--data-format-ohlcv {json,jsongz,hdf5}
Storage format for downloaded candle (OHLCV) data.
(default: `json`).
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
@ -194,6 +227,7 @@ Common arguments:
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
#### Example list-data
@ -257,7 +291,7 @@ This will download historical candle (OHLCV) data for all the currency pairs you
### Trades (tick) data
By default, `download-data` subcommand downloads Candles (OHLCV) data. Some exchanges also provide historic trade-data via their API.
By default, `download-data` sub-command downloads Candles (OHLCV) data. Some exchanges also provide historic trade-data via their API.
This data can be useful if you need many different timeframes, since it is only downloaded once, and then resampled locally to the desired timeframes.
Since this data is large by default, the files use gzip by default. They are stored in your data-directory with the naming convention of `<pair>-trades.json.gz` (`ETH_BTC-trades.json.gz`). Incremental mode is also supported, as for historic OHLCV data, so downloading the data once per week with `--days 8` will create an incremental data-repository.

13
docs/deprecated.md
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@ -9,21 +9,20 @@ and are no longer supported. Please avoid their usage in your configuration.
### the `--refresh-pairs-cached` command line option
`--refresh-pairs-cached` in the context of backtesting, hyperopt and edge allows to refresh candle data for backtesting.
Since this leads to much confusion, and slows down backtesting (while not being part of backtesting) this has been singled out
as a seperate freqtrade subcommand `freqtrade download-data`.
Since this leads to much confusion, and slows down backtesting (while not being part of backtesting) this has been singled out as a separate freqtrade sub-command `freqtrade download-data`.
This command line option was deprecated in 2019.7-dev (develop branch) and removed in 2019.9 (master branch).
This command line option was deprecated in 2019.7-dev (develop branch) and removed in 2019.9.
### The **--dynamic-whitelist** command line option
This command line option was deprecated in 2018 and removed freqtrade 2019.6-dev (develop branch)
and in freqtrade 2019.7 (master branch).
and in freqtrade 2019.7.
### the `--live` command line option
`--live` in the context of backtesting allowed to download the latest tick data for backtesting.
Did only download the latest 500 candles, so was ineffective in getting good backtest data.
Removed in 2019-7-dev (develop branch) and in freqtrade 2019-8 (master branch)
Removed in 2019-7-dev (develop branch) and in freqtrade 2019.8.
### Allow running multiple pairlists in sequence
@ -31,6 +30,6 @@ The former `"pairlist"` section in the configuration has been removed, and is re
The old section of configuration parameters (`"pairlist"`) has been deprecated in 2019.11 and has been removed in 2020.4.
### deprecation of bidVolume and askVolume from volumepairlist
### deprecation of bidVolume and askVolume from volume-pairlist
Since only quoteVolume can be compared between assets, the other options (bidVolume, askVolume) have been deprecated in 2020.4.
Since only quoteVolume can be compared between assets, the other options (bidVolume, askVolume) have been deprecated in 2020.4, and have been removed in 2020.9.

59
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@ -10,6 +10,15 @@ Documentation is available at [https://freqtrade.io](https://www.freqtrade.io/)
Special fields for the documentation (like Note boxes, ...) can be found [here](https://squidfunk.github.io/mkdocs-material/extensions/admonition/).
To test the documentation locally use the following commands.
``` bash
pip install -r docs/requirements-docs.txt
mkdocs serve
```
This will spin up a local server (usually on port 8000) so you can see if everything looks as you'd like it to.
## Developer setup
To configure a development environment, best use the `setup.sh` script and answer "y" when asked "Do you want to install dependencies for dev [y/N]? ".
@ -52,6 +61,7 @@ The fastest and easiest way to start up is to use docker-compose.develop which g
* [docker-compose](https://docs.docker.com/compose/install/)
#### Starting the bot
##### Use the develop dockerfile
``` bash
@ -74,7 +84,7 @@ docker-compose up
docker-compose build
```
##### Execing (effectively SSH into the container)
##### Executing (effectively SSH into the container)
The `exec` command requires that the container already be running, if you want to start it
that can be effected by `docker-compose up` or `docker-compose run freqtrade_develop`
@ -85,6 +95,35 @@ docker-compose exec freqtrade_develop /bin/bash
![image](https://user-images.githubusercontent.com/419355/65456522-ba671a80-de06-11e9-9598-df9ca0d8dcac.png)
## ErrorHandling
Freqtrade Exceptions all inherit from `FreqtradeException`.
This general class of error should however not be used directly. Instead, multiple specialized sub-Exceptions exist.
Below is an outline of exception inheritance hierarchy:
```
+ FreqtradeException
|
+---+ OperationalException
|
+---+ DependencyException
| |
| +---+ PricingError
| |
| +---+ ExchangeError
| |
| +---+ TemporaryError
| |
| +---+ DDosProtection
| |
| +---+ InvalidOrderException
| |
| +---+ RetryableOrderError
|
+---+ StrategyError
```
## Modules
### Dynamic Pairlist
@ -98,7 +137,7 @@ First of all, have a look at the [VolumePairList](https://github.com/freqtrade/f
This is a simple Handler, which however serves as a good example on how to start developing.
Next, modify the classname of the Handler (ideally align this with the module filename).
Next, modify the class-name of the Handler (ideally align this with the module filename).
The base-class provides an instance of the exchange (`self._exchange`) the pairlist manager (`self._pairlistmanager`), as well as the main configuration (`self._config`), the pairlist dedicated configuration (`self._pairlistconfig`) and the absolute position within the list of pairlists.
@ -118,7 +157,7 @@ Configuration for the chain of Pairlist Handlers is done in the bot configuratio
By convention, `"number_assets"` is used to specify the maximum number of pairs to keep in the pairlist. Please follow this to ensure a consistent user experience.
Additional parameters can be configured as needed. For instance, `VolumePairList` uses `"sort_key"` to specify the sorting value - however feel free to specify whatever is necessary for your great algorithm to be successfull and dynamic.
Additional parameters can be configured as needed. For instance, `VolumePairList` uses `"sort_key"` to specify the sorting value - however feel free to specify whatever is necessary for your great algorithm to be successful and dynamic.
#### short_desc
@ -134,7 +173,7 @@ This is called with each iteration of the bot (only if the Pairlist Handler is a
It must return the resulting pairlist (which may then be passed into the chain of Pairlist Handlers).
Validations are optional, the parent class exposes a `_verify_blacklist(pairlist)` and `_whitelist_for_active_markets(pairlist)` to do default filtering. Use this if you limit your result to a certain number of pairs - so the endresult is not shorter than expected.
Validations are optional, the parent class exposes a `_verify_blacklist(pairlist)` and `_whitelist_for_active_markets(pairlist)` to do default filtering. Use this if you limit your result to a certain number of pairs - so the end-result is not shorter than expected.
#### filter_pairlist
@ -142,7 +181,7 @@ This method is called for each Pairlist Handler in the chain by the pairlist man
This is called with each iteration of the bot - so consider implementing caching for compute/network heavy calculations.
It get's passed a pairlist (which can be the result of previous pairlists) as well as `tickers`, a pre-fetched version of `get_tickers()`.
It gets passed a pairlist (which can be the result of previous pairlists) as well as `tickers`, a pre-fetched version of `get_tickers()`.
The default implementation in the base class simply calls the `_validate_pair()` method for each pair in the pairlist, but you may override it. So you should either implement the `_validate_pair()` in your Pairlist Handler or override `filter_pairlist()` to do something else.
@ -172,7 +211,7 @@ Most exchanges supported by CCXT should work out of the box.
Check if the new exchange supports Stoploss on Exchange orders through their API.
Since CCXT does not provide unification for Stoploss On Exchange yet, we'll need to implement the exchange-specific parameters ourselfs. Best look at `binance.py` for an example implementation of this. You'll need to dig through the documentation of the Exchange's API on how exactly this can be done. [CCXT Issues](https://github.com/ccxt/ccxt/issues) may also provide great help, since others may have implemented something similar for their projects.
Since CCXT does not provide unification for Stoploss On Exchange yet, we'll need to implement the exchange-specific parameters ourselves. Best look at `binance.py` for an example implementation of this. You'll need to dig through the documentation of the Exchange's API on how exactly this can be done. [CCXT Issues](https://github.com/ccxt/ccxt/issues) may also provide great help, since others may have implemented something similar for their projects.
### Incomplete candles
@ -245,6 +284,7 @@ git checkout -b new_release <commitid>
Determine if crucial bugfixes have been made between this commit and the current state, and eventually cherry-pick these.
* Merge the release branch (master) into this branch.
* Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7.1` should we need to do a second release that month. Version numbers must follow allowed versions from PEP0440 to avoid failures pushing to pypi.
* Commit this part
* push that branch to the remote and create a PR against the master branch
@ -252,14 +292,14 @@ Determine if crucial bugfixes have been made between this commit and the current
### Create changelog from git commits
!!! Note
Make sure that the master branch is uptodate!
Make sure that the master branch is up-to-date!
``` bash
# Needs to be done before merging / pulling that branch.
git log --oneline --no-decorate --no-merges master..new_release
```
To keep the release-log short, best wrap the full git changelog into a collapsible details secction.
To keep the release-log short, best wrap the full git changelog into a collapsible details section.
```markdown
<details>
@ -283,6 +323,9 @@ Once the PR against master is merged (best right after merging):
### pypi
!!! Note
This process is now automated as part of Github Actions.
To create a pypi release, please run the following commands:
Additional requirement: `wheel`, `twine` (for uploading), account on pypi with proper permissions.

186
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@ -1,145 +1,7 @@
# Using Freqtrade with Docker
## Install Docker
Start by downloading and installing Docker CE for your platform:
* [Mac](https://docs.docker.com/docker-for-mac/install/)
* [Windows](https://docs.docker.com/docker-for-windows/install/)
* [Linux](https://docs.docker.com/install/)
Optionally, [docker-compose](https://docs.docker.com/compose/install/) should be installed and available to follow the [docker quick start guide](#docker-quick-start).
Once you have Docker installed, simply prepare the config file (e.g. `config.json`) and run the image for `freqtrade` as explained below.
## Freqtrade with docker-compose
Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/), as well as a [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) ready for usage.
!!! Note
The following section assumes that docker and docker-compose is installed and available to the logged in user.
!!! Note
All below comands use relative directories and will have to be executed from the directory containing the `docker-compose.yml` file.
!!! Note "Docker on Raspberry"
If you're running freqtrade on a Raspberry PI, you must change the image from `freqtradeorg/freqtrade:master` to `freqtradeorg/freqtrade:master_pi` or `freqtradeorg/freqtrade:develop_pi`, otherwise the image will not work.
### Docker quick start
Create a new directory and place the [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) in this directory.
``` bash
mkdir ft_userdata
cd ft_userdata/
# Download the docker-compose file from the repository
curl https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docker-compose.yml -o docker-compose.yml
# Pull the freqtrade image
docker-compose pull
# Create user directory structure
docker-compose run --rm freqtrade create-userdir --userdir user_data
# Create configuration - Requires answering interactive questions
docker-compose run --rm freqtrade new-config --config user_data/config.json
```
The above snippet creates a new directory called "ft_userdata", downloads the latest compose file and pulls the freqtrade image.
The last 2 steps in the snippet create the directory with user-data, as well as (interactively) the default configuration based on your selections.
!!! Note
You can edit the configuration at any time, which is available as `user_data/config.json` (within the directory `ft_userdata`) when using the above configuration.
#### Adding your strategy
The configuration is now available as `user_data/config.json`.
You should now copy your strategy to `user_data/strategies/` - and add the Strategy class name to the `docker-compose.yml` file, replacing `SampleStrategy`. If you wish to run the bot with the SampleStrategy, just leave it as it is.
!!! Warning
The `SampleStrategy` is there for your reference and give you ideas for your own strategy.
Please always backtest the strategy and use dry-run for some time before risking real money!
Once this is done, you're ready to launch the bot in trading mode (Dry-run or Live-trading, depending on your answer to the corresponding question you made above).
``` bash
docker-compose up -d
```
#### Docker-compose logs
Logs will be written to `user_data/logs/freqtrade.log`.
Alternatively, you can check the latest logs using `docker-compose logs -f`.
#### Database
The database will be in the user_data directory as well, and will be called `user_data/tradesv3.sqlite`.
#### Updating freqtrade with docker-compose
To update freqtrade when using docker-compose is as simple as running the following 2 commands:
``` bash
# Download the latest image
docker-compose pull
# Restart the image
docker-compose up -d
```
This will first pull the latest image, and will then restart the container with the just pulled version.
!!! Note
You should always check the changelog for breaking changes / manual interventions required and make sure the bot starts correctly after the update.
#### Going from here
Advanced users may edit the docker-compose file further to include all possible options or arguments.
All possible freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command> <optional arguments>`.
!!! Note "`docker-compose run --rm`"
Including `--rm` will clean up the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
##### Example: Download data with docker-compose
Download backtesting data for 5 days for the pair ETH/BTC and 1h timeframe from Binance. The data will be stored in the directory `user_data/data/` on the host.
``` bash
docker-compose run --rm freqtrade download-data --pairs ETH/BTC --exchange binance --days 5 -t 1h
```
Head over to the [Data Downloading Documentation](data-download.md) for more details on downloading data.
##### Example: Backtest with docker-compose
Run backtesting in docker-containers for SampleStrategy and specified timerange of historical data, on 5m timeframe:
``` bash
docker-compose run --rm freqtrade backtesting --config user_data/config.json --strategy SampleStrategy --timerange 20190801-20191001 -i 5m
```
Head over to the [Backtesting Documentation](backtesting.md) to learn more.
#### Additional dependencies with docker-compose
If your strategy requires dependencies not included in the default image (like [technical](https://github.com/freqtrade/technical)) - it will be necessary to build the image on your host.
For this, please create a Dockerfile containing installation steps for the additional dependencies (have a look at [Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/Dockerfile.technical) for an example).
You'll then also need to modify the `docker-compose.yml` file and uncomment the build step, as well as rename the image to avoid naming collisions.
``` yaml
image: freqtrade_custom
build:
context: .
dockerfile: "./Dockerfile.<yourextension>"
```
You can then run `docker-compose build` to build the docker image, and run it using the commands described above.
## Freqtrade with docker without docker-compose
!!! Warning
The below documentation is provided for completeness and assumes that you are somewhat familiar with running docker containers. If you're just starting out with docker, we recommend to follow the [Freqtrade with docker-compose](#freqtrade-with-docker-compose) instructions.
The below documentation is provided for completeness and assumes that you are familiar with running docker containers. If you're just starting out with Docker, we recommend to follow the [Quickstart](docker.md) instructions.
### Download the official Freqtrade docker image
@ -148,9 +10,9 @@ Pull the image from docker hub.
Branches / tags available can be checked out on [Dockerhub tags page](https://hub.docker.com/r/freqtradeorg/freqtrade/tags/).
```bash
docker pull freqtradeorg/freqtrade:develop
docker pull freqtradeorg/freqtrade:master
# Optionally tag the repository so the run-commands remain shorter
docker tag freqtradeorg/freqtrade:develop freqtrade
docker tag freqtradeorg/freqtrade:master freqtrade
```
To update the image, simply run the above commands again and restart your running container.
@ -190,20 +52,19 @@ cp -n config.json.example config.json
#### Create your database file
Production
```bash
touch tradesv3.sqlite
````
Dry-Run
=== "Dry-Run"
``` bash
touch tradesv3.dryrun.sqlite
```
!!! Note
Make sure to use the path to this file when starting the bot in docker.
=== "Production"
``` bash
touch tradesv3.sqlite
```
!!! Warning "Database File Path"
Make sure to use the path to the correct database file when starting the bot in Docker.
### Build your own Docker image
@ -221,8 +82,8 @@ If you are developing using Docker, use `Dockerfile.develop` to build a dev Dock
docker build -f Dockerfile.develop -t freqtrade-dev .
```
!!! Note
For security reasons, your configuration file will not be included in the image, you will need to bind mount it. It is also advised to bind mount an SQLite database file (see the "5. Run a restartable docker image" section) to keep it between updates.
!!! Warning "Include your config file manually"
For security reasons, your configuration file will not be included in the image, you will need to bind mount it. It is also advised to bind mount an SQLite database file (see [5. Run a restartable docker image](#run-a-restartable-docker-image)") to keep it between updates.
#### Verify the Docker image
@ -243,15 +104,14 @@ docker run --rm -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
```
!!! Warning
In this example, the database will be created inside the docker instance and will be lost when you will refresh your image.
In this example, the database will be created inside the docker instance and will be lost when you refresh your image.
#### Adjust timezone
By default, the container will use UTC timezone.
Should you find this irritating please add the following to your docker commands:
##### Linux
If you would like to change the timezone use the following commands:
=== "Linux"
``` bash
-v /etc/timezone:/etc/timezone:ro
@ -259,21 +119,21 @@ Should you find this irritating please add the following to your docker commands
docker run --rm -v /etc/timezone:/etc/timezone:ro -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
```
##### MacOS
There is known issue in OSX Docker versions after 17.09.1, whereby `/etc/localtime` cannot be shared causing Docker to not start. A work-around for this is to start with the following cmd.
=== "MacOS"
```bash
docker run --rm -e TZ=`ls -la /etc/localtime | cut -d/ -f8-9` -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
```
!!! Note "MacOS Issues"
The OSX Docker versions after 17.09.1 have a known issue whereby `/etc/localtime` cannot be shared causing Docker to not start.<br>
A work-around for this is to start with the MacOS command above
More information on this docker issue and work-around can be read [here](https://github.com/docker/for-mac/issues/2396).
### Run a restartable docker image
To run a restartable instance in the background (feel free to place your configuration and database files wherever it feels comfortable on your filesystem).
#### Move your config file and database
#### 1. Move your config file and database
The following will assume that you place your configuration / database files to `~/.freqtrade`, which is a hidden directory in your home directory. Feel free to use a different directory and replace the directory in the upcomming commands.
@ -283,7 +143,7 @@ mv config.json ~/.freqtrade
mv tradesv3.sqlite ~/.freqtrade
```
#### Run the docker image
#### 2. Run the docker image
```bash
docker run -d \

162
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@ -0,0 +1,162 @@
# Using Freqtrade with Docker
## Install Docker
Start by downloading and installing Docker CE for your platform:
* [Mac](https://docs.docker.com/docker-for-mac/install/)
* [Windows](https://docs.docker.com/docker-for-windows/install/)
* [Linux](https://docs.docker.com/install/)
Optionally, [`docker-compose`](https://docs.docker.com/compose/install/) should be installed and available to follow the [docker quick start guide](#docker-quick-start).
Once you have Docker installed, simply prepare the config file (e.g. `config.json`) and run the image for `freqtrade` as explained below.
## Freqtrade with docker-compose
Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/), as well as a [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) ready for usage.
!!! Note
- The following section assumes that `docker` and `docker-compose` are installed and available to the logged in user.
- All below commands use relative directories and will have to be executed from the directory containing the `docker-compose.yml` file.
### Docker quick start
Create a new directory and place the [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) in this directory.
=== "PC/MAC/Linux"
``` bash
mkdir ft_userdata
cd ft_userdata/
# Download the docker-compose file from the repository
curl https://raw.githubusercontent.com/freqtrade/freqtrade/master/docker-compose.yml -o docker-compose.yml
# Pull the freqtrade image
docker-compose pull
# Create user directory structure
docker-compose run --rm freqtrade create-userdir --userdir user_data
# Create configuration - Requires answering interactive questions
docker-compose run --rm freqtrade new-config --config user_data/config.json
```
=== "RaspberryPi"
``` bash
mkdir ft_userdata
cd ft_userdata/
# Download the docker-compose file from the repository
curl https://raw.githubusercontent.com/freqtrade/freqtrade/master/docker-compose.yml -o docker-compose.yml
# Pull the freqtrade image
docker-compose pull
# Create user directory structure
docker-compose run --rm freqtrade create-userdir --userdir user_data
# Create configuration - Requires answering interactive questions
docker-compose run --rm freqtrade new-config --config user_data/config.json
```
!!! Note "Change your docker Image"
You have to change the docker image in the docker-compose file for your Raspberry build to work properly.
``` yml
image: freqtradeorg/freqtrade:master_pi
# image: freqtradeorg/freqtrade:develop_pi
```
The above snippet creates a new directory called `ft_userdata`, downloads the latest compose file and pulls the freqtrade image.
The last 2 steps in the snippet create the directory with `user_data`, as well as (interactively) the default configuration based on your selections.
!!! Question "How to edit the bot configuration?"
You can edit the configuration at any time, which is available as `user_data/config.json` (within the directory `ft_userdata`) when using the above configuration.
You can also change the both Strategy and commands by editing the `docker-compose.yml` file.
#### Adding a custom strategy
1. The configuration is now available as `user_data/config.json`
2. Copy a custom strategy to the directory `user_data/strategies/`
3. add the Strategy' class name to the `docker-compose.yml` file
The `SampleStrategy` is run by default.
!!! Warning "`SampleStrategy` is just a demo!"
The `SampleStrategy` is there for your reference and give you ideas for your own strategy.
Please always backtest the strategy and use dry-run for some time before risking real money!
Once this is done, you're ready to launch the bot in trading mode (Dry-run or Live-trading, depending on your answer to the corresponding question you made above).
``` bash
docker-compose up -d
```
#### Docker-compose logs
Logs will be located at: `user_data/logs/freqtrade.log`.
You can check the latest log with the command `docker-compose logs -f`.
#### Database
The database will be at: `user_data/tradesv3.sqlite`
#### Updating freqtrade with docker-compose
To update freqtrade when using `docker-compose` is as simple as running the following 2 commands:
``` bash
# Download the latest image
docker-compose pull
# Restart the image
docker-compose up -d
```
This will first pull the latest image, and will then restart the container with the just pulled version.
!!! Warning "Check the Changelog"
You should always check the changelog for breaking changes / manual interventions required and make sure the bot starts correctly after the update.
### Editing the docker-compose file
Advanced users may edit the docker-compose file further to include all possible options or arguments.
All possible freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command> <optional arguments>`.
!!! Note "`docker-compose run --rm`"
Including `--rm` will clean up the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
#### Example: Download data with docker-compose
Download backtesting data for 5 days for the pair ETH/BTC and 1h timeframe from Binance. The data will be stored in the directory `user_data/data/` on the host.
``` bash
docker-compose run --rm freqtrade download-data --pairs ETH/BTC --exchange binance --days 5 -t 1h
```
Head over to the [Data Downloading Documentation](data-download.md) for more details on downloading data.
#### Example: Backtest with docker-compose
Run backtesting in docker-containers for SampleStrategy and specified timerange of historical data, on 5m timeframe:
``` bash
docker-compose run --rm freqtrade backtesting --config user_data/config.json --strategy SampleStrategy --timerange 20190801-20191001 -i 5m
```
Head over to the [Backtesting Documentation](backtesting.md) to learn more.
### Additional dependencies with docker-compose
If your strategy requires dependencies not included in the default image (like [technical](https://github.com/freqtrade/technical)) - it will be necessary to build the image on your host.
For this, please create a Dockerfile containing installation steps for the additional dependencies (have a look at [Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/Dockerfile.technical) for an example).
You'll then also need to modify the `docker-compose.yml` file and uncomment the build step, as well as rename the image to avoid naming collisions.
``` yaml
image: freqtrade_custom
build:
context: .
dockerfile: "./Dockerfile.<yourextension>"
```
You can then run `docker-compose build` to build the docker image, and run it using the commands described above.

205
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@ -1,92 +1,142 @@
# Edge positioning
This page explains how to use Edge Positioning module in your bot in order to enter into a trade only if the trade has a reasonable win rate and risk reward ratio, and consequently adjust your position size and stoploss.
The `Edge Positioning` module uses probability to calculate your win rate and risk reward ration. It will use these statistics to control your strategy trade entry points, position side and, stoploss.
!!! Warning
Edge positioning is not compatible with dynamic (volume-based) whitelist.
`Edge positioning` is not compatible with dynamic (volume-based) whitelist.
!!! Note
Edge does not consider anything other than *its own* buy/sell/stoploss signals. It ignores the stoploss, trailing stoploss, and ROI settings in the strategy configuration file.
Therefore, it is important to understand that Edge can improve the performance of some trading strategies but *decrease* the performance of others.
`Edge Positioning` only considers *its own* buy/sell/stoploss signals. It ignores the stoploss, trailing stoploss, and ROI settings in the strategy configuration file.
`Edge Positioning` improves the performance of some trading strategies and *decreases* the performance of others.
## Introduction
Trading is all about probability. No one can claim that he has a strategy working all the time. You have to assume that sometimes you lose.
Trading strategies are not perfect. They are frameworks that are susceptible to the market and its indicators. Because the market is not at all predictable, sometimes a strategy will win and sometimes the same strategy will lose.
But it doesn't mean there is no rule, it only means rules should work "most of the time". Let's play a game: we toss a coin, heads: I give you 10$, tails: you give me 10$. Is it an interesting game? No, it's quite boring, isn't it?
To obtain an edge in the market, a strategy has to make more money than it loses. Making money in trading is not only about *how often* the strategy makes or loses money.
But let's say the probability that we have heads is 80% (because our coin has the displaced distribution of mass or other defect), and the probability that we have tails is 20%. Now it is becoming interesting...
!!! tip "It doesn't matter how often, but how much!"
A bad strategy might make 1 penny in *ten* transactions but lose 1 dollar in *one* transaction. If one only checks the number of winning trades, it would be misleading to think that the strategy is actually making a profit.
That means 10$ X 80% versus 10$ X 20%. 8$ versus 2$. That means over time you will win 8$ risking only 2$ on each toss of coin.
The Edge Positioning module seeks to improve a strategy's winning probability and the money that the strategy will make *on the long run*.
Let's complicate it more: you win 80% of the time but only 2$, I win 20% of the time but 8$. The calculation is: 80% X 2$ versus 20% X 8$. It is becoming boring again because overtime you win $1.6$ (80% X 2$) and me $1.6 (20% X 8$) too.
We raise the following question[^1]:
The question is: How do you calculate that? How do you know if you wanna play?
!!! Question "Which trade is a better option?"
a) A trade with 80% of chance of losing $100 and 20% chance of winning $200<br/>
b) A trade with 100% of chance of losing $30
The answer comes to two factors:
???+ Info "Answer"
The expected value of *a)* is smaller than the expected value of *b)*.<br/>
Hence, *b*) represents a smaller loss in the long run.<br/>
However, the answer is: *it depends*
- Win Rate
- Risk Reward Ratio
Another way to look at it is to ask a similar question:
### Win Rate
!!! Question "Which trade is a better option?"
a) A trade with 80% of chance of winning 100 and 20% chance of losing $200<br/>
b) A trade with 100% of chance of winning $30
Win Rate (*W*) is is the mean over some amount of trades (*N*) what is the percentage of winning trades to total number of trades (note that we don't consider how much you gained but only if you won or not).
Edge positioning tries to answer the hard questions about risk/reward and position size automatically, seeking to minimizes the chances of losing of a given strategy.
```
W = (Number of winning trades) / (Total number of trades) = (Number of winning trades) / N
```
### Trading, winning and losing
Complementary Loss Rate (*L*) is defined as
Let's call $o$ the return of a single transaction $o$ where $o \in \mathbb{R}$. The collection $O = \{o_1, o_2, ..., o_N\}$ is the set of all returns of transactions made during a trading session. We say that $N$ is the cardinality of $O$, or, in lay terms, it is the number of transactions made in a trading session.
```
L = (Number of losing trades) / (Total number of trades) = (Number of losing trades) / N
```
!!! Example
In a session where a strategy made three transactions we can say that $O = \{3.5, -1, 15\}$. That means that $N = 3$ and $o_1 = 3.5$, $o_2 = -1$, $o_3 = 15$.
or, which is the same, as
A winning trade is a trade where a strategy *made* money. Making money means that the strategy closed the position in a value that returned a profit, after all deducted fees. Formally, a winning trade will have a return $o_i > 0$. Similarly, a losing trade will have a return $o_j \leq 0$. With that, we can discover the set of all winning trades, $T_{win}$, as follows:
```
L = 1 – W
```
$$ T_{win} = \{ o \in O | o > 0 \} $$
Similarly, we can discover the set of losing trades $T_{lose}$ as follows:
$$ T_{lose} = \{o \in O | o \leq 0\} $$
!!! Example
In a section where a strategy made three transactions $O = \{3.5, -1, 15, 0\}$:<br>
$T_{win} = \{3.5, 15\}$<br>
$T_{lose} = \{-1, 0\}$<br>
### Win Rate and Lose Rate
The win rate $W$ is the proportion of winning trades with respect to all the trades made by a strategy. We use the following function to compute the win rate:
$$W = \frac{|T_{win}|}{N}$$
Where $W$ is the win rate, $N$ is the number of trades and, $T_{win}$ is the set of all trades where the strategy made money.
Similarly, we can compute the rate of losing trades:
$$
L = \frac{|T_{lose}|}{N}
$$
Where $L$ is the lose rate, $N$ is the amount of trades made and, $T_{lose}$ is the set of all trades where the strategy lost money. Note that the above formula is the same as calculating $L = 1 – W$ or $W = 1 – L$
### Risk Reward Ratio
Risk Reward Ratio (*R*) is a formula used to measure the expected gains of a given investment against the risk of loss. It is basically what you potentially win divided by what you potentially lose:
Risk Reward Ratio ($R$) is a formula used to measure the expected gains of a given investment against the risk of loss. It is basically what you potentially win divided by what you potentially lose. Formally:
```
R = Profit / Loss
```
$$ R = \frac{\text{potential_profit}}{\text{potential_loss}} $$
Over time, on many trades, you can calculate your risk reward by dividing your average profit on winning trades by your average loss on losing trades:
???+ Example "Worked example of $R$ calculation"
Let's say that you think that the price of *stonecoin* today is $10.0. You believe that, because they will start mining stonecoin, it will go up to $15.0 tomorrow. There is the risk that the stone is too hard, and the GPUs can't mine it, so the price might go to $0 tomorrow. You are planning to invest $100.<br>
Your potential profit is calculated as:<br>
$\begin{aligned}
\text{potential_profit} &= (\text{potential_price} - \text{cost_per_unit}) * \frac{\text{investment}}{\text{cost_per_unit}} \\
&= (15 - 10) * \frac{100}{15}\\
&= 33.33
\end{aligned}$<br>
Since the price might go to $0, the $100 dolars invested could turn into 0. We can compute the Risk Reward Ratio as follows:<br>
$\begin{aligned}
R &= \frac{\text{potential_profit}}{\text{potential_loss}}\\
&= \frac{33.33}{100}\\
&= 0.333...
\end{aligned}$<br>
What it effectivelly means is that the strategy have the potential to make $0.33 for each $1 invested.
```
Average profit = (Sum of profits) / (Number of winning trades)
On a long horizon, that is, on many trades, we can calculate the risk reward by dividing the strategy' average profit on winning trades by the strategy' average loss on losing trades. We can calculate the average profit, $\mu_{win}$, as follows:
Average loss = (Sum of losses) / (Number of losing trades)
$$ \text{average_profit} = \mu_{win} = \frac{\text{sum_of_profits}}{\text{count_winning_trades}} = \frac{\sum^{o \in T_{win}} o}{|T_{win}|} $$
Similarly, we can calculate the average loss, $\mu_{lose}$, as follows:
$$ \text{average_loss} = \mu_{lose} = \frac{\text{sum_of_losses}}{\text{count_losing_trades}} = \frac{\sum^{o \in T_{lose}} o}{|T_{lose}|} $$
Finally, we can calculate the Risk Reward ratio, $R$, as follows:
$$ R = \frac{\text{average_profit}}{\text{average_loss}} = \frac{\mu_{win}}{\mu_{lose}}\\ $$
???+ Example "Worked example of $R$ calculation using mean profit/loss"
Let's say the strategy that we are using makes an average win $\mu_{win} = 2.06$ and an average loss $\mu_{loss} = 4.11$.<br>
We calculate the risk reward ratio as follows:<br>
$R = \frac{\mu_{win}}{\mu_{loss}} = \frac{2.06}{4.11} = 0.5012...$
R = (Average profit) / (Average loss)
```
### Expectancy
At this point we can combine *W* and *R* to create an expectancy ratio. This is a simple process of multiplying the risk reward ratio by the percentage of winning trades and subtracting the percentage of losing trades, which is calculated as follows:
By combining the Win Rate $W$ and and the Risk Reward ratio $R$ to create an expectancy ratio $E$. A expectance ratio is the expected return of the investment made in a trade. We can compute the value of $E$ as follows:
```
Expectancy Ratio = (Risk Reward Ratio X Win Rate) – Loss Rate = (R X W) – L
```
$$E = R * W - L$$
So lets say your Win rate is 28% and your Risk Reward Ratio is 5:
!!! Example "Calculating $E$"
Let's say that a strategy has a win rate $W = 0.28$ and a risk reward ratio $R = 5$. What this means is that the strategy is expected to make 5 times the investment around on 28% of the trades it makes. Working out the example:<br>
$E = R * W - L = 5 * 0.28 - 0.72 = 0.68$
<br>
```
Expectancy = (5 X 0.28) – 0.72 = 0.68
```
The expectancy worked out in the example above means that, on average, this strategy' trades will return 1.68 times the size of its losses. Said another way, the strategy makes $1.68 for every $1 it loses, on average.
Superficially, this means that on average you expect this strategy’s trades to return 1.68 times the size of your loses. Said another way, you can expect to win $1.68 for every $1 you lose. This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
It is important to remember that any system with an expectancy greater than 0 is profitable using past data. The key is finding one that will be profitable in the future.
You can also use this value to evaluate the effectiveness of modifications to this system.
**NOTICE:** It's important to keep in mind that Edge is testing your expectancy using historical data, there's no guarantee that you will have a similar edge in the future. It's still vital to do this testing in order to build confidence in your methodology, but be wary of "curve-fitting" your approach to the historical data as things are unlikely to play out the exact same way for future trades.
!!! Note
It's important to keep in mind that Edge is testing your expectancy using historical data, there's no guarantee that you will have a similar edge in the future. It's still vital to do this testing in order to build confidence in your methodology but be wary of "curve-fitting" your approach to the historical data as things are unlikely to play out the exact same way for future trades.
## How does it work?
@ -99,13 +149,13 @@ Edge combines dynamic stoploss, dynamic positions, and whitelist generation into
| XZC/ETH | -0.03 | 0.52 |1.359670 | 0.228 |
| XZC/ETH | -0.04 | 0.51 |1.234539 | 0.117 |
The goal here is to find the best stoploss for the strategy in order to have the maximum expectancy. In the above example stoploss at 3% leads to the maximum expectancy according to historical data.
The goal here is to find the best stoploss for the strategy in order to have the maximum expectancy. In the above example stoploss at $3%$ leads to the maximum expectancy according to historical data.
Edge module then forces stoploss value it evaluated to your strategy dynamically.
### Position size
Edge also dictates the stake amount for each trade to the bot according to the following factors:
Edge dictates the amount at stake for each trade to the bot according to the following factors:
- Allowed capital at risk
- Stoploss
@ -116,9 +166,9 @@ Allowed capital at risk is calculated as follows:
Allowed capital at risk = (Capital available_percentage) X (Allowed risk per trade)
```
Stoploss is calculated as described above against historical data.
Stoploss is calculated as described above with respect to historical data.
Your position size then will be:
The position size is calculated as follows:
```
Position size = (Allowed capital at risk) / Stoploss
@ -126,19 +176,23 @@ Position size = (Allowed capital at risk) / Stoploss
Example:
Let's say the stake currency is ETH and you have 10 ETH on the exchange, your capital available percentage is 50% and you would allow 1% of risk for each trade. thus your available capital for trading is **10 x 0.5 = 5 ETH** and allowed capital at risk would be **5 x 0.01 = 0.05 ETH**.
Let's say the stake currency is **ETH** and there is $10$ **ETH** on the wallet. The capital available percentage is $50%$ and the allowed risk per trade is $1\%$. Thus, the available capital for trading is $10 * 0.5 = 5$ **ETH** and the allowed capital at risk would be $5 * 0.01 = 0.05$ **ETH**.
Let's assume Edge has calculated that for **XLM/ETH** market your stoploss should be at 2%. So your position size will be **0.05 / 0.02 = 2.5 ETH**.
- **Trade 1:** The strategy detects a new buy signal in the **XLM/ETH** market. `Edge Positioning` calculates a stoploss of $2\%$ and a position of $0.05 / 0.02 = 2.5$ **ETH**. The bot takes a position of $2.5$ **ETH** in the **XLM/ETH** market.
Bot takes a position of 2.5 ETH on XLM/ETH (call it trade 1). Up next, you receive another buy signal while trade 1 is still open. This time on **BTC/ETH** market. Edge calculated stoploss for this market at 4%. So your position size would be 0.05 / 0.04 = 1.25 ETH (call it trade 2).
- **Trade 2:** The strategy detects a buy signal on the **BTC/ETH** market while **Trade 1** is still open. `Edge Positioning` calculates the stoploss of $4\%$ on this market. Thus, **Trade 2** position size is $0.05 / 0.04 = 1.25$ **ETH**.
Note that available capital for trading didn’t change for trade 2 even if you had already trade 1. The available capital doesn’t mean the free amount on your wallet.
!!! Tip "Available Capital $\neq$ Available in wallet"
The available capital for trading didn't change in **Trade 2** even with **Trade 1** still open. The available capital **is not** the free amount in the wallet.
Now you have two trades open. The bot receives yet another buy signal for another market: **ADA/ETH**. This time the stoploss is calculated at 1%. So your position size is **0.05 / 0.01 = 5 ETH**. But there are already 3.75 ETH blocked in two previous trades. So the position size for this third trade would be **5 – 3.75 = 1.25 ETH**.
- **Trade 3:** The strategy detects a buy signal in the **ADA/ETH** market. `Edge Positioning` calculates a stoploss of $1\%$ and a position of $0.05 / 0.01 = 5$ **ETH**. Since **Trade 1** has $2.5$ **ETH** blocked and **Trade 2** has $1.25$ **ETH** blocked, there is only $5 - 1.25 - 2.5 = 1.25$ **ETH** available. Hence, the position size of **Trade 3** is $1.25$ **ETH**.
Available capital doesn’t change before a position is sold. Let’s assume that trade 1 receives a sell signal and it is sold with a profit of 1 ETH. Your total capital on exchange would be 11 ETH and the available capital for trading becomes 5.5 ETH.
!!! Tip "Available Capital Updates"
The available capital does not change before a position is sold. After a trade is closed the Available Capital goes up if the trade was profitable or goes down if the trade was a loss.
So the Bot receives another buy signal for trade 4 with a stoploss at 2% then your position size would be **0.055 / 0.02 = 2.75 ETH**.
- The strategy detects a sell signal in the **XLM/ETH** market. The bot exits **Trade 1** for a profit of $1$ **ETH**. The total capital in the wallet becomes $11$ **ETH** and the available capital for trading becomes $5.5$ **ETH**.
- **Trade 4** The strategy detects a new buy signal int the **XLM/ETH** market. `Edge Positioning` calculates the stoploss of $2%$, and the position size of $0.055 / 0.02 = 2.75$ **ETH**.
## Configurations
@ -169,23 +223,23 @@ freqtrade edge
An example of its output:
| pair | stoploss | win rate | risk reward ratio | required risk reward | expectancy | total number of trades | average duration (min) |
|:----------|-----------:|-----------:|--------------------:|-----------------------:|-------------:|-------------------------:|-------------------------:|
| AGI/BTC | -0.02 | 0.64 | 5.86 | 0.56 | 3.41 | 14 | 54 |
| NXS/BTC | -0.03 | 0.64 | 2.99 | 0.57 | 1.54 | 11 | 26 |
| LEND/BTC | -0.02 | 0.82 | 2.05 | 0.22 | 1.50 | 11 | 36 |
| VIA/BTC | -0.01 | 0.55 | 3.01 | 0.83 | 1.19 | 11 | 48 |
| MTH/BTC | -0.09 | 0.56 | 2.82 | 0.80 | 1.12 | 18 | 52 |
| ARDR/BTC | -0.04 | 0.42 | 3.14 | 1.40 | 0.73 | 12 | 42 |
| BCPT/BTC | -0.01 | 0.71 | 1.34 | 0.40 | 0.67 | 14 | 30 |
| WINGS/BTC | -0.02 | 0.56 | 1.97 | 0.80 | 0.65 | 27 | 42 |
| VIBE/BTC | -0.02 | 0.83 | 0.91 | 0.20 | 0.59 | 12 | 35 |
| MCO/BTC | -0.02 | 0.79 | 0.97 | 0.27 | 0.55 | 14 | 31 |
| GNT/BTC | -0.02 | 0.50 | 2.06 | 1.00 | 0.53 | 18 | 24 |
| HOT/BTC | -0.01 | 0.17 | 7.72 | 4.81 | 0.50 | 209 | 7 |
| SNM/BTC | -0.03 | 0.71 | 1.06 | 0.42 | 0.45 | 17 | 38 |
| APPC/BTC | -0.02 | 0.44 | 2.28 | 1.27 | 0.44 | 25 | 43 |
| NEBL/BTC | -0.03 | 0.63 | 1.29 | 0.58 | 0.44 | 19 | 59 |
| **pair** | **stoploss** | **win rate** | **risk reward ratio** | **required risk reward** | **expectancy** | **total number of trades** | **average duration (min)** |
|:----------|-----------:|-----------:|--------------------:|-----------------------:|-------------:|-----------------:|---------------:|
| **AGI/BTC** | -0.02 | 0.64 | 5.86 | 0.56 | 3.41 | 14 | 54 |
| **NXS/BTC** | -0.03 | 0.64 | 2.99 | 0.57 | 1.54 | 11 | 26 |
| **LEND/BTC** | -0.02 | 0.82 | 2.05 | 0.22 | 1.50 | 11 | 36 |
| **VIA/BTC** | -0.01 | 0.55 | 3.01 | 0.83 | 1.19 | 11 | 48 |
| **MTH/BTC** | -0.09 | 0.56 | 2.82 | 0.80 | 1.12 | 18 | 52 |
| **ARDR/BTC** | -0.04 | 0.42 | 3.14 | 1.40 | 0.73 | 12 | 42 |
| **BCPT/BTC** | -0.01 | 0.71 | 1.34 | 0.40 | 0.67 | 14 | 30 |
| **WINGS/BTC** | -0.02 | 0.56 | 1.97 | 0.80 | 0.65 | 27 | 42 |
| **VIBE/BTC** | -0.02 | 0.83 | 0.91 | 0.20 | 0.59 | 12 | 35 |
| **MCO/BTC** | -0.02 | 0.79 | 0.97 | 0.27 | 0.55 | 14 | 31 |
| **GNT/BTC** | -0.02 | 0.50 | 2.06 | 1.00 | 0.53 | 18 | 24 |
| **HOT/BTC** | -0.01 | 0.17 | 7.72 | 4.81 | 0.50 | 209 | 7 |
| **SNM/BTC** | -0.03 | 0.71 | 1.06 | 0.42 | 0.45 | 17 | 38 |
| **APPC/BTC** | -0.02 | 0.44 | 2.28 | 1.27 | 0.44 | 25 | 43 |
| **NEBL/BTC** | -0.03 | 0.63 | 1.29 | 0.58 | 0.44 | 19 | 59 |
Edge produced the above table by comparing `calculate_since_number_of_days` to `minimum_expectancy` to find `min_trade_number` historical information based on the config file. The timerange Edge uses for its comparisons can be further limited by using the `--timerange` switch.
@ -218,3 +272,6 @@ The full timerange specification:
* Use tickframes since 2018/01/31: `--timerange=20180131-`
* Use tickframes since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
* Use tickframes between POSIX timestamps 1527595200 1527618600: `--timerange=1527595200-1527618600`
[^1]: Question extracted from MIT Opencourseware S096 - Mathematics with applications in Finance: https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-with-applications-in-finance-fall-2013/

33
docs/faq.md
View File

@ -1,5 +1,9 @@
# Freqtrade FAQ
## Beginner Tips & Tricks
* When you work with your strategy & hyperopt file you should use a proper code editor like vscode or Pycharm. A good code editor will provide syntax highlighting as well as line numbers, making it easy to find syntax errors (most likely, pointed out by Freqtrade during startup).
## Freqtrade common issues
### The bot does not start
@ -15,10 +19,12 @@ This could have the following reasons:
### I have waited 5 minutes, why hasn't the bot made any trades yet?!
Depending on the buy strategy, the amount of whitelisted coins, the
* Depending on the buy strategy, the amount of whitelisted coins, the
situation of the market etc, it can take up to hours to find good entry
position for a trade. Be patient!
* Or it may because of a configuration error? Best check the logs, it's usually telling you if the bot is simply not getting buy signals (only heartbeat messages), or if there is something wrong (errors / exceptions in the log).
### I have made 12 trades already, why is my total profit negative?!
I understand your disappointment but unfortunately 12 trades is just
@ -129,25 +135,27 @@ to find a great result (unless if you are very lucky), so you probably
have to run it for 10.000 or more. But it will take an eternity to
compute.
We recommend you to run it at least 10.000 epochs:
Since hyperopt uses Bayesian search, running for too many epochs may not produce greater results.
It's therefore recommended to run between 500-1000 epochs over and over until you hit at least 10.000 epocs in total (or are satisfied with the result). You can best judge by looking at the results - if the bot keeps discovering better strategies, it's best to keep on going.
```bash
freqtrade hyperopt -e 10000
freqtrade hyperopt -e 1000
```
or if you want intermediate result to see
```bash
for i in {1..100}; do freqtrade hyperopt -e 100; done
for i in {1..100}; do freqtrade hyperopt -e 1000; done
```
### Why it is so long to run hyperopt?
### Why does it take a long time to run hyperopt?
Finding a great Hyperopt results takes time.
* Discovering a great strategy with Hyperopt takes time. Study www.freqtrade.io, the Freqtrade Documentation page, join the Freqtrade [Slack community](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) - or the Freqtrade [discord community](https://discord.gg/X89cVG). While you patiently wait for the most advanced, free crypto bot in the world, to hand you a possible golden strategy specially designed just for you.
If you wonder why it takes a while to find great hyperopt results
* If you wonder why it can take from 20 minutes to days to do 1000 epocs here are some answers:
This answer was written during the under the release 0.15.1, when we had:
This answer was written during the release 0.15.1, when we had:
- 8 triggers
- 9 guards: let's say we evaluate even 10 values from each
@ -157,7 +165,14 @@ The following calculation is still very rough and not very precise
but it will give the idea. With only these triggers and guards there is
already 8\*10^9\*10 evaluations. A roughly total of 80 billion evals.
Did you run 100 000 evals? Congrats, you've done roughly 1 / 100 000 th
of the search space.
of the search space, assuming that the bot never tests the same parameters more than once.
* The time it takes to run 1000 hyperopt epocs depends on things like: The available cpu, harddisk, ram, timeframe, timerange, indicator settings, indicator count, amount of coins that hyperopt test strategies on and the resulting trade count - which can be 650 trades in a year or 10.0000 trades depending if the strategy aims for big profits by trading rarely or for many low profit trades.
Example: 4% profit 650 times vs 0,3% profit a trade 10.000 times in a year. If we assume you set the --timerange to 365 days.
Example:
`freqtrade --config config.json --strategy SampleStrategy --hyperopt SampleHyperopt -e 1000 --timerange 20190601-20200601`
## Edge module

3
docs/hyperopt.md
View File

@ -370,6 +370,9 @@ By default, hyperopt prints colorized results -- epochs with positive profit are
You can use the `--print-all` command line option if you would like to see all results in the hyperopt output, not only the best ones. When `--print-all` is used, current best results are also colorized by default -- they are printed in bold (bright) style. This can also be switched off with the `--no-color` command line option.
!!! Note "Windows and color output"
Windows does not support color-output nativly, therefore it is automatically disabled. To have color-output for hyperopt running under windows, please consider using WSL.
### Understand Hyperopt ROI results
If you are optimizing ROI (i.e. if optimization search-space contains 'all', 'default' or 'roi'), your result will look as follows and include a ROI table:

6
docs/index.md
View File

@ -37,13 +37,9 @@ Freqtrade is a crypto-currency algorithmic trading software developed in python
## Requirements
### Up to date clock
The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
### Hardware requirements
To run this bot we recommend you a cloud instance with a minimum of:
To run this bot we recommend you a linux cloud instance with a minimum of:
- 2GB RAM
- 1GB disk space

93
docs/installation.md
View File

@ -18,6 +18,9 @@ Click each one for install guide:
We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot), which is optional but recommended.
!!! Warning "Up-to-date clock"
The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
## Quick start
Freqtrade provides the Linux/MacOS Easy Installation script to install all dependencies and help you configure the bot.
@ -38,7 +41,7 @@ This can be achieved with the following commands:
```bash
git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade
git checkout master # Optional, see (1)
# git checkout master # Optional, see (1)
./setup.sh --install
```
@ -79,11 +82,9 @@ This option will hard reset your branch (only if you are on either `master` or `
DEPRECATED - use `freqtrade new-config -c config.json` instead.
** Setup your virtual environment **
Each time you open a new terminal, you must run `source .env/bin/activate`
### Activate your virtual environment
Each time you open a new terminal, you must run `source .env/bin/activate`.
------
@ -95,8 +96,7 @@ OS Specific steps are listed first, the [Common](#common) section below is neces
!!! Note
Python3.6 or higher and the corresponding pip are assumed to be available.
### Linux - Ubuntu 16.04
=== "Ubuntu 16.04"
#### Install necessary dependencies
```bash
@ -104,8 +104,7 @@ sudo apt-get update
sudo apt-get install build-essential git
```
### Raspberry Pi / Raspbian
=== "RaspberryPi/Raspbian"
The following assumes the latest [Raspbian Buster lite image](https://www.raspberrypi.org/downloads/raspbian/) from at least September 2019.
This image comes with python3.7 preinstalled, making it easy to get freqtrade up and running.
@ -175,11 +174,6 @@ Clone the git repository:
```bash
git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade
```
Optionally checkout the master branch to get the latest stable release:
```bash
git checkout master
```
@ -218,72 +212,18 @@ On Linux, as an optional post-installation task, you may wish to setup the bot t
------
## Using Conda
### Anaconda
Freqtrade can also be installed using Anaconda (or Miniconda).
!!! Note
This requires the [ta-lib](#1-install-ta-lib) C-library to be installed first. See below.
``` bash
conda env create -f environment.yml
```
!!! Note
This requires the [ta-lib](#1-install-ta-lib) C-library to be installed first.
## Windows
We recommend that Windows users use [Docker](docker.md) as this will work much easier and smoother (also more secure).
If that is not possible, try using the Windows Linux subsystem (WSL) - for which the Ubuntu instructions should work.
If that is not available on your system, feel free to try the instructions below, which led to success for some.
### Install freqtrade manually
!!! Note
Make sure to use 64bit Windows and 64bit Python to avoid problems with backtesting or hyperopt due to the memory constraints 32bit applications have under Windows.
!!! Hint
Using the [Anaconda Distribution](https://www.anaconda.com/distribution/) under Windows can greatly help with installation problems. Check out the [Conda section](#using-conda) in this document for more information.
#### Clone the git repository
```bash
git clone https://github.com/freqtrade/freqtrade.git
```
#### Install ta-lib
Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows).
As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial precompiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which needs to be downloaded and installed using `pip install TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl` (make sure to use the version matching your python version)
```cmd
>cd \path\freqtrade-develop
>python -m venv .env
>.env\Scripts\activate.bat
REM optionally install ta-lib from wheel
REM >pip install TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl
>pip install -r requirements.txt
>pip install -e .
>freqtrade
```
> Thanks [Owdr](https://github.com/Owdr) for the commands. Source: [Issue #222](https://github.com/freqtrade/freqtrade/issues/222)
#### Error during installation under Windows
``` bash
error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools
```
Unfortunately, many packages requiring compilation don't provide a pre-build wheel. It is therefore mandatory to have a C/C++ compiler installed and available for your python environment to use.
The easiest way is to download install Microsoft Visual Studio Community [here](https://visualstudio.microsoft.com/downloads/) and make sure to install "Common Tools for Visual C++" to enable building c code on Windows. Unfortunately, this is a heavy download / dependency (~4Gb) so you might want to consider WSL or [docker](docker.md) first.
---
Now you have an environment ready, the next step is
[Bot Configuration](configuration.md).
-----
## Troubleshooting
### MacOS installation error
@ -297,4 +237,9 @@ For MacOS 10.14, this can be accomplished with the below command.
open /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10.14.pkg
```
If this file is inexistant, then you're probably on a different version of MacOS, so you may need to consult the internet for specific resolution details.
If this file is inexistent, then you're probably on a different version of MacOS, so you may need to consult the internet for specific resolution details.
-----
Now you have an environment ready, the next step is
[Bot Configuration](configuration.md).

12
docs/javascripts/config.js Normal file
View File

@ -0,0 +1,12 @@
window.MathJax = {
tex: {
inlineMath: [["\\(", "\\)"]],
displayMath: [["\\[", "\\]"]],
processEscapes: true,
processEnvironments: true
},
options: {
ignoreHtmlClass: ".*|",
processHtmlClass: "arithmatex"
}
};

10
docs/plotting.md
View File

@ -224,7 +224,8 @@ Possible options for the `freqtrade plot-profit` subcommand:
```
usage: freqtrade plot-profit [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH] [-p PAIRS [PAIRS ...]]
[-d PATH] [--userdir PATH] [-s NAME]
[--strategy-path PATH] [-p PAIRS [PAIRS ...]]
[--timerange TIMERANGE] [--export EXPORT]
[--export-filename PATH] [--db-url PATH]
[--trade-source {DB,file}] [-i TIMEFRAME]
@ -270,6 +271,11 @@ Common arguments:
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
Strategy arguments:
-s NAME, --strategy NAME
Specify strategy class name which will be used by the
bot.
--strategy-path PATH Specify additional strategy lookup path.
```
The `-p/--pairs` argument, can be used to limit the pairs that are considered for this calculation.
@ -279,7 +285,7 @@ Examples:
Use custom backtest-export file
``` bash
freqtrade plot-profit -p LTC/BTC --export-filename user_data/backtest_results/backtest-result-Strategy005.json
freqtrade plot-profit -p LTC/BTC --export-filename user_data/backtest_results/backtest-result.json
```
Use custom database

2
docs/requirements-docs.txt
View File

@ -1,2 +1,2 @@
mkdocs-material==5.5.3
mkdocs-material==5.5.12
mdx_truly_sane_lists==1.2

74
docs/rest-api.md
View File

@ -116,6 +116,7 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
| `trades` | List last trades.
| `delete_trade <trade_id>` | Remove trade from the database. Tries to close open orders. Requires manual handling of this trade on the exchange.
| `show_config` | Shows part of the current configuration with relevant settings to operation
| `logs` | Shows last log messages
| `status` | Lists all open trades
| `count` | Displays number of trades used and available
| `profit` | Display a summary of your profit/loss from close trades and some stats about your performance
@ -138,78 +139,83 @@ python3 scripts/rest_client.py help
``` output
Possible commands:
balance
Get the account balance
:returns: json object
Get the account balance.
blacklist
Show the current blacklist
Show the current blacklist.
:param add: List of coins to add (example: "BNB/BTC")
:returns: json object
count
Returns the amount of open trades
:returns: json object
Return the amount of open trades.
daily
Returns the amount of open trades
:returns: json object
Return the amount of open trades.
delete_trade
Delete trade from the database.
Tries to close open orders. Requires manual handling of this asset on the exchange.
:param trade_id: Deletes the trade with this ID from the database.
edge
Returns information about edge
:returns: json object
Return information about edge.
forcebuy
Buy an asset
Buy an asset.
:param pair: Pair to buy (ETH/BTC)
:param price: Optional - price to buy
:returns: json object of the trade
forcesell
Force-sell a trade
Force-sell a trade.
:param tradeid: Id of the trade (can be received via status command)
:returns: json object
logs
Show latest logs.
:param limit: Limits log messages to the last <limit> logs. No limit to get all the trades.
performance
Returns the performance of the different coins
:returns: json object
Return the performance of the different coins.
profit
Returns the profit summary
:returns: json object
Return the profit summary.
reload_config
Reload configuration
:returns: json object
Reload configuration.
show_config
Returns part of the configuration, relevant for trading operations.
:return: json object containing the version
start
Start the bot if it's in stopped state.
:returns: json object
Start the bot if it's in the stopped state.
status
Get the status of open trades
:returns: json object
Get the status of open trades.
stop
Stop the bot. Use start to restart
:returns: json object
Stop the bot. Use `start` to restart.
stopbuy
Stop buying (but handle sells gracefully).
use reload_config to reset
:returns: json object
Stop buying (but handle sells gracefully). Use `reload_config` to reset.
trades
Return trades history.
:param limit: Limits trades to the X last trades. No limit to get all the trades.
version
Returns the version of the bot
:returns: json object containing the version
Return the version of the bot.
whitelist
Show the current whitelist
:returns: json object
Show the current whitelist.
```
## Advanced API usage using JWT tokens

170
docs/sandbox-testing.md
View File

@ -1,104 +1,59 @@
# Sandbox API testing
Where an exchange provides a sandbox for risk-free integration, or end-to-end, testing CCXT provides access to these.
Some exchanges provide sandboxes or testbeds for risk-free testing, while running the bot against a real exchange.
With some configuration, freqtrade (in combination with ccxt) provides access to these.
This document is a *light overview of configuring Freqtrade and GDAX sandbox.
This can be useful to developers and trader alike as Freqtrade is quite customisable.
This document is an overview to configure Freqtrade to be used with sandboxes.
This can be useful to developers and trader alike.
When testing your API connectivity, make sure to use the following URLs.
***Website**
https://public.sandbox.gdax.com
***REST API**
https://api-public.sandbox.gdax.com
## Exchanges known to have a sandbox / testnet
* [binance](https://testnet.binance.vision/)
* [coinbasepro](https://public.sandbox.pro.coinbase.com)
* [gemini](https://exchange.sandbox.gemini.com/)
* [huobipro](https://www.testnet.huobi.pro/)
* [kucoin](https://sandbox.kucoin.com/)
* [phemex](https://testnet.phemex.com/)
!!! Note
We did not test correct functioning of all of the above testnets. Please report your experiences with each sandbox.
---
# Configure a Sandbox account on Gdax
## Configure a Sandbox account
Aim of this document section
When testing your API connectivity, make sure to use the appropriate sandbox / testnet URL.
- An sanbox account
- create 2FA (needed to create an API)
- Add test 50BTC to account
- Create :
- - API-KEY
- - API-Secret
- - API Password
In general, you should follow these steps to enable an exchange's sandbox:
## Acccount
* Figure out if an exchange has a sandbox (most likely by using google or the exchange's support documents)
* Create a sandbox account (often the sandbox-account requires separate registration)
* [Add some test assets to account](#add-test-funds)
* Create API keys
This link will redirect to the sandbox main page to login / create account dialogues:
https://public.sandbox.pro.coinbase.com/orders/
### Add test funds
After registration and Email confimation you wil be redirected into your sanbox account. It is easy to verify you're in sandbox by checking the URL bar.
> https://public.sandbox.pro.coinbase.com/
Usually, sandbox exchanges allow depositing funds directly via web-interface.
You should make sure to have a realistic amount of funds available to your test-account, so results are representable of your real account funds.
## Enable 2Fa (a prerequisite to creating sandbox API Keys)
!!! Warning
Test exchanges will **NEVER** require your real credit card or banking details!
From within sand box site select your profile, top right.
>Or as a direct link: https://public.sandbox.pro.coinbase.com/profile
## Configure freqtrade to use a exchange's sandbox
From the menu panel to the left of the screen select
> Security: "*View or Update*"
In the new site select "enable authenticator" as typical google Authenticator.
- open Google Authenticator on your phone
- scan barcode
- enter your generated 2fa
## Enable API Access
From within sandbox select profile>api>create api-keys
>or as a direct link: https://public.sandbox.pro.coinbase.com/profile/api
Click on "create one" and ensure **view** and **trade** are "checked" and sumbit your 2FA
- **Copy and paste the Passphase** into a notepade this will be needed later
- **Copy and paste the API Secret** popup into a notepad this will needed later
- **Copy and paste the API Key** into a notepad this will needed later
## Add 50 BTC test funds
To add funds, use the web interface deposit and withdraw buttons.
To begin select 'Wallets' from the top menu.
> Or as a direct link: https://public.sandbox.pro.coinbase.com/wallets
- Deposits (bottom left of screen)
- - Deposit Funds Bitcoin
- - - Coinbase BTC Wallet
- - - - Max (50 BTC)
- - - - - Deposit
*This process may be repeated for other currencies, ETH as example*
---
# Configure Freqtrade to use Gax Sandbox
The aim of this document section
- Enable sandbox URLs in Freqtrade
- Configure API
- - secret
- - key
- - passphrase
## Sandbox URLs
### Sandbox URLs
Freqtrade makes use of CCXT which in turn provides a list of URLs to Freqtrade.
These include `['test']` and `['api']`.
- `[Test]` if available will point to an Exchanges sandbox.
- `[Api]` normally used, and resolves to live API target on the exchange
* `[Test]` if available will point to an Exchanges sandbox.
* `[Api]` normally used, and resolves to live API target on the exchange.
To make use of sandbox / test add "sandbox": true, to your config.json
```json
"exchange": {
"name": "gdax",
"name": "coinbasepro",
"sandbox": true,
"key": "5wowfxemogxeowo;heiohgmd",
"secret": "/ZMH1P62rCVmwefewrgcewX8nh4gob+lywxfwfxwwfxwfNsH1ySgvWCUR/w==",
@ -106,36 +61,57 @@ To make use of sandbox / test add "sandbox": true, to your config.json
"outdated_offset": 5
"pair_whitelist": [
"BTC/USD"
]
},
"datadir": "user_data/data/coinbasepro_sandbox"
```
Also insert your
Also the following information:
- api-key (noted earlier)
- api-secret (noted earlier)
- password (the passphrase - noted earlier)
* api-key (created for the sandbox webpage)
* api-secret (noted earlier)
* password (the passphrase - noted earlier)
!!! Tip "Different data directory"
We also recommend to set `datadir` to something identifying downloaded data as sandbox data, to avoid having sandbox data mixed with data from the real exchange.
This can be done by adding the `"datadir"` key to the configuration.
Now, whenever you use this configuration, your data directory will be set to this directory.
---
## You should now be ready to test your sandbox
Ensure Freqtrade logs show the sandbox URL, and trades made are shown in sandbox.
** Typically the BTC/USD has the most activity in sandbox to test against.
Ensure Freqtrade logs show the sandbox URL, and trades made are shown in sandbox. Also make sure to select a pair which shows at least some decent value (which very often is BTC/<somestablecoin>).
## GDAX - Old Candles problem
## Common problems with sandbox exchanges
It is my experience that GDAX sandbox candles may be 20+- minutes out of date. This can cause trades to fail as one of Freqtrades safety checks.
Sandbox exchange instances often have very low volume, which can cause some problems which usually are not seen on a real exchange instance.
To disable this check, add / change the `"outdated_offset"` parameter in the exchange section of your configuration to adjust for this delay.
Example based on the above configuration:
### Old Candles problem
```json
"exchange": {
"name": "gdax",
"sandbox": true,
"key": "5wowfxemogxeowo;heiohgmd",
"secret": "/ZMH1P62rCVmwefewrgcewX8nh4gob+lywxfwfxwwfxwfNsH1ySgvWCUR/w==",
"password": "1bkjfkhfhfu6sr",
"outdated_offset": 30
"pair_whitelist": [
"BTC/USD"
Since Sandboxes often have low volume, candles can be quite old and show no volume.
To disable the error "Outdated history for pair ...", best increase the parameter `"outdated_offset"` to a number that seems realistic for the sandbox you're using.
### Unfilled orders
Sandboxes often have very low volumes - which means that many trades can go unfilled, or can go unfilled for a very long time.
To mitigate this, you can try to match the first order on the opposite orderbook side using the following configuration:
``` jsonc
"order_types": {
"buy": "limit",
"sell": "limit"
// ...
},
"bid_strategy": {
"price_side": "ask",
// ...
},
"ask_strategy":{
"price_side": "bid",
// ...
},
```
The configuration is similar to the suggested configuration for market orders - however by using limit-orders you can avoid moving the price too much, and you can set the worst price you might get.

6
docs/sql_cheatsheet.md
View File

@ -46,7 +46,7 @@ sqlite3
### Trade table structure
```sql
CREATE TABLE trades
CREATE TABLE trades(
id INTEGER NOT NULL,
exchange VARCHAR NOT NULL,
pair VARCHAR NOT NULL,
@ -110,7 +110,7 @@ SET is_open=0,
close_date=<close_date>,
close_rate=<close_rate>,
close_profit = close_rate / open_rate - 1,
close_profit_abs = (amount * <close_rate> * (1 - fee_close) - (amount * (open_rate * 1 - fee_open))),
close_profit_abs = (amount * <close_rate> * (1 - fee_close) - (amount * (open_rate * (1 - fee_open)))),
sell_reason=<sell_reason>
WHERE id=<trade_ID_to_update>;
```
@ -123,7 +123,7 @@ SET is_open=0,
close_date='2020-06-20 03:08:45.103418',
close_rate=0.19638016,
close_profit=0.0496,
close_profit_abs = (amount * 0.19638016 * (1 - fee_close) - (amount * open_rate * (1 - fee_open))),
close_profit_abs = (amount * 0.19638016 * (1 - fee_close) - (amount * (open_rate * (1 - fee_open)))),
sell_reason='force_sell'
WHERE id=31;
```

149
docs/stoploss.md
View File

@ -6,7 +6,63 @@ For example, value `-0.10` will cause immediate sell if the profit dips below -1
Most of the strategy files already include the optimal `stoploss` value.
!!! Info
All stoploss properties mentioned in this file can be set in the Strategy, or in the configuration. Configuration values will override the strategy values.
All stoploss properties mentioned in this file can be set in the Strategy, or in the configuration.
<ins>Configuration values will override the strategy values.</ins>
## Stop Loss On-Exchange/Freqtrade
Those stoploss modes can be *on exchange* or *off exchange*.
These modes can be configured with these values:
``` python
'emergencysell': 'market',
'stoploss_on_exchange': False
'stoploss_on_exchange_interval': 60,
'stoploss_on_exchange_limit_ratio': 0.99
```
!!! Note
Stoploss on exchange is only supported for Binance (stop-loss-limit), Kraken (stop-loss-market) and FTX (stop limit and stop-market) as of now.
<ins>Do not set too low stoploss value if using stop loss on exchange!</ins>
If set to low/tight then you have greater risk of missing fill on the order and stoploss will not work
### stoploss_on_exchange and stoploss_on_exchange_limit_ratio
Enable or Disable stop loss on exchange.
If the stoploss is *on exchange* it means a stoploss limit order is placed on the exchange immediately after buy order happens successfully. This will protect you against sudden crashes in market as the order will be in the queue immediately and if market goes down then the order has more chance of being fulfilled.
If `stoploss_on_exchange` uses limit orders, the exchange needs 2 prices, the stoploss_price and the Limit price.
`stoploss` defines the stop-price where the limit order is placed - and limit should be slightly below this.
If an exchange supports both limit and market stoploss orders, then the value of `stoploss` will be used to determine the stoploss type.
Calculation example: we bought the asset at 100$.
Stop-price is 95$, then limit would be `95 * 0.99 = 94.05$` - so the limit order fill can happen between 95$ and 94.05$.
For example, assuming the stoploss is on exchange, and trailing stoploss is enabled, and the market is going up, then the bot automatically cancels the previous stoploss order and puts a new one with a stop value higher than the previous stoploss order.
### stoploss_on_exchange_interval
In case of stoploss on exchange there is another parameter called `stoploss_on_exchange_interval`. This configures the interval in seconds at which the bot will check the stoploss and update it if necessary.
The bot cannot do these every 5 seconds (at each iteration), otherwise it would get banned by the exchange.
So this parameter will tell the bot how often it should update the stoploss order. The default value is 60 (1 minute).
This same logic will reapply a stoploss order on the exchange should you cancel it accidentally.
### emergencysell
`emergencysell` is an optional value, which defaults to `market` and is used when creating stop loss on exchange orders fails.
The below is the default which is used if not changed in strategy or configuration file.
Example from strategy file:
``` python
order_types = {
'buy': 'limit',
'sell': 'limit',
'emergencysell': 'market',
'stoploss': 'market',
'stoploss_on_exchange': True,
'stoploss_on_exchange_interval': 60,
'stoploss_on_exchange_limit_ratio': 0.99
}
```
## Stop Loss Types
@ -17,28 +73,28 @@ At this stage the bot contains the following stoploss support modes:
3. Trailing stop loss, custom positive loss.
4. Trailing stop loss only once the trade has reached a certain offset.
Those stoploss modes can be *on exchange* or *off exchange*. If the stoploss is *on exchange* it means a stoploss limit order is placed on the exchange immediately after buy order happens successfully. This will protect you against sudden crashes in market as the order will be in the queue immediately and if market goes down then the order has more chance of being fulfilled.
In case of stoploss on exchange there is another parameter called `stoploss_on_exchange_interval`. This configures the interval in seconds at which the bot will check the stoploss and update it if necessary.
For example, assuming the stoploss is on exchange, and trailing stoploss is enabled, and the market is going up, then the bot automatically cancels the previous stoploss order and puts a new one with a stop value higher than the previous stoploss order.
The bot cannot do this every 5 seconds (at each iteration), otherwise it would get banned by the exchange.
So this parameter will tell the bot how often it should update the stoploss order. The default value is 60 (1 minute).
This same logic will reapply a stoploss order on the exchange should you cancel it accidentally.
!!! Note
Stoploss on exchange is only supported for Binance (stop-loss-limit), Kraken (stop-loss-market) and FTX (stop limit and stop-market) as of now.
## Static Stop Loss
### Static Stop Loss
This is very simple, you define a stop loss of x (as a ratio of price, i.e. x * 100% of price). This will try to sell the asset once the loss exceeds the defined loss.
## Trailing Stop Loss
Example of stop loss:
``` python
stoploss = -0.10
```
For example, simplified math:
* the bot buys an asset at a price of 100$
* the stop loss is defined at -10%
* the stop loss would get triggered once the asset drops below 90$
### Trailing Stop Loss
The initial value for this is `stoploss`, just as you would define your static Stop loss.
To enable trailing stoploss:
``` python
stoploss = -0.10
trailing_stop = True
```
@ -47,35 +103,43 @@ This will now activate an algorithm, which automatically moves the stop loss up
For example, simplified math:
* the bot buys an asset at a price of 100$
* the stop loss is defined at 2%
* the stop loss would get triggered once the asset dropps below 98$
* the stop loss is defined at -10%
* the stop loss would get triggered once the asset drops below 90$
* assuming the asset now increases to 102$
* the stop loss will now be 2% of 102$ or 99.96$
* now the asset drops in value to 101$, the stop loss will still be 99.96$ and would trigger at 99.96$.
* the stop loss will now be -10% of 102$ = 91.8$
* now the asset drops in value to 101$, the stop loss will still be 91.8$ and would trigger at 91.8$.
In summary: The stoploss will be adjusted to be always be 2% of the highest observed price.
In summary: The stoploss will be adjusted to be always be -10% of the highest observed price.
### Custom positive stoploss
### Trailing stop loss, custom positive loss
It is also possible to have a default stop loss, when you are in the red with your buy, but once your profit surpasses a certain percentage, the system will utilize a new stop loss, which can have a different value.
For example your default stop loss is 5%, but once you have 1.1% profit, it will be changed to be only a 1% stop loss, which trails the green candles until it goes below them.
It is also possible to have a default stop loss, when you are in the red with your buy (buy - fee), but once you hit positive result the system will utilize a new stop loss, which can have a different value.
For example, your default stop loss is -10%, but once you have more than 0% profit (example 0.1%) a different trailing stoploss will be used.
Both values require `trailing_stop` to be set to true.
!!! Note
If you want the stoploss to only be changed when you break even of making a profit (what most users want) please refer to next section with [offset enabled](#Trailing-stop-loss-only-once-the-trade-has-reached-a-certain-offset).
Both values require `trailing_stop` to be set to true and `trailing_stop_positive` with a value.
``` python
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.011
stoploss = -0.10
trailing_stop = True
trailing_stop_positive = 0.02
```
The 0.01 would translate to a 1% stop loss, once you hit 1.1% profit.
For example, simplified math:
* the bot buys an asset at a price of 100$
* the stop loss is defined at -10%
* the stop loss would get triggered once the asset drops below 90$
* assuming the asset now increases to 102$
* the stop loss will now be -2% of 102$ = 99.96$ (99.96$ stop loss will be locked in and will follow asset price increasements with -2%)
* now the asset drops in value to 101$, the stop loss will still be 99.96$ and would trigger at 99.96$
The 0.02 would translate to a -2% stop loss.
Before this, `stoploss` is used for the trailing stoploss.
Read the [next section](#trailing-only-once-offset-is-reached) to keep stoploss at 5% of the entry point.
!!! Tip
Make sure to have this value (`trailing_stop_positive_offset`) lower than minimal ROI, otherwise minimal ROI will apply first and sell the trade.
### Trailing only once offset is reached
### Trailing stop loss only once the trade has reached a certain offset
It is also possible to use a static stoploss until the offset is reached, and then trail the trade to take profits once the market turns.
@ -87,17 +151,28 @@ This option can be used with or without `trailing_stop_positive`, but uses `trai
trailing_only_offset_is_reached = True
```
Simplified example:
Configuration (offset is buyprice + 3%):
``` python
stoploss = 0.05
stoploss = -0.10
trailing_stop = True
trailing_stop_positive = 0.02
trailing_stop_positive_offset = 0.03
trailing_only_offset_is_reached = True
```
For example, simplified math:
* the bot buys an asset at a price of 100$
* the stop loss is defined at 5%
* the stop loss will remain at 95% until profit reaches +3%
* the stop loss is defined at -10%
* the stop loss would get triggered once the asset drops below 90$
* stoploss will remain at 90$ unless asset increases to or above our configured offset
* assuming the asset now increases to 103$ (where we have the offset configured)
* the stop loss will now be -2% of 103$ = 100.94$
* now the asset drops in value to 101$, the stop loss will still be 100.94$ and would trigger at 100.94$
!!! Tip
Make sure to have this value (`trailing_stop_positive_offset`) lower than minimal ROI, otherwise minimal ROI will apply first and sell the trade.
## Changing stoploss on open trades

21
docs/strategy-advanced.md
View File

@ -199,3 +199,24 @@ class Awesomestrategy(IStrategy):
return True
```
## Derived strategies
The strategies can be derived from other strategies. This avoids duplication of your custom strategy code. You can use this technique to override small parts of your main strategy, leaving the rest untouched:
``` python
class MyAwesomeStrategy(IStrategy):
...
stoploss = 0.13
trailing_stop = False
# All other attributes and methods are here as they
# should be in any custom strategy...
...
class MyAwesomeStrategy2(MyAwesomeStrategy):
# Override something
stoploss = 0.08
trailing_stop = True
```
Both attributes and methods may be overriden, altering behavior of the original strategy in a way you need.

296
docs/strategy-customization.md
View File

@ -58,12 +58,12 @@ file as reference.**
!!! Note "Strategies and Backtesting"
To avoid problems and unexpected differences between Backtesting and dry/live modes, please be aware
that during backtesting the full time-interval is passed to the `populate_*()` methods at once.
that during backtesting the full time range is passed to the `populate_*()` methods at once.
It is therefore best to use vectorized operations (across the whole dataframe, not loops) and
avoid index referencing (`df.iloc[-1]`), but instead use `df.shift()` to get to the previous candle.
!!! Warning "Warning: Using future data"
Since backtesting passes the full time interval to the `populate_*()` methods, the strategy author
Since backtesting passes the full time range to the `populate_*()` methods, the strategy author
needs to take care to avoid having the strategy utilize data from the future.
Some common patterns for this are listed in the [Common Mistakes](#common-mistakes-when-developing-strategies) section of this document.
@ -251,7 +251,7 @@ minimal_roi = {
While technically not completely disabled, this would sell once the trade reaches 10000% Profit.
To use times based on candle duration (timeframe), the following snippet can be handy.
This will allow you to change the ticket_interval for the strategy, and ROI times will still be set as candles (e.g. after 3 candles ...)
This will allow you to change the timeframe for the strategy, and ROI times will still be set as candles (e.g. after 3 candles ...)
``` python
from freqtrade.exchange import timeframe_to_minutes
@ -285,7 +285,7 @@ If your exchange supports it, it's recommended to also set `"stoploss_on_exchang
For more information on order_types please look [here](configuration.md#understand-order_types).
### Timeframe (ticker interval)
### Timeframe (formerly ticker interval)
This is the set of candles the bot should download and use for the analysis.
Common values are `"1m"`, `"5m"`, `"15m"`, `"1h"`, however all values supported by your exchange should work.
@ -328,15 +328,15 @@ class Awesomestrategy(IStrategy):
***
### Additional data (informative_pairs)
## Additional data (informative_pairs)
#### Get data for non-tradeable pairs
### Get data for non-tradeable pairs
Data for additional, informative pairs (reference pairs) can be beneficial for some strategies.
Ohlcv data for these pairs will be downloaded as part of the regular whitelist refresh process and is available via `DataProvider` just as other pairs (see below).
OHLCV data for these pairs will be downloaded as part of the regular whitelist refresh process and is available via `DataProvider` just as other pairs (see below).
These parts will **not** be traded unless they are also specified in the pair whitelist, or have been selected by Dynamic Whitelisting.
The pairs need to be specified as tuples in the format `("pair", "interval")`, with pair as the first and time interval as the second argument.
The pairs need to be specified as tuples in the format `("pair", "timeframe")`, with pair as the first and timeframe as the second argument.
Sample:
@ -347,15 +347,17 @@ def informative_pairs(self):
]
```
A full sample can be found [in the DataProvider section](#complete-data-provider-sample).
!!! Warning
As these pairs will be refreshed as part of the regular whitelist refresh, it's best to keep this list short.
All intervals and all pairs can be specified as long as they are available (and active) on the used exchange.
It is however better to use resampling to longer time-intervals when possible
All timeframes and all pairs can be specified as long as they are available (and active) on the used exchange.
It is however better to use resampling to longer timeframes whenever possible
to avoid hammering the exchange with too many requests and risk being blocked.
***
### Additional data (DataProvider)
## Additional data (DataProvider)
The strategy provides access to the `DataProvider`. This allows you to get additional data to use in your strategy.
@ -363,10 +365,14 @@ All methods return `None` in case of failure (do not raise an exception).
Please always check the mode of operation to select the correct method to get data (samples see below).
#### Possible options for DataProvider
!!! Warning "Hyperopt"
Dataprovider is available during hyperopt, however it can only be used in `populate_indicators()` within a strategy.
It is not available in `populate_buy()` and `populate_sell()` methods, nor in `populate_indicators()`, if this method located in the hyperopt file.
- [`available_pairs`](#available_pairs) - Property with tuples listing cached pairs with their intervals (pair, interval).
- [`current_whitelist()`](#current_whitelist) - Returns a current list of whitelisted pairs. Useful for accessing dynamic whitelists (ie. VolumePairlist)
### Possible options for DataProvider
- [`available_pairs`](#available_pairs) - Property with tuples listing cached pairs with their timeframe (pair, timeframe).
- [`current_whitelist()`](#current_whitelist) - Returns a current list of whitelisted pairs. Useful for accessing dynamic whitelists (i.e. VolumePairlist)
- [`get_pair_dataframe(pair, timeframe)`](#get_pair_dataframepair-timeframe) - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
- [`get_analyzed_dataframe(pair, timeframe)`](#get_analyzed_dataframepair-timeframe) - Returns the analyzed dataframe (after calling `populate_indicators()`, `populate_buy()`, `populate_sell()`) and the time of the latest analysis.
- `historic_ohlcv(pair, timeframe)` - Returns historical data stored on disk.
@ -376,9 +382,9 @@ Please always check the mode of operation to select the correct method to get da
- [`ticker(pair)`](#tickerpair) - Returns current ticker data for the pair. See [ccxt documentation](https://github.com/ccxt/ccxt/wiki/Manual#price-tickers) for more details on the Ticker data structure.
- `runmode` - Property containing the current runmode.
#### Example Usages:
### Example Usages
#### *available_pairs*
### *available_pairs*
``` python
if self.dp:
@ -386,7 +392,7 @@ if self.dp:
print(f"available {pair}, {timeframe}")
```
#### *current_whitelist()*
### *current_whitelist()*
Imagine you've developed a strategy that trades the `5m` timeframe using signals generated from a `1d` timeframe on the top 10 volume pairs by volume.
@ -401,6 +407,85 @@ Since we can't resample our data we will have to use an informative pair; and si
This is where calling `self.dp.current_whitelist()` comes in handy.
```python
def informative_pairs(self):
# get access to all pairs available in whitelist.
pairs = self.dp.current_whitelist()
# Assign tf to each pair so they can be downloaded and cached for strategy.
informative_pairs = [(pair, '1d') for pair in pairs]
return informative_pairs
```
### *get_pair_dataframe(pair, timeframe)*
``` python
# fetch live / historical candle (OHLCV) data for the first informative pair
if self.dp:
inf_pair, inf_timeframe = self.informative_pairs()[0]
informative = self.dp.get_pair_dataframe(pair=inf_pair,
timeframe=inf_timeframe)
```
!!! Warning "Warning about backtesting"
Be careful when using dataprovider in backtesting. `historic_ohlcv()` (and `get_pair_dataframe()`
for the backtesting runmode) provides the full time-range in one go,
so please be aware of it and make sure to not "look into the future" to avoid surprises when running in dry/live mode.
### *get_analyzed_dataframe(pair, timeframe)*
This method is used by freqtrade internally to determine the last signal.
It can also be used in specific callbacks to get the signal that caused the action (see [Advanced Strategy Documentation](strategy-advanced.md) for more details on available callbacks).
``` python
# fetch current dataframe
if self.dp:
dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'],
timeframe=self.timeframe)
```
!!! Note "No data available"
Returns an empty dataframe if the requested pair was not cached.
This should not happen when using whitelisted pairs.
### *orderbook(pair, maximum)*
``` python
if self.dp:
if self.dp.runmode.value in ('live', 'dry_run'):
ob = self.dp.orderbook(metadata['pair'], 1)
dataframe['best_bid'] = ob['bids'][0][0]
dataframe['best_ask'] = ob['asks'][0][0]
```
!!! Warning
The order book is not part of the historic data which means backtesting and hyperopt will not work correctly if this method is used.
### *ticker(pair)*
``` python
if self.dp:
if self.dp.runmode.value in ('live', 'dry_run'):
ticker = self.dp.ticker(metadata['pair'])
dataframe['last_price'] = ticker['last']
dataframe['volume24h'] = ticker['quoteVolume']
dataframe['vwap'] = ticker['vwap']
```
!!! Warning
Although the ticker data structure is a part of the ccxt Unified Interface, the values returned by this method can
vary for different exchanges. For instance, many exchanges do not return `vwap` values, the FTX exchange
does not always fills in the `last` field (so it can be None), etc. So you need to carefully verify the ticker
data returned from the exchange and add appropriate error handling / defaults.
!!! Warning "Warning about backtesting"
This method will always return up-to-date values - so usage during backtesting / hyperopt will lead to wrong results.
### Complete Data-provider sample
```python
from freqtrade.strategy import IStrategy, merge_informative_pair
from pandas import DataFrame
class SampleStrategy(IStrategy):
# strategy init stuff...
@ -414,27 +499,30 @@ class SampleStrategy(IStrategy):
pairs = self.dp.current_whitelist()
# Assign tf to each pair so they can be downloaded and cached for strategy.
informative_pairs = [(pair, '1d') for pair in pairs]
# Optionally Add additional "static" pairs
informative_pairs += [("ETH/USDT", "5m"),
("BTC/TUSD", "15m"),
]
return informative_pairs
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if not self.dp:
# Don't do anything if DataProvider is not available.
return dataframe
inf_tf = '1d'
# Get the informative pair
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1d')
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
# Get the 14 day rsi
informative['rsi'] = ta.RSI(informative, timeperiod=14)
# Rename columns to be unique
informative.columns = [f"{col}_{inf_tf}" for col in informative.columns]
# Assuming inf_tf = '1d' - then the columns will now be:
# date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d
# Use the helper function merge_informative_pair to safely merge the pair
# Automatically renames the columns and merges a shorter timeframe dataframe and a longer timeframe informative pair
# use ffill to have the 1d value available in every row throughout the day.
# Without this, comparisons between columns of the original and the informative pair would only work once per day.
# Full documentation of this method, see below
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
# Combine the 2 dataframes
# all indicators on the informative sample MUST be calculated before this point
dataframe = pd.merge(dataframe, informative, left_on='date', right_on=f'date_{inf_tf}', how='left')
# FFill to have the 1d value available in every row throughout the day.
# Without this, comparisons would only work once per day.
dataframe = dataframe.ffill()
# Calculate rsi of the original dataframe (5m timeframe)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
@ -455,77 +543,72 @@ class SampleStrategy(IStrategy):
```
#### *get_pair_dataframe(pair, timeframe)*
***
## Helper functions
### *merge_informative_pair()*
This method helps you merge an informative pair to a regular dataframe without lookahead bias.
It's there to help you merge the dataframe in a safe and consistent way.
Options:
- Rename the columns for you to create unique columns
- Merge the dataframe without lookahead bias
- Forward-fill (optional)
All columns of the informative dataframe will be available on the returning dataframe in a renamed fashion:
!!! Example "Column renaming"
Assuming `inf_tf = '1d'` the resulting columns will be:
``` python
# fetch live / historical candle (OHLCV) data for the first informative pair
if self.dp:
inf_pair, inf_timeframe = self.informative_pairs()[0]
informative = self.dp.get_pair_dataframe(pair=inf_pair,
timeframe=inf_timeframe)
'date', 'open', 'high', 'low', 'close', 'rsi' # from the original dataframe
'date_1d', 'open_1d', 'high_1d', 'low_1d', 'close_1d', 'rsi_1d' # from the informative dataframe
```
!!! Warning "Warning about backtesting"
Be careful when using dataprovider in backtesting. `historic_ohlcv()` (and `get_pair_dataframe()`
for the backtesting runmode) provides the full time-range in one go,
so please be aware of it and make sure to not "look into the future" to avoid surprises when running in dry/live mode).
!!! Warning "Warning in hyperopt"
This option cannot currently be used during hyperopt.
#### *get_analyzed_dataframe(pair, timeframe)*
This method is used by freqtrade internally to determine the last signal.
It can also be used in specific callbacks to get the signal that caused the action (see [Advanced Strategy Documentation](strategy-advanced.md) for more details on available callbacks).
??? Example "Column renaming - 1h"
Assuming `inf_tf = '1h'` the resulting columns will be:
``` python
# fetch current dataframe
if self.dp:
dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'],
timeframe=self.ticker_interval)
'date', 'open', 'high', 'low', 'close', 'rsi' # from the original dataframe
'date_1h', 'open_1h', 'high_1h', 'low_1h', 'close_1h', 'rsi_1h' # from the informative dataframe
```
!!! Note "No data available"
Returns an empty dataframe if the requested pair was not cached.
This should not happen when using whitelisted pairs.
!!! Warning "Warning in hyperopt"
This option cannot currently be used during hyperopt.
#### *orderbook(pair, maximum)*
??? Example "Custom implementation"
A custom implementation for this is possible, and can be done as follows:
``` python
if self.dp:
if self.dp.runmode.value in ('live', 'dry_run'):
ob = self.dp.orderbook(metadata['pair'], 1)
dataframe['best_bid'] = ob['bids'][0][0]
dataframe['best_ask'] = ob['asks'][0][0]
# Shift date by 1 candle
# This is necessary since the data is always the "open date"
# and a 15m candle starting at 12:15 should not know the close of the 1h candle from 12:00 to 13:00
minutes = timeframe_to_minutes(inf_tf)
# Only do this if the timeframes are different:
informative['date_merge'] = informative["date"] + pd.to_timedelta(minutes, 'm')
# Rename columns to be unique
informative.columns = [f"{col}_{inf_tf}" for col in informative.columns]
# Assuming inf_tf = '1d' - then the columns will now be:
# date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d
# Combine the 2 dataframes
# all indicators on the informative sample MUST be calculated before this point
dataframe = pd.merge(dataframe, informative, left_on='date', right_on=f'date_merge_{inf_tf}', how='left')
# FFill to have the 1d value available in every row throughout the day.
# Without this, comparisons would only work once per day.
dataframe = dataframe.ffill()
```
!!! Warning
The order book is not part of the historic data which means backtesting and hyperopt will not work if this
method is used.
#### *ticker(pair)*
``` python
if self.dp:
if self.dp.runmode.value in ('live', 'dry_run'):
ticker = self.dp.ticker(metadata['pair'])
dataframe['last_price'] = ticker['last']
dataframe['volume24h'] = ticker['quoteVolume']
dataframe['vwap'] = ticker['vwap']
```
!!! Warning
Although the ticker data structure is a part of the ccxt Unified Interface, the values returned by this method can
vary for different exchanges. For instance, many exchanges do not return `vwap` values, the FTX exchange
does not always fills in the `last` field (so it can be None), etc. So you need to carefully verify the ticker
data returned from the exchange and add appropriate error handling / defaults.
!!! Warning "Informative timeframe < timeframe"
Using informative timeframes smaller than the dataframe timeframe is not recommended with this method, as it will not use any of the additional information this would provide.
To use the more detailed information properly, more advanced methods should be applied (which are out of scope for freqtrade documentation, as it'll depend on the respective need).
***
### Additional data (Wallets)
## Additional data (Wallets)
The strategy provides access to the `Wallets` object. This contains the current balances on the exchange.
@ -541,7 +624,7 @@ if self.wallets:
total_eth = self.wallets.get_total('ETH')
```
#### Possible options for Wallets
### Possible options for Wallets
- `get_free(asset)` - currently available balance to trade
- `get_used(asset)` - currently tied up balance (open orders)
@ -549,7 +632,7 @@ if self.wallets:
***
### Additional data (Trades)
## Additional data (Trades)
A history of Trades can be retrieved in the strategy by querying the database.
@ -595,13 +678,13 @@ Sample return value: ETH/BTC had 5 trades, with a total profit of 1.5% (ratio of
!!! Warning
Trade history is not available during backtesting or hyperopt.
### Prevent trades from happening for a specific pair
## Prevent trades from happening for a specific pair
Freqtrade locks pairs automatically for the current candle (until that candle is over) when a pair is sold, preventing an immediate re-buy of that pair.
Locked pairs will show the message `Pair <pair> is currently locked.`.
#### Locking pairs from within the strategy
### Locking pairs from within the strategy
Sometimes it may be desired to lock a pair after certain events happen (e.g. multiple losing trades in a row).
@ -618,7 +701,7 @@ To verify if a pair is currently locked, use `self.is_pair_locked(pair)`.
!!! Warning
Locking pairs is not functioning during backtesting.
##### Pair locking example
#### Pair locking example
``` python
from freqtrade.persistence import Trade
@ -640,7 +723,7 @@ if self.config['runmode'].value in ('live', 'dry_run'):
self.lock_pair(metadata['pair'], until=datetime.now(timezone.utc) + timedelta(hours=12))
```
### Print created dataframe
## Print created dataframe
To inspect the created dataframe, you can issue a print-statement in either `populate_buy_trend()` or `populate_sell_trend()`.
You may also want to print the pair so it's clear what data is currently shown.
@ -664,36 +747,7 @@ def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
Printing more than a few rows is also possible (simply use `print(dataframe)` instead of `print(dataframe.tail())`), however not recommended, as that will be very verbose (~500 lines per pair every 5 seconds).
### Specify custom strategy location
If you want to use a strategy from a different directory you can pass `--strategy-path`
```bash
freqtrade trade --strategy AwesomeStrategy --strategy-path /some/directory
```
### Derived strategies
The strategies can be derived from other strategies. This avoids duplication of your custom strategy code. You can use this technique to override small parts of your main strategy, leaving the rest untouched:
``` python
class MyAwesomeStrategy(IStrategy):
...
stoploss = 0.13
trailing_stop = False
# All other attributes and methods are here as they
# should be in any custom strategy...
...
class MyAwesomeStrategy2(MyAwesomeStrategy):
# Override something
stoploss = 0.08
trailing_stop = True
```
Both attributes and methods may be overriden, altering behavior of the original strategy in a way you need.
### Common mistakes when developing strategies
## Common mistakes when developing strategies
Backtesting analyzes the whole time-range at once for performance reasons. Because of this, strategy authors need to make sure that strategies do not look-ahead into the future.
This is a common pain-point, which can cause huge differences between backtesting and dry/live run methods, since they all use data which is not available during dry/live runs, so these strategies will perform well during backtesting, but will fail / perform badly in real conditions.
@ -705,7 +759,7 @@ The following lists some common patterns which should be avoided to prevent frus
- don't use `dataframe['volume'].mean()`. This uses the full DataFrame for backtesting, including data from the future. Use `dataframe['volume'].rolling(<window>).mean()` instead
- don't use `.resample('1h')`. This uses the left border of the interval, so moves data from an hour to the start of the hour. Use `.resample('1h', label='right')` instead.
### Further strategy ideas
## Further strategy ideas
To get additional Ideas for strategies, head over to our [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as they are - but results will depend on the current market situation, pairs used etc. - therefore please backtest the strategy for your exchange/desired pairs first, evaluate carefully, use at your own risk.
Feel free to use any of them as inspiration for your own strategies.

40
docs/strategy_analysis_example.md
View File

@ -85,10 +85,44 @@ Analyze a trades dataframe (also used below for plotting)
```python
from freqtrade.data.btanalysis import load_backtest_data
from freqtrade.data.btanalysis import load_backtest_data, load_backtest_stats
# Load backtest results
trades = load_backtest_data(config["user_data_dir"] / "backtest_results/backtest-result.json")
# if backtest_dir points to a directory, it'll automatically load the last backtest file.
backtest_dir = config["user_data_dir"] / "backtest_results"
# backtest_dir can also point to a specific file
# backtest_dir = config["user_data_dir"] / "backtest_results/backtest-result-2020-07-01_20-04-22.json"
```
```python
# You can get the full backtest statistics by using the following command.
# This contains all information used to generate the backtest result.
stats = load_backtest_stats(backtest_dir)
strategy = 'SampleStrategy'
# All statistics are available per strategy, so if `--strategy-list` was used during backtest, this will be reflected here as well.
# Example usages:
print(stats['strategy'][strategy]['results_per_pair'])
# Get pairlist used for this backtest
print(stats['strategy'][strategy]['pairlist'])
# Get market change (average change of all pairs from start to end of the backtest period)
print(stats['strategy'][strategy]['market_change'])
# Maximum drawdown ()
print(stats['strategy'][strategy]['max_drawdown'])
# Maximum drawdown start and end
print(stats['strategy'][strategy]['drawdown_start'])
print(stats['strategy'][strategy]['drawdown_end'])
# Get strategy comparison (only relevant if multiple strategies were compared)
print(stats['strategy_comparison'])
```
```python
# Load backtested trades as dataframe
trades = load_backtest_data(backtest_dir)
# Show value-counts per pair
trades.groupby("pair")["sell_reason"].value_counts()

1
docs/telegram-usage.md
View File

@ -54,6 +54,7 @@ official commands. You can ask at any moment for help with `/help`.
| `/stopbuy` | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
| `/reload_config` | Reloads the configuration file
| `/show_config` | Shows part of the current configuration with relevant settings to operation
| `/logs [limit]` | Show last log messages.
| `/status` | Lists all open trades
| `/status table` | List all open trades in a table format. Pending buy orders are marked with an asterisk (*) Pending sell orders are marked with a double asterisk (**)
| `/trades [limit]` | List all recently closed trades in a table format.

57
docs/windows_installation.md Normal file
View File

@ -0,0 +1,57 @@
We **strongly** recommend that Windows users use [Docker](docker.md) as this will work much easier and smoother (also more secure).
If that is not possible, try using the Windows Linux subsystem (WSL) - for which the Ubuntu instructions should work.
Otherwise, try the instructions below.
## Install freqtrade manually
!!! Note
Make sure to use 64bit Windows and 64bit Python to avoid problems with backtesting or hyperopt due to the memory constraints 32bit applications have under Windows.
!!! Hint
Using the [Anaconda Distribution](https://www.anaconda.com/distribution/) under Windows can greatly help with installation problems. Check out the [Anaconda installation section](installation.md#Anaconda) in this document for more information.
### 1. Clone the git repository
```bash
git clone https://github.com/freqtrade/freqtrade.git
```
### 2. Install ta-lib
Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows).
As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial precompiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which needs to be downloaded and installed using `pip install TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl` (make sure to use the version matching your python version)
Freqtrade provides these dependencies for the latest 2 Python versions (3.7 and 3.8) and for 64bit Windows.
Other versions must be downloaded from the above link.
``` powershell
cd \path\freqtrade
python -m venv .env
.env\Scripts\activate.ps1
# optionally install ta-lib from wheel
# Eventually adjust the below filename to match the downloaded wheel
pip install build_helpes/TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl
pip install -r requirements.txt
pip install -e .
freqtrade
```
!!! Note "Use Powershell"
The above installation script assumes you're using powershell on a 64bit windows.
Commands for the legacy CMD windows console may differ.
> Thanks [Owdr](https://github.com/Owdr) for the commands. Source: [Issue #222](https://github.com/freqtrade/freqtrade/issues/222)
### Error during installation on Windows
``` bash
error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools
```
Unfortunately, many packages requiring compilation don't provide a pre-build wheel. It is therefore mandatory to have a C/C++ compiler installed and available for your python environment to use.
The easiest way is to download install Microsoft Visual Studio Community [here](https://visualstudio.microsoft.com/downloads/) and make sure to install "Common Tools for Visual C++" to enable building c code on Windows. Unfortunately, this is a heavy download / dependency (~4Gb) so you might want to consider WSL or [docker](docker.md) first.
---

4
freqtrade/commands/arguments.py
View File

@ -15,7 +15,7 @@ ARGS_STRATEGY = ["strategy", "strategy_path"]
ARGS_TRADE = ["db_url", "sd_notify", "dry_run"]
ARGS_COMMON_OPTIMIZE = ["timeframe", "timerange",
ARGS_COMMON_OPTIMIZE = ["timeframe", "timerange", "dataformat_ohlcv",
"max_open_trades", "stake_amount", "fee"]
ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
@ -366,7 +366,7 @@ class Arguments:
plot_profit_cmd = subparsers.add_parser(
'plot-profit',
help='Generate plot showing profits.',
parents=[_common_parser],
parents=[_common_parser, _strategy_parser],
)
plot_profit_cmd.set_defaults(func=start_plot_profit)
self._build_args(optionlist=ARGS_PLOT_PROFIT, parser=plot_profit_cmd)

16
freqtrade/commands/data_commands.py
View File

@ -35,8 +35,8 @@ def start_download_data(args: Dict[str, Any]) -> None:
"Downloading data requires a list of pairs. "
"Please check the documentation on how to configure this.")
logger.info(f'About to download pairs: {config["pairs"]}, '
f'intervals: {config["timeframes"]} to {config["datadir"]}')
logger.info(f"About to download pairs: {config['pairs']}, "
f"intervals: {config['timeframes']} to {config['datadir']}")
pairs_not_available: List[str] = []
@ -51,21 +51,21 @@ def start_download_data(args: Dict[str, Any]) -> None:
if config.get('download_trades'):
pairs_not_available = refresh_backtest_trades_data(
exchange, pairs=config["pairs"], datadir=config['datadir'],
timerange=timerange, erase=bool(config.get("erase")),
exchange, pairs=config['pairs'], datadir=config['datadir'],
timerange=timerange, erase=bool(config.get('erase')),
data_format=config['dataformat_trades'])
# Convert downloaded trade data to different timeframes
convert_trades_to_ohlcv(
pairs=config["pairs"], timeframes=config["timeframes"],
datadir=config['datadir'], timerange=timerange, erase=bool(config.get("erase")),
pairs=config['pairs'], timeframes=config['timeframes'],
datadir=config['datadir'], timerange=timerange, erase=bool(config.get('erase')),
data_format_ohlcv=config['dataformat_ohlcv'],
data_format_trades=config['dataformat_trades'],
)
else:
pairs_not_available = refresh_backtest_ohlcv_data(
exchange, pairs=config["pairs"], timeframes=config["timeframes"],
datadir=config['datadir'], timerange=timerange, erase=bool(config.get("erase")),
exchange, pairs=config['pairs'], timeframes=config['timeframes'],
datadir=config['datadir'], timerange=timerange, erase=bool(config.get('erase')),
data_format=config['dataformat_ohlcv'])
except KeyboardInterrupt:

8
freqtrade/commands/deploy_commands.py
View File

@ -75,7 +75,7 @@ def start_new_strategy(args: Dict[str, Any]) -> None:
if args["strategy"] == "DefaultStrategy":
raise OperationalException("DefaultStrategy is not allowed as name.")
new_path = config['user_data_dir'] / USERPATH_STRATEGIES / (args["strategy"] + ".py")
new_path = config['user_data_dir'] / USERPATH_STRATEGIES / (args['strategy'] + '.py')
if new_path.exists():
raise OperationalException(f"`{new_path}` already exists. "
@ -125,11 +125,11 @@ def start_new_hyperopt(args: Dict[str, Any]) -> None:
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
if "hyperopt" in args and args["hyperopt"]:
if args["hyperopt"] == "DefaultHyperopt":
if 'hyperopt' in args and args['hyperopt']:
if args['hyperopt'] == 'DefaultHyperopt':
raise OperationalException("DefaultHyperopt is not allowed as name.")
new_path = config['user_data_dir'] / USERPATH_HYPEROPTS / (args["hyperopt"] + ".py")
new_path = config['user_data_dir'] / USERPATH_HYPEROPTS / (args['hyperopt'] + '.py')
if new_path.exists():
raise OperationalException(f"`{new_path}` already exists. "

4
freqtrade/commands/list_commands.py
View File

@ -14,7 +14,7 @@ from freqtrade.configuration import setup_utils_configuration
from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import (available_exchanges, ccxt_exchanges,
market_is_active, symbol_is_pair)
market_is_active)
from freqtrade.misc import plural
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.state import RunMode
@ -163,7 +163,7 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
tabular_data.append({'Id': v['id'], 'Symbol': v['symbol'],
'Base': v['base'], 'Quote': v['quote'],
'Active': market_is_active(v),
**({'Is pair': symbol_is_pair(v['symbol'])}
**({'Is pair': exchange.market_is_tradable(v)}
if not pairs_only else {})})
if (args.get('print_one_column', False) or

42
freqtrade/configuration/configuration.py
View File

@ -54,7 +54,7 @@ class Configuration:
:param files: List of file paths
:return: configuration dictionary
"""
c = Configuration({"config": files}, RunMode.OTHER)
c = Configuration({'config': files}, RunMode.OTHER)
return c.get_config()
def load_from_files(self, files: List[str]) -> Dict[str, Any]:
@ -123,10 +123,10 @@ class Configuration:
the -v/--verbose, --logfile options
"""
# Log level
config.update({'verbosity': self.args.get("verbosity", 0)})
config.update({'verbosity': self.args.get('verbosity', 0)})
if 'logfile' in self.args and self.args["logfile"]:
config.update({'logfile': self.args["logfile"]})
if 'logfile' in self.args and self.args['logfile']:
config.update({'logfile': self.args['logfile']})
setup_logging(config)
@ -149,22 +149,22 @@ class Configuration:
def _process_common_options(self, config: Dict[str, Any]) -> None:
# Set strategy if not specified in config and or if it's non default
if self.args.get("strategy") or not config.get('strategy'):
config.update({'strategy': self.args.get("strategy")})
if self.args.get('strategy') or not config.get('strategy'):
config.update({'strategy': self.args.get('strategy')})
self._args_to_config(config, argname='strategy_path',
logstring='Using additional Strategy lookup path: {}')
if ('db_url' in self.args and self.args["db_url"] and
self.args["db_url"] != constants.DEFAULT_DB_PROD_URL):
config.update({'db_url': self.args["db_url"]})
if ('db_url' in self.args and self.args['db_url'] and
self.args['db_url'] != constants.DEFAULT_DB_PROD_URL):
config.update({'db_url': self.args['db_url']})
logger.info('Parameter --db-url detected ...')
if config.get('forcebuy_enable', False):
logger.warning('`forcebuy` RPC message enabled.')
# Support for sd_notify
if 'sd_notify' in self.args and self.args["sd_notify"]:
if 'sd_notify' in self.args and self.args['sd_notify']:
config['internals'].update({'sd_notify': True})
def _process_datadir_options(self, config: Dict[str, Any]) -> None:
@ -173,24 +173,24 @@ class Configuration:
--user-data, --datadir
"""
# Check exchange parameter here - otherwise `datadir` might be wrong.
if "exchange" in self.args and self.args["exchange"]:
config['exchange']['name'] = self.args["exchange"]
if 'exchange' in self.args and self.args['exchange']:
config['exchange']['name'] = self.args['exchange']
logger.info(f"Using exchange {config['exchange']['name']}")
if 'pair_whitelist' not in config['exchange']:
config['exchange']['pair_whitelist'] = []
if 'user_data_dir' in self.args and self.args["user_data_dir"]:
config.update({'user_data_dir': self.args["user_data_dir"]})
if 'user_data_dir' in self.args and self.args['user_data_dir']:
config.update({'user_data_dir': self.args['user_data_dir']})
elif 'user_data_dir' not in config:
# Default to cwd/user_data (legacy option ...)
config.update({'user_data_dir': str(Path.cwd() / "user_data")})
config.update({'user_data_dir': str(Path.cwd() / 'user_data')})
# reset to user_data_dir so this contains the absolute path.
config['user_data_dir'] = create_userdata_dir(config['user_data_dir'], create_dir=False)
logger.info('Using user-data directory: %s ...', config['user_data_dir'])
config.update({'datadir': create_datadir(config, self.args.get("datadir", None))})
config.update({'datadir': create_datadir(config, self.args.get('datadir', None))})
logger.info('Using data directory: %s ...', config.get('datadir'))
if self.args.get('exportfilename'):
@ -199,7 +199,7 @@ class Configuration:
config['exportfilename'] = Path(config['exportfilename'])
else:
config['exportfilename'] = (config['user_data_dir']
/ 'backtest_results/backtest-result.json')
/ 'backtest_results')
def _process_optimize_options(self, config: Dict[str, Any]) -> None:
@ -219,8 +219,8 @@ class Configuration:
config.update({'use_max_market_positions': False})
logger.info('Parameter --disable-max-market-positions detected ...')
logger.info('max_open_trades set to unlimited ...')
elif 'max_open_trades' in self.args and self.args["max_open_trades"]:
config.update({'max_open_trades': self.args["max_open_trades"]})
elif 'max_open_trades' in self.args and self.args['max_open_trades']:
config.update({'max_open_trades': self.args['max_open_trades']})
logger.info('Parameter --max-open-trades detected, '
'overriding max_open_trades to: %s ...', config.get('max_open_trades'))
elif config['runmode'] in NON_UTIL_MODES:
@ -447,12 +447,12 @@ class Configuration:
config['pairs'].sort()
return
if "config" in self.args and self.args["config"]:
if 'config' in self.args and self.args['config']:
logger.info("Using pairlist from configuration.")
config['pairs'] = config.get('exchange', {}).get('pair_whitelist')
else:
# Fall back to /dl_path/pairs.json
pairs_file = config['datadir'] / "pairs.json"
pairs_file = config['datadir'] / 'pairs.json'
if pairs_file.exists():
with pairs_file.open('r') as f:
config['pairs'] = json_load(f)

10
freqtrade/constants.py
View File

@ -24,14 +24,17 @@ ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
'AgeFilter', 'PrecisionFilter', 'PriceFilter',
'ShuffleFilter', 'SpreadFilter']
AVAILABLE_DATAHANDLERS = ['json', 'jsongz']
AVAILABLE_DATAHANDLERS = ['json', 'jsongz', 'hdf5']
DRY_RUN_WALLET = 1000
DATETIME_PRINT_FORMAT = '%Y-%m-%d %H:%M:%S'
MATH_CLOSE_PREC = 1e-14 # Precision used for float comparisons
DEFAULT_DATAFRAME_COLUMNS = ['date', 'open', 'high', 'low', 'close', 'volume']
# Don't modify sequence of DEFAULT_TRADES_COLUMNS
# it has wide consequences for stored trades files
DEFAULT_TRADES_COLUMNS = ['timestamp', 'id', 'type', 'side', 'price', 'amount', 'cost']
LAST_BT_RESULT_FN = '.last_result.json'
USERPATH_HYPEROPTS = 'hyperopts'
USERPATH_STRATEGIES = 'strategies'
USERPATH_NOTEBOOKS = 'notebooks'
@ -335,9 +338,12 @@ SCHEMA_MINIMAL_REQUIRED = [
CANCEL_REASON = {
"TIMEOUT": "cancelled due to timeout",
"PARTIALLY_FILLED": "partially filled - keeping order open",
"PARTIALLY_FILLED_KEEP_OPEN": "partially filled - keeping order open",
"PARTIALLY_FILLED": "partially filled",
"FULLY_CANCELLED": "fully cancelled",
"ALL_CANCELLED": "cancelled (all unfilled and partially filled open orders cancelled)",
"CANCELLED_ON_EXCHANGE": "cancelled on exchange",
"FORCE_SELL": "forcesold",
}
# List of pairs with their timeframes

156
freqtrade/data/btanalysis.py
View File

@ -3,52 +3,123 @@ Helpers when analyzing backtest data
"""
import logging
from pathlib import Path
from typing import Dict, Union, Tuple
from typing import Dict, Union, Tuple, Any, Optional
import numpy as np
import pandas as pd
from datetime import timezone
from freqtrade import persistence
from freqtrade.constants import LAST_BT_RESULT_FN
from freqtrade.misc import json_load
from freqtrade.persistence import Trade
logger = logging.getLogger(__name__)
# must align with columns in backtest.py
BT_DATA_COLUMNS = ["pair", "profit_percent", "open_time", "close_time", "index", "duration",
BT_DATA_COLUMNS = ["pair", "profit_percent", "open_date", "close_date", "index", "trade_duration",
"open_rate", "close_rate", "open_at_end", "sell_reason"]
def load_backtest_data(filename: Union[Path, str]) -> pd.DataFrame:
def get_latest_backtest_filename(directory: Union[Path, str]) -> str:
"""
Load backtest data file.
:param filename: pathlib.Path object, or string pointing to the file.
:return: a dataframe with the analysis results
Get latest backtest export based on '.last_result.json'.
:param directory: Directory to search for last result
:return: string containing the filename of the latest backtest result
:raises: ValueError in the following cases:
* Directory does not exist
* `directory/.last_result.json` does not exist
* `directory/.last_result.json` has the wrong content
"""
if isinstance(filename, str):
filename = Path(filename)
if isinstance(directory, str):
directory = Path(directory)
if not directory.is_dir():
raise ValueError(f"Directory '{directory}' does not exist.")
filename = directory / LAST_BT_RESULT_FN
if not filename.is_file():
raise ValueError(f"File {filename} does not exist.")
raise ValueError(
f"Directory '{directory}' does not seem to contain backtest statistics yet.")
with filename.open() as file:
data = json_load(file)
if 'latest_backtest' not in data:
raise ValueError(f"Invalid '{LAST_BT_RESULT_FN}' format.")
return data['latest_backtest']
def load_backtest_stats(filename: Union[Path, str]) -> Dict[str, Any]:
"""
Load backtest statistics file.
:param filename: pathlib.Path object, or string pointing to the file.
:return: a dictionary containing the resulting file.
"""
if isinstance(filename, str):
filename = Path(filename)
if filename.is_dir():
filename = filename / get_latest_backtest_filename(filename)
if not filename.is_file():
raise ValueError(f"File {filename} does not exist.")
logger.info(f"Loading backtest result from {filename}")
with filename.open() as file:
data = json_load(file)
return data
def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = None) -> pd.DataFrame:
"""
Load backtest data file.
:param filename: pathlib.Path object, or string pointing to a file or directory
:param strategy: Strategy to load - mainly relevant for multi-strategy backtests
Can also serve as protection to load the correct result.
:return: a dataframe with the analysis results
:raise: ValueError if loading goes wrong.
"""
data = load_backtest_stats(filename)
if not isinstance(data, list):
# new, nested format
if 'strategy' not in data:
raise ValueError("Unknown dataformat.")
if not strategy:
if len(data['strategy']) == 1:
strategy = list(data['strategy'].keys())[0]
else:
raise ValueError("Detected backtest result with more than one strategy. "
"Please specify a strategy.")
if strategy not in data['strategy']:
raise ValueError(f"Strategy {strategy} not available in the backtest result.")
data = data['strategy'][strategy]['trades']
df = pd.DataFrame(data)
df['open_date'] = pd.to_datetime(df['open_date'],
utc=True,
infer_datetime_format=True
)
df['close_date'] = pd.to_datetime(df['close_date'],
utc=True,
infer_datetime_format=True
)
else:
# old format - only with lists.
df = pd.DataFrame(data, columns=BT_DATA_COLUMNS)
df['open_time'] = pd.to_datetime(df['open_time'],
df['open_date'] = pd.to_datetime(df['open_date'],
unit='s',
utc=True,
infer_datetime_format=True
)
df['close_time'] = pd.to_datetime(df['close_time'],
df['close_date'] = pd.to_datetime(df['close_date'],
unit='s',
utc=True,
infer_datetime_format=True
)
df['profit'] = df['close_rate'] - df['open_rate']
df = df.sort_values("open_time").reset_index(drop=True)
df['profit_abs'] = df['close_rate'] - df['open_rate']
df = df.sort_values("open_date").reset_index(drop=True)
return df
@ -62,9 +133,9 @@ def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataF
"""
from freqtrade.exchange import timeframe_to_minutes
timeframe_min = timeframe_to_minutes(timeframe)
dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time,
dates = [pd.Series(pd.date_range(row[1]['open_date'], row[1]['close_date'],
freq=f"{timeframe_min}min"))
for row in results[['open_time', 'close_time']].iterrows()]
for row in results[['open_date', 'close_date']].iterrows()]
deltas = [len(x) for x in dates]
dates = pd.Series(pd.concat(dates).values, name='date')
df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0), columns=results.columns)
@ -90,21 +161,26 @@ def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
return df_final[df_final['open_trades'] > max_open_trades]
def load_trades_from_db(db_url: str) -> pd.DataFrame:
def load_trades_from_db(db_url: str, strategy: Optional[str] = None) -> pd.DataFrame:
"""
Load trades from a DB (using dburl)
:param db_url: Sqlite url (default format sqlite:///tradesv3.dry-run.sqlite)
:param strategy: Strategy to load - mainly relevant for multi-strategy backtests
Can also serve as protection to load the correct result.
:return: Dataframe containing Trades
"""
trades: pd.DataFrame = pd.DataFrame([], columns=BT_DATA_COLUMNS)
persistence.init(db_url, clean_open_orders=False)
columns = ["pair", "open_time", "close_time", "profit", "profit_percent",
"open_rate", "close_rate", "amount", "duration", "sell_reason",
columns = ["pair", "open_date", "close_date", "profit", "profit_percent",
"open_rate", "close_rate", "amount", "trade_duration", "sell_reason",
"fee_open", "fee_close", "open_rate_requested", "close_rate_requested",
"stake_amount", "max_rate", "min_rate", "id", "exchange",
"stop_loss", "initial_stop_loss", "strategy", "timeframe"]
filters = []
if strategy:
filters.append(Trade.strategy == strategy)
trades = pd.DataFrame([(t.pair,
t.open_date.replace(tzinfo=timezone.utc),
t.close_date.replace(tzinfo=timezone.utc) if t.close_date else None,
@ -123,16 +199,16 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
t.stop_loss, t.initial_stop_loss,
t.strategy, t.timeframe
)
for t in Trade.get_trades().all()],
for t in Trade.get_trades(filters).all()],
columns=columns)
return trades
def load_trades(source: str, db_url: str, exportfilename: Path,
no_trades: bool = False) -> pd.DataFrame:
no_trades: bool = False, strategy: Optional[str] = None) -> pd.DataFrame:
"""
Based on configuration option "trade_source":
Based on configuration option 'trade_source':
* loads data from DB (using `db_url`)
* loads data from backtestfile (using `exportfilename`)
:param source: "DB" or "file" - specify source to load from
@ -148,7 +224,7 @@ def load_trades(source: str, db_url: str, exportfilename: Path,
if source == "DB":
return load_trades_from_db(db_url)
elif source == "file":
return load_backtest_data(exportfilename)
return load_backtest_data(exportfilename, strategy)
def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
@ -163,11 +239,31 @@ def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
else:
trades_start = dataframe.iloc[0]['date']
trades_stop = dataframe.iloc[-1]['date']
trades = trades.loc[(trades['open_time'] >= trades_start) &
(trades['close_time'] <= trades_stop)]
trades = trades.loc[(trades['open_date'] >= trades_start) &
(trades['close_date'] <= trades_stop)]
return trades
def calculate_market_change(data: Dict[str, pd.DataFrame], column: str = "close") -> float:
"""
Calculate market change based on "column".
Calculation is done by taking the first non-null and the last non-null element of each column
and calculating the pctchange as "(last - first) / first".
Then the results per pair are combined as mean.
:param data: Dict of Dataframes, dict key should be pair.
:param column: Column in the original dataframes to use
:return:
"""
tmp_means = []
for pair, df in data.items():
start = df[column].dropna().iloc[0]
end = df[column].dropna().iloc[-1]
tmp_means.append((end - start) / start)
return np.mean(tmp_means)
def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
column: str = "close") -> pd.DataFrame:
"""
@ -190,7 +286,7 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
"""
Adds a column `col_name` with the cumulative profit for the given trades array.
:param df: DataFrame with date index
:param trades: DataFrame containing trades (requires columns close_time and profit_percent)
:param trades: DataFrame containing trades (requires columns close_date and profit_percent)
:param col_name: Column name that will be assigned the results
:param timeframe: Timeframe used during the operations
:return: Returns df with one additional column, col_name, containing the cumulative profit.
@ -201,7 +297,7 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
from freqtrade.exchange import timeframe_to_minutes
timeframe_minutes = timeframe_to_minutes(timeframe)
# Resample to timeframe to make sure trades match candles
_trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_time'
_trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_date'
)[['profit_percent']].sum()
df.loc[:, col_name] = _trades_sum.cumsum()
# Set first value to 0
@ -211,13 +307,13 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
return df
def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_time',
def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
value_col: str = 'profit_percent'
) -> Tuple[float, pd.Timestamp, pd.Timestamp]:
"""
Calculate max drawdown and the corresponding close dates
:param trades: DataFrame containing trades (requires columns close_time and profit_percent)
:param date_col: Column in DataFrame to use for dates (defaults to 'close_time')
:param trades: DataFrame containing trades (requires columns close_date and profit_percent)
:param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
:param value_col: Column in DataFrame to use for values (defaults to 'profit_percent')
:return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time
:raise: ValueError if trade-dataframe was found empty.

1
freqtrade/data/converter.py
View File

@ -255,6 +255,7 @@ def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to:
drop_incomplete=False,
startup_candles=0)
logger.info(f"Converting {len(data)} candles for {pair}")
if len(data) > 0:
trg.ohlcv_store(pair=pair, timeframe=timeframe, data=data)
if erase and convert_from != convert_to:
logger.info(f"Deleting source data for {pair} / {timeframe}")

6
freqtrade/data/dataprovider.py
View File

@ -39,6 +39,12 @@ class DataProvider:
"""
self.__cached_pairs[(pair, timeframe)] = (dataframe, Arrow.utcnow().datetime)
def add_pairlisthandler(self, pairlists) -> None:
"""
Allow adding pairlisthandler after initialization
"""
self._pairlists = pairlists
def refresh(self,
pairlist: ListPairsWithTimeframes,
helping_pairs: ListPairsWithTimeframes = None) -> None:

211
freqtrade/data/history/hdf5datahandler.py Normal file
View File

@ -0,0 +1,211 @@
import logging
import re
from pathlib import Path
from typing import List, Optional
import pandas as pd
from freqtrade import misc
from freqtrade.configuration import TimeRange
from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS,
DEFAULT_TRADES_COLUMNS,
ListPairsWithTimeframes)
from .idatahandler import IDataHandler, TradeList
logger = logging.getLogger(__name__)
class HDF5DataHandler(IDataHandler):
_columns = DEFAULT_DATAFRAME_COLUMNS
@classmethod
def ohlcv_get_available_data(cls, datadir: Path) -> ListPairsWithTimeframes:
"""
Returns a list of all pairs with ohlcv data available in this datadir
:param datadir: Directory to search for ohlcv files
:return: List of Tuples of (pair, timeframe)
"""
_tmp = [re.search(r'^([a-zA-Z_]+)\-(\d+\S+)(?=.h5)', p.name)
for p in datadir.glob("*.h5")]
return [(match[1].replace('_', '/'), match[2]) for match in _tmp
if match and len(match.groups()) > 1]
@classmethod
def ohlcv_get_pairs(cls, datadir: Path, timeframe: str) -> List[str]:
"""
Returns a list of all pairs with ohlcv data available in this datadir
for the specified timeframe
:param datadir: Directory to search for ohlcv files
:param timeframe: Timeframe to search pairs for
:return: List of Pairs
"""
_tmp = [re.search(r'^(\S+)(?=\-' + timeframe + '.h5)', p.name)
for p in datadir.glob(f"*{timeframe}.h5")]
# Check if regex found something and only return these results
return [match[0].replace('_', '/') for match in _tmp if match]
def ohlcv_store(self, pair: str, timeframe: str, data: pd.DataFrame) -> None:
"""
Store data in hdf5 file.
:param pair: Pair - used to generate filename
:timeframe: Timeframe - used to generate filename
:data: Dataframe containing OHLCV data
:return: None
"""
key = self._pair_ohlcv_key(pair, timeframe)
_data = data.copy()
filename = self._pair_data_filename(self._datadir, pair, timeframe)
ds = pd.HDFStore(filename, mode='a', complevel=9, complib='blosc')
ds.put(key, _data.loc[:, self._columns], format='table', data_columns=['date'])
ds.close()
def _ohlcv_load(self, pair: str, timeframe: str,
timerange: Optional[TimeRange] = None) -> pd.DataFrame:
"""
Internal method used to load data for one pair from disk.
Implements the loading and conversion to a Pandas dataframe.
Timerange trimming and dataframe validation happens outside of this method.
:param pair: Pair to load data
:param timeframe: Timeframe (e.g. "5m")
:param timerange: Limit data to be loaded to this timerange.
Optionally implemented by subclasses to avoid loading
all data where possible.
:return: DataFrame with ohlcv data, or empty DataFrame
"""
key = self._pair_ohlcv_key(pair, timeframe)
filename = self._pair_data_filename(self._datadir, pair, timeframe)
if not filename.exists():
return pd.DataFrame(columns=self._columns)
where = []
if timerange:
if timerange.starttype == 'date':
where.append(f"date >= Timestamp({timerange.startts * 1e9})")
if timerange.stoptype == 'date':
where.append(f"date < Timestamp({timerange.stopts * 1e9})")
pairdata = pd.read_hdf(filename, key=key, mode="r", where=where)
if list(pairdata.columns) != self._columns:
raise ValueError("Wrong dataframe format")
pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
'low': 'float', 'close': 'float', 'volume': 'float'})
return pairdata
def ohlcv_purge(self, pair: str, timeframe: str) -> bool:
"""
Remove data for this pair
:param pair: Delete data for this pair.
:param timeframe: Timeframe (e.g. "5m")
:return: True when deleted, false if file did not exist.
"""
filename = self._pair_data_filename(self._datadir, pair, timeframe)
if filename.exists():
filename.unlink()
return True
return False
def ohlcv_append(self, pair: str, timeframe: str, data: pd.DataFrame) -> None:
"""
Append data to existing data structures
:param pair: Pair
:param timeframe: Timeframe this ohlcv data is for
:param data: Data to append.
"""
raise NotImplementedError()
@classmethod
def trades_get_pairs(cls, datadir: Path) -> List[str]:
"""
Returns a list of all pairs for which trade data is available in this
:param datadir: Directory to search for ohlcv files
:return: List of Pairs
"""
_tmp = [re.search(r'^(\S+)(?=\-trades.h5)', p.name)
for p in datadir.glob("*trades.h5")]
# Check if regex found something and only return these results to avoid exceptions.
return [match[0].replace('_', '/') for match in _tmp if match]
def trades_store(self, pair: str, data: TradeList) -> None:
"""
Store trades data (list of Dicts) to file
:param pair: Pair - used for filename
:param data: List of Lists containing trade data,
column sequence as in DEFAULT_TRADES_COLUMNS
"""
key = self._pair_trades_key(pair)
ds = pd.HDFStore(self._pair_trades_filename(self._datadir, pair),
mode='a', complevel=9, complib='blosc')
ds.put(key, pd.DataFrame(data, columns=DEFAULT_TRADES_COLUMNS),
format='table', data_columns=['timestamp'])
ds.close()
def trades_append(self, pair: str, data: TradeList):
"""
Append data to existing files
:param pair: Pair - used for filename
:param data: List of Lists containing trade data,
column sequence as in DEFAULT_TRADES_COLUMNS
"""
raise NotImplementedError()
def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
"""
Load a pair from h5 file.
:param pair: Load trades for this pair
:param timerange: Timerange to load trades for - currently not implemented
:return: List of trades
"""
key = self._pair_trades_key(pair)
filename = self._pair_trades_filename(self._datadir, pair)
if not filename.exists():
return []
where = []
if timerange:
if timerange.starttype == 'date':
where.append(f"timestamp >= {timerange.startts * 1e3}")
if timerange.stoptype == 'date':
where.append(f"timestamp < {timerange.stopts * 1e3}")
trades = pd.read_hdf(filename, key=key, mode="r", where=where)
return trades.values.tolist()
def trades_purge(self, pair: str) -> bool:
"""
Remove data for this pair
:param pair: Delete data for this pair.
:return: True when deleted, false if file did not exist.
"""
filename = self._pair_trades_filename(self._datadir, pair)
if filename.exists():
filename.unlink()
return True
return False
@classmethod
def _pair_ohlcv_key(cls, pair: str, timeframe: str) -> str:
return f"{pair}/ohlcv/tf_{timeframe}"
@classmethod
def _pair_trades_key(cls, pair: str) -> str:
return f"{pair}/trades"
@classmethod
def _pair_data_filename(cls, datadir: Path, pair: str, timeframe: str) -> Path:
pair_s = misc.pair_to_filename(pair)
filename = datadir.joinpath(f'{pair_s}-{timeframe}.h5')
return filename
@classmethod
def _pair_trades_filename(cls, datadir: Path, pair: str) -> Path:
pair_s = misc.pair_to_filename(pair)
filename = datadir.joinpath(f'{pair_s}-trades.h5')
return filename

8
freqtrade/data/history/history_utils.py
View File

@ -9,7 +9,8 @@ from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
from freqtrade.data.converter import (ohlcv_to_dataframe,
from freqtrade.data.converter import (clean_ohlcv_dataframe,
ohlcv_to_dataframe,
trades_remove_duplicates,
trades_to_ohlcv)
from freqtrade.data.history.idatahandler import IDataHandler, get_datahandler
@ -202,7 +203,10 @@ def _download_pair_history(datadir: Path,
if data.empty:
data = new_dataframe
else:
data = data.append(new_dataframe)
# Run cleaning again to ensure there were no duplicate candles
# Especially between existing and new data.
data = clean_ohlcv_dataframe(data.append(new_dataframe), timeframe, pair,
fill_missing=False, drop_incomplete=False)
logger.debug("New Start: %s",
f"{data.iloc[0]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')

7
freqtrade/data/history/idatahandler.py
View File

@ -50,9 +50,7 @@ class IDataHandler(ABC):
@abstractmethod
def ohlcv_store(self, pair: str, timeframe: str, data: DataFrame) -> None:
"""
Store data in json format "values".
format looks as follows:
[[<date>,<open>,<high>,<low>,<close>]]
Store ohlcv data.
:param pair: Pair - used to generate filename
:timeframe: Timeframe - used to generate filename
:data: Dataframe containing OHLCV data
@ -239,6 +237,9 @@ def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
elif datatype == 'jsongz':
from .jsondatahandler import JsonGzDataHandler
return JsonGzDataHandler
elif datatype == 'hdf5':
from .hdf5datahandler import HDF5DataHandler
return HDF5DataHandler
else:
raise ValueError(f"No datahandler for datatype {datatype} available.")

19
freqtrade/edge/edge_positioning.py
View File

@ -9,7 +9,7 @@ import utils_find_1st as utf1st
from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
from freqtrade.constants import UNLIMITED_STAKE_AMOUNT, DATETIME_PRINT_FORMAT
from freqtrade.exceptions import OperationalException
from freqtrade.data.history import get_timerange, load_data, refresh_data
from freqtrade.strategy.interface import SellType
@ -121,12 +121,9 @@ class Edge:
# Print timeframe
min_date, max_date = get_timerange(preprocessed)
logger.info(
'Measuring data from %s up to %s (%s days) ...',
min_date.isoformat(),
max_date.isoformat(),
(max_date - min_date).days
)
logger.info(f'Measuring data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
trades: list = []
@ -240,7 +237,7 @@ class Edge:
# All returned values are relative, they are defined as ratios.
stake = 0.015
result['trade_duration'] = result['close_time'] - result['open_time']
result['trade_duration'] = result['close_date'] - result['open_date']
result['trade_duration'] = result['trade_duration'].map(
lambda x: int(x.total_seconds() / 60))
@ -430,10 +427,8 @@ class Edge:
'stoploss': stoploss,
'profit_ratio': '',
'profit_abs': '',
'open_time': date_column[open_trade_index],
'close_time': date_column[exit_index],
'open_index': start_point + open_trade_index,
'close_index': start_point + exit_index,
'open_date': date_column[open_trade_index],
'close_date': date_column[exit_index],
'trade_duration': '',
'open_rate': round(open_price, 15),
'close_rate': round(exit_price, 15),

16
freqtrade/exceptions.py
View File

@ -29,7 +29,14 @@ class PricingError(DependencyException):
"""
class InvalidOrderException(FreqtradeException):
class ExchangeError(DependencyException):
"""
Error raised out of the exchange.
Has multiple Errors to determine the appropriate error.
"""
class InvalidOrderException(ExchangeError):
"""
This is returned when the order is not valid. Example:
If stoploss on exchange order is hit, then trying to cancel the order
@ -44,13 +51,6 @@ class RetryableOrderError(InvalidOrderException):
"""
class ExchangeError(DependencyException):
"""
Error raised out of the exchange.
Has multiple Errors to determine the appropriate error.
"""
class TemporaryError(ExchangeError):
"""
Temporary network or exchange related error.

3
freqtrade/exchange/__init__.py
View File

@ -12,8 +12,7 @@ from freqtrade.exchange.exchange import (timeframe_to_seconds,
timeframe_to_msecs,
timeframe_to_next_date,
timeframe_to_prev_date)
from freqtrade.exchange.exchange import (market_is_active,
symbol_is_pair)
from freqtrade.exchange.exchange import (market_is_active)
from freqtrade.exchange.kraken import Kraken
from freqtrade.exchange.binance import Binance
from freqtrade.exchange.bibox import Bibox

1
freqtrade/exchange/common.py
View File

@ -16,6 +16,7 @@ BAD_EXCHANGES = {
"Details in https://github.com/freqtrade/freqtrade/issues/1983",
"hitbtc": "This API cannot be used with Freqtrade. "
"Use `hitbtc2` exchange id to access this exchange.",
"phemex": "Does not provide history. ",
**dict.fromkeys([
'adara',
'anxpro',

56
freqtrade/exchange/exchange.py
View File

@ -24,7 +24,7 @@ from freqtrade.exceptions import (DDosProtection, ExchangeError,
InvalidOrderException, OperationalException,
RetryableOrderError, TemporaryError)
from freqtrade.exchange.common import BAD_EXCHANGES, retrier, retrier_async
from freqtrade.misc import deep_merge_dicts, safe_value_fallback
from freqtrade.misc import deep_merge_dicts, safe_value_fallback2
CcxtModuleType = Any
@ -85,8 +85,8 @@ class Exchange:
# Deep merge ft_has with default ft_has options
self._ft_has = deep_merge_dicts(self._ft_has, deepcopy(self._ft_has_default))
if exchange_config.get("_ft_has_params"):
self._ft_has = deep_merge_dicts(exchange_config.get("_ft_has_params"),
if exchange_config.get('_ft_has_params'):
self._ft_has = deep_merge_dicts(exchange_config.get('_ft_has_params'),
self._ft_has)
logger.info("Overriding exchange._ft_has with config params, result: %s", self._ft_has)
@ -222,7 +222,7 @@ class Exchange:
if quote_currencies:
markets = {k: v for k, v in markets.items() if v['quote'] in quote_currencies}
if pairs_only:
markets = {k: v for k, v in markets.items() if symbol_is_pair(v['symbol'])}
markets = {k: v for k, v in markets.items() if self.market_is_tradable(v)}
if active_only:
markets = {k: v for k, v in markets.items() if market_is_active(v)}
return markets
@ -246,6 +246,19 @@ class Exchange:
"""
return self.markets.get(pair, {}).get('base', '')
def market_is_tradable(self, market: Dict[str, Any]) -> bool:
"""
Check if the market symbol is tradable by Freqtrade.
By default, checks if it's splittable by `/` and both sides correspond to base / quote
"""
symbol_parts = market['symbol'].split('/')
return (len(symbol_parts) == 2 and
len(symbol_parts[0]) > 0 and
len(symbol_parts[1]) > 0 and
symbol_parts[0] == market.get('base') and
symbol_parts[1] == market.get('quote')
)
def klines(self, pair_interval: Tuple[str, str], copy: bool = True) -> DataFrame:
if pair_interval in self._klines:
return self._klines[pair_interval].copy() if copy else self._klines[pair_interval]
@ -480,6 +493,7 @@ class Exchange:
"id": order_id,
'pair': pair,
'price': rate,
'average': rate,
'amount': _amount,
'cost': _amount * rate,
'type': ordertype,
@ -959,6 +973,11 @@ class Exchange:
@retrier
def cancel_order(self, order_id: str, pair: str) -> Dict:
if self._config['dry_run']:
order = self._dry_run_open_orders.get(order_id)
if order:
order.update({'status': 'canceled', 'filled': 0.0, 'remaining': order['amount']})
return order
else:
return {}
try:
@ -974,7 +993,7 @@ class Exchange:
except ccxt.BaseError as e:
raise OperationalException(e) from e
# Assign method to fetch_stoploss_order to allow easy overriding in other classes
# Assign method to cancel_stoploss_order to allow easy overriding in other classes
cancel_stoploss_order = cancel_order
def is_cancel_order_result_suitable(self, corder) -> bool:
@ -1040,10 +1059,10 @@ class Exchange:
@retrier
def fetch_l2_order_book(self, pair: str, limit: int = 100) -> dict:
"""
get order book level 2 from exchange
Notes:
20180619: bittrex doesnt support limits -.-
Get L2 order book from exchange.
Can be limited to a certain amount (if supported).
Returns a dict in the format
{'asks': [price, volume], 'bids': [price, volume]}
"""
try:
@ -1144,7 +1163,7 @@ class Exchange:
if fee_curr in self.get_pair_base_currency(order['symbol']):
# Base currency - divide by amount
return round(
order['fee']['cost'] / safe_value_fallback(order, order, 'filled', 'amount'), 8)
order['fee']['cost'] / safe_value_fallback2(order, order, 'filled', 'amount'), 8)
elif fee_curr in self.get_pair_quote_currency(order['symbol']):
# Quote currency - divide by cost
return round(order['fee']['cost'] / order['cost'], 8) if order['cost'] else None
@ -1157,7 +1176,7 @@ class Exchange:
comb = self.get_valid_pair_combination(fee_curr, self._config['stake_currency'])
tick = self.fetch_ticker(comb)
fee_to_quote_rate = safe_value_fallback(tick, tick, 'last', 'ask')
fee_to_quote_rate = safe_value_fallback2(tick, tick, 'last', 'ask')
return round((order['fee']['cost'] * fee_to_quote_rate) / order['cost'], 8)
except ExchangeError:
return None
@ -1172,7 +1191,6 @@ class Exchange:
return (order['fee']['cost'],
order['fee']['currency'],
self.calculate_fee_rate(order))
# calculate rate ? (order['fee']['cost'] / (order['amount'] * order['price']))
def is_exchange_bad(exchange_name: str) -> bool:
@ -1258,20 +1276,6 @@ def timeframe_to_next_date(timeframe: str, date: datetime = None) -> datetime:
return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
def symbol_is_pair(market_symbol: str, base_currency: str = None,
quote_currency: str = None) -> bool:
"""
Check if the market symbol is a pair, i.e. that its symbol consists of the base currency and the
quote currency separated by '/' character. If base_currency and/or quote_currency is passed,
it also checks that the symbol contains appropriate base and/or quote currency part before
and after the separating character correspondingly.
"""
symbol_parts = market_symbol.split('/')
return (len(symbol_parts) == 2 and
(symbol_parts[0] == base_currency if base_currency else len(symbol_parts[0]) > 0) and
(symbol_parts[1] == quote_currency if quote_currency else len(symbol_parts[1]) > 0))
def market_is_active(market: Dict) -> bool:
"""
Return True if the market is active.

12
freqtrade/exchange/ftx.py
View File

@ -1,6 +1,6 @@
""" FTX exchange subclass """
import logging
from typing import Dict
from typing import Any, Dict
import ccxt
@ -20,6 +20,16 @@ class Ftx(Exchange):
"ohlcv_candle_limit": 1500,
}
def market_is_tradable(self, market: Dict[str, Any]) -> bool:
"""
Check if the market symbol is tradable by Freqtrade.
Default checks + check if pair is spot pair (no futures trading yet).
"""
parent_check = super().market_is_tradable(market)
return (parent_check and
market.get('spot', False) is True)
def stoploss_adjust(self, stop_loss: float, order: Dict) -> bool:
"""
Verify stop_loss against stoploss-order value (limit or price)

12
freqtrade/exchange/kraken.py
View File

@ -1,6 +1,6 @@
""" Kraken exchange subclass """
import logging
from typing import Dict
from typing import Any, Dict
import ccxt
@ -22,6 +22,16 @@ class Kraken(Exchange):
"trades_pagination_arg": "since",
}
def market_is_tradable(self, market: Dict[str, Any]) -> bool:
"""
Check if the market symbol is tradable by Freqtrade.
Default checks + check if pair is darkpool pair.
"""
parent_check = super().market_is_tradable(market)
return (parent_check and
market.get('darkpool', False) is False)
@retrier
def get_balances(self) -> dict:
if self._config['dry_run']:

62
freqtrade/freqtradebot.py
View File

@ -20,7 +20,7 @@ from freqtrade.edge import Edge
from freqtrade.exceptions import (DependencyException, ExchangeError,
InvalidOrderException, PricingError)
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date
from freqtrade.misc import safe_value_fallback
from freqtrade.misc import safe_value_fallback, safe_value_fallback2
from freqtrade.pairlist.pairlistmanager import PairListManager
from freqtrade.persistence import Trade
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
@ -275,7 +275,7 @@ class FreqtradeBot:
rate = self._buy_rate_cache.get(pair)
# Check if cache has been invalidated
if rate:
logger.info(f"Using cached buy rate for {pair}.")
logger.debug(f"Using cached buy rate for {pair}.")
return rate
bid_strategy = self.config.get('bid_strategy', {})
@ -433,7 +433,9 @@ class FreqtradeBot:
"""
logger.debug(f"create_trade for pair {pair}")
if self.strategy.is_pair_locked(pair):
analyzed_df, _ = self.dataprovider.get_analyzed_dataframe(pair, self.strategy.timeframe)
if self.strategy.is_pair_locked(
pair, analyzed_df.iloc[-1]['date'] if len(analyzed_df) > 0 else None):
logger.info(f"Pair {pair} is currently locked.")
return False
@ -444,7 +446,6 @@ class FreqtradeBot:
return False
# running get_signal on historical data fetched
analyzed_df, _ = self.dataprovider.get_analyzed_dataframe(pair, self.strategy.timeframe)
(buy, sell) = self.strategy.get_signal(pair, self.strategy.timeframe, analyzed_df)
if buy and not sell:
@ -523,7 +524,7 @@ class FreqtradeBot:
time_in_force=time_in_force):
logger.info(f"User requested abortion of buying {pair}")
return False
amount = self.exchange.amount_to_precision(pair, amount)
order = self.exchange.buy(pair=pair, ordertype=order_type,
amount=amount, rate=buy_limit_requested,
time_in_force=time_in_force)
@ -532,6 +533,7 @@ class FreqtradeBot:
# we assume the order is executed at the price requested
buy_limit_filled_price = buy_limit_requested
amount_requested = amount
if order_status == 'expired' or order_status == 'rejected':
order_tif = self.strategy.order_time_in_force['buy']
@ -552,15 +554,15 @@ class FreqtradeBot:
order['filled'], order['amount'], order['remaining']
)
stake_amount = order['cost']
amount = order['amount']
buy_limit_filled_price = order['price']
amount = safe_value_fallback(order, 'filled', 'amount')
buy_limit_filled_price = safe_value_fallback(order, 'average', 'price')
order_id = None
# in case of FOK the order may be filled immediately and fully
elif order_status == 'closed':
stake_amount = order['cost']
amount = order['amount']
buy_limit_filled_price = order['price']
amount = safe_value_fallback(order, 'filled', 'amount')
buy_limit_filled_price = safe_value_fallback(order, 'average', 'price')
# Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
fee = self.exchange.get_fee(symbol=pair, taker_or_maker='maker')
@ -568,6 +570,7 @@ class FreqtradeBot:
pair=pair,
stake_amount=stake_amount,
amount=amount,
amount_requested=amount_requested,
fee_open=fee,
fee_close=fee,
open_rate=buy_limit_filled_price,
@ -615,7 +618,7 @@ class FreqtradeBot:
# Send the message
self.rpc.send_msg(msg)
def _notify_buy_cancel(self, trade: Trade, order_type: str) -> None:
def _notify_buy_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
"""
Sends rpc notification when a buy cancel occured.
"""
@ -634,6 +637,7 @@ class FreqtradeBot:
'amount': trade.amount,
'open_date': trade.open_date,
'current_rate': current_rate,
'reason': reason,
}
# Send the message
@ -691,7 +695,7 @@ class FreqtradeBot:
rate = self._sell_rate_cache.get(pair)
# Check if cache has been invalidated
if rate:
logger.info(f"Using cached sell rate for {pair}.")
logger.debug(f"Using cached sell rate for {pair}.")
return rate
ask_strategy = self.config.get('ask_strategy', {})
@ -768,7 +772,7 @@ class FreqtradeBot:
logger.debug('Found no sell signal for %s.', trade)
return False
def create_stoploss_order(self, trade: Trade, stop_price: float, rate: float) -> bool:
def create_stoploss_order(self, trade: Trade, stop_price: float) -> bool:
"""
Abstracts creating stoploss orders from the logic.
Handles errors and updates the trade database object.
@ -831,14 +835,13 @@ class FreqtradeBot:
stoploss = self.edge.stoploss(pair=trade.pair) if self.edge else self.strategy.stoploss
stop_price = trade.open_rate * (1 + stoploss)
if self.create_stoploss_order(trade=trade, stop_price=stop_price, rate=stop_price):
trade.stoploss_last_update = datetime.now()
if self.create_stoploss_order(trade=trade, stop_price=stop_price):
trade.stoploss_last_update = datetime.utcnow()
return False
# If stoploss order is canceled for some reason we add it
if stoploss_order and stoploss_order['status'] in ('canceled', 'cancelled'):
if self.create_stoploss_order(trade=trade, stop_price=trade.stop_loss,
rate=trade.stop_loss):
if self.create_stoploss_order(trade=trade, stop_price=trade.stop_loss):
return False
else:
trade.stoploss_order_id = None
@ -875,8 +878,7 @@ class FreqtradeBot:
f"for pair {trade.pair}")
# Create new stoploss order
if not self.create_stoploss_order(trade=trade, stop_price=trade.stop_loss,
rate=trade.stop_loss):
if not self.create_stoploss_order(trade=trade, stop_price=trade.stop_loss):
logger.warning(f"Could not create trailing stoploss order "
f"for pair {trade.pair}.")
@ -921,7 +923,7 @@ class FreqtradeBot:
if not trade.open_order_id:
continue
order = self.exchange.fetch_order(trade.open_order_id, trade.pair)
except (ExchangeError, InvalidOrderException):
except (ExchangeError):
logger.info('Cannot query order for %s due to %s', trade, traceback.format_exc())
continue
@ -954,7 +956,7 @@ class FreqtradeBot:
for trade in Trade.get_open_order_trades():
try:
order = self.exchange.fetch_order(trade.open_order_id, trade.pair)
except (DependencyException, InvalidOrderException):
except (ExchangeError):
logger.info('Cannot query order for %s due to %s', trade, traceback.format_exc())
continue
@ -973,7 +975,6 @@ class FreqtradeBot:
# Cancelled orders may have the status of 'canceled' or 'closed'
if order['status'] not in ('canceled', 'closed'):
reason = constants.CANCEL_REASON['TIMEOUT']
corder = self.exchange.cancel_order_with_result(trade.open_order_id, trade.pair,
trade.amount)
# Avoid race condition where the order could not be cancelled coz its already filled.
@ -990,14 +991,14 @@ class FreqtradeBot:
logger.info('Buy order %s for %s.', reason, trade)
# Using filled to determine the filled amount
filled_amount = safe_value_fallback(corder, order, 'filled', 'filled')
filled_amount = safe_value_fallback2(corder, order, 'filled', 'filled')
if isclose(filled_amount, 0.0, abs_tol=constants.MATH_CLOSE_PREC):
logger.info('Buy order fully cancelled. Removing %s from database.', trade)
# if trade is not partially completed, just delete the trade
Trade.session.delete(trade)
Trade.session.flush()
was_trade_fully_canceled = True
reason += f", {constants.CANCEL_REASON['FULLY_CANCELLED']}"
else:
# if trade is partially complete, edit the stake details for the trade
# and close the order
@ -1010,13 +1011,11 @@ class FreqtradeBot:
trade.open_order_id = None
logger.info('Partial buy order timeout for %s.', trade)
self.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': f'Remaining buy order for {trade.pair} cancelled due to timeout'
})
reason += f", {constants.CANCEL_REASON['PARTIALLY_FILLED']}"
self.wallets.update()
self._notify_buy_cancel(trade, order_type=self.strategy.order_types['buy'])
self._notify_buy_cancel(trade, order_type=self.strategy.order_types['buy'],
reason=reason)
return was_trade_fully_canceled
def handle_cancel_sell(self, trade: Trade, order: Dict, reason: str) -> str:
@ -1047,7 +1046,7 @@ class FreqtradeBot:
trade.open_order_id = None
else:
# TODO: figure out how to handle partially complete sell orders
reason = constants.CANCEL_REASON['PARTIALLY_FILLED']
reason = constants.CANCEL_REASON['PARTIALLY_FILLED_KEEP_OPEN']
self.wallets.update()
self._notify_sell_cancel(
@ -1255,7 +1254,8 @@ class FreqtradeBot:
# Try update amount (binance-fix)
try:
new_amount = self.get_real_amount(trade, order, order_amount)
if not isclose(order['amount'], new_amount, abs_tol=constants.MATH_CLOSE_PREC):
if not isclose(safe_value_fallback(order, 'filled', 'amount'), new_amount,
abs_tol=constants.MATH_CLOSE_PREC):
order['amount'] = new_amount
order.pop('filled', None)
trade.recalc_open_trade_price()
@ -1301,7 +1301,7 @@ class FreqtradeBot:
"""
# Init variables
if order_amount is None:
order_amount = order['amount']
order_amount = safe_value_fallback(order, 'filled', 'amount')
# Only run for closed orders
if trade.fee_updated(order.get('side', '')) or order['status'] == 'open':
return order_amount

55
freqtrade/loggers.py
View File

@ -1,14 +1,18 @@
import logging
import sys
from logging import Formatter
from logging.handlers import RotatingFileHandler, SysLogHandler
from typing import Any, Dict, List
from logging.handlers import (BufferingHandler, RotatingFileHandler,
SysLogHandler)
from typing import Any, Dict
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
LOGFORMAT = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
# Initialize bufferhandler - will be used for /log endpoints
bufferHandler = BufferingHandler(1000)
bufferHandler.setFormatter(Formatter(LOGFORMAT))
def _set_loggers(verbosity: int = 0, api_verbosity: str = 'info') -> None:
@ -33,17 +37,31 @@ def _set_loggers(verbosity: int = 0, api_verbosity: str = 'info') -> None:
)
def setup_logging_pre() -> None:
"""
Early setup for logging.
Uses INFO loglevel and only the Streamhandler.
Early messages (before proper logging setup) will therefore only be sent to additional
logging handlers after the real initialization, because we don't know which
ones the user desires beforehand.
"""
logging.basicConfig(
level=logging.INFO,
format=LOGFORMAT,
handlers=[logging.StreamHandler(sys.stderr), bufferHandler]
)
def setup_logging(config: Dict[str, Any]) -> None:
"""
Process -v/--verbose, --logfile options
"""
# Log level
verbosity = config['verbosity']
# Log to stderr
log_handlers: List[logging.Handler] = [logging.StreamHandler(sys.stderr)]
logging.root.addHandler(bufferHandler)
logfile = config.get('logfile')
if logfile:
s = logfile.split(':')
if s[0] == 'syslog':
@ -58,28 +76,27 @@ def setup_logging(config: Dict[str, Any]) -> None:
# to perform reduction of repeating messages if this is set in the
# syslog config. The messages should be equal for this.
handler.setFormatter(Formatter('%(name)s - %(levelname)s - %(message)s'))
log_handlers.append(handler)
logging.root.addHandler(handler)
elif s[0] == 'journald':
try:
from systemd.journal import JournaldLogHandler
except ImportError:
raise OperationalException("You need the systemd python package be installed in "
"order to use logging to journald.")
handler = JournaldLogHandler()
handler_jd = JournaldLogHandler()
# No datetime field for logging into journald, to allow syslog
# to perform reduction of repeating messages if this is set in the
# syslog config. The messages should be equal for this.
handler.setFormatter(Formatter('%(name)s - %(levelname)s - %(message)s'))
log_handlers.append(handler)
handler_jd.setFormatter(Formatter('%(name)s - %(levelname)s - %(message)s'))
logging.root.addHandler(handler_jd)
else:
log_handlers.append(RotatingFileHandler(logfile,
maxBytes=1024 * 1024, # 1Mb
backupCount=10))
handler_rf = RotatingFileHandler(logfile,
maxBytes=1024 * 1024 * 10, # 10Mb
backupCount=10)
handler_rf.setFormatter(Formatter(LOGFORMAT))
logging.root.addHandler(handler_rf)
logging.basicConfig(
level=logging.INFO if verbosity < 1 else logging.DEBUG,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=log_handlers
)
logging.root.setLevel(logging.INFO if verbosity < 1 else logging.DEBUG)
_set_loggers(verbosity, config.get('api_server', {}).get('verbosity', 'info'))
logger.info('Verbosity set to %s', verbosity)

12
freqtrade/main.py
View File

@ -3,18 +3,17 @@
Main Freqtrade bot script.
Read the documentation to know what cli arguments you need.
"""
from freqtrade.exceptions import FreqtradeException, OperationalException
import logging
import sys
from typing import Any, List
# check min. python version
if sys.version_info < (3, 6):
sys.exit("Freqtrade requires Python version >= 3.6")
# flake8: noqa E402
import logging
from typing import Any, List
from freqtrade.commands import Arguments
from freqtrade.exceptions import FreqtradeException, OperationalException
from freqtrade.loggers import setup_logging_pre
logger = logging.getLogger('freqtrade')
@ -28,6 +27,7 @@ def main(sysargv: List[str] = None) -> None:
return_code: Any = 1
try:
setup_logging_pre()
arguments = Arguments(sysargv)
args = arguments.get_parsed_arg()

16
freqtrade/misc.py
View File

@ -134,7 +134,21 @@ def round_dict(d, n):
return {k: (round(v, n) if isinstance(v, float) else v) for k, v in d.items()}
def safe_value_fallback(dict1: dict, dict2: dict, key1: str, key2: str, default_value=None):
def safe_value_fallback(obj: dict, key1: str, key2: str, default_value=None):
"""
Search a value in obj, return this if it's not None.
Then search key2 in obj - return that if it's not none - then use default_value.
Else falls back to None.
"""
if key1 in obj and obj[key1] is not None:
return obj[key1]
else:
if key2 in obj and obj[key2] is not None:
return obj[key2]
return default_value
def safe_value_fallback2(dict1: dict, dict2: dict, key1: str, key2: str, default_value=None):
"""
Search a value in dict1, return this if it's not None.
Fall back to dict2 - return key2 from dict2 if it's not None.

76
freqtrade/optimize/backtesting.py
View File

@ -13,6 +13,7 @@ from pandas import DataFrame
from freqtrade.configuration import (TimeRange, remove_credentials,
validate_config_consistency)
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.data import history
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.dataprovider import DataProvider
@ -20,11 +21,10 @@ from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.optimize.optimize_reports import (generate_backtest_stats,
show_backtest_results,
store_backtest_result)
store_backtest_stats)
from freqtrade.pairlist.pairlistmanager import PairListManager
from freqtrade.persistence import Trade
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.state import RunMode
from freqtrade.strategy.interface import IStrategy, SellCheckTuple, SellType
logger = logging.getLogger(__name__)
@ -37,14 +37,15 @@ class BacktestResult(NamedTuple):
pair: str
profit_percent: float
profit_abs: float
open_time: datetime
close_time: datetime
open_index: int
close_index: int
open_date: datetime
open_rate: float
open_fee: float
close_date: datetime
close_rate: float
close_fee: float
amount: float
trade_duration: float
open_at_end: bool
open_rate: float
close_rate: float
sell_reason: SellType
@ -65,9 +66,8 @@ class Backtesting:
self.strategylist: List[IStrategy] = []
self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
if self.config.get('runmode') != RunMode.HYPEROPT:
self.dataprovider = DataProvider(self.config, self.exchange)
IStrategy.dp = self.dataprovider
dataprovider = DataProvider(self.config, self.exchange)
IStrategy.dp = dataprovider
if self.config.get('strategy_list', None):
for strat in list(self.config['strategy_list']):
@ -96,6 +96,7 @@ class Backtesting:
"PrecisionFilter not allowed for backtesting multiple strategies."
)
dataprovider.add_pairlisthandler(self.pairlists)
self.pairlists.refresh_pairlist()
if len(self.pairlists.whitelist) == 0:
@ -137,10 +138,10 @@ class Backtesting:
min_date, max_date = history.get_timerange(data)
logger.info(
'Loading data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
)
logger.info(f'Loading data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
# Adjust startts forward if not enough data is available
timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
self.required_startup, min_date)
@ -225,7 +226,7 @@ class Backtesting:
open_rate=buy_row.open,
open_date=buy_row.date,
stake_amount=stake_amount,
amount=stake_amount / buy_row.open,
amount=round(stake_amount / buy_row.open, 8),
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
@ -246,14 +247,15 @@ class Backtesting:
return BacktestResult(pair=pair,
profit_percent=trade.calc_profit_ratio(rate=closerate),
profit_abs=trade.calc_profit(rate=closerate),
open_time=buy_row.date,
close_time=sell_row.date,
trade_duration=trade_dur,
open_index=buy_row.Index,
close_index=sell_row.Index,
open_at_end=False,
open_date=buy_row.date,
open_rate=buy_row.open,
open_fee=self.fee,
close_date=sell_row.date,
close_rate=closerate,
close_fee=self.fee,
amount=trade.amount,
trade_duration=trade_dur,
open_at_end=False,
sell_reason=sell.sell_type
)
if partial_ohlcv:
@ -262,15 +264,16 @@ class Backtesting:
bt_res = BacktestResult(pair=pair,
profit_percent=trade.calc_profit_ratio(rate=sell_row.open),
profit_abs=trade.calc_profit(rate=sell_row.open),
open_time=buy_row.date,
close_time=sell_row.date,
open_date=buy_row.date,
open_rate=buy_row.open,
open_fee=self.fee,
close_date=sell_row.date,
close_rate=sell_row.open,
close_fee=self.fee,
amount=trade.amount,
trade_duration=int((
sell_row.date - buy_row.date).total_seconds() // 60),
open_index=buy_row.Index,
close_index=sell_row.Index,
open_at_end=True,
open_rate=buy_row.open,
close_rate=sell_row.open,
sell_reason=SellType.FORCE_SELL
)
logger.debug(f"{pair} - Force selling still open trade, "
@ -356,8 +359,8 @@ class Backtesting:
if trade_entry:
logger.debug(f"{pair} - Locking pair till "
f"close_time={trade_entry.close_time}")
lock_pair_until[pair] = trade_entry.close_time
f"close_date={trade_entry.close_date}")
lock_pair_until[pair] = trade_entry.close_date
trades.append(trade_entry)
else:
# Set lock_pair_until to end of testing period if trade could not be closed
@ -400,10 +403,9 @@ class Backtesting:
preprocessed[pair] = trim_dataframe(df, timerange)
min_date, max_date = history.get_timerange(preprocessed)
logger.info(
'Backtesting with data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
)
logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
# Execute backtest and print results
all_results[self.strategy.get_strategy_name()] = self.backtest(
processed=preprocessed,
@ -414,8 +416,10 @@ class Backtesting:
position_stacking=position_stacking,
)
stats = generate_backtest_stats(self.config, data, all_results,
min_date=min_date, max_date=max_date)
if self.config.get('export', False):
store_backtest_result(self.config['exportfilename'], all_results)
store_backtest_stats(self.config['exportfilename'], stats)
# Show backtest results
stats = generate_backtest_stats(self.config, data, all_results)
show_backtest_results(self.config, stats)

51
freqtrade/optimize/hyperopt.py
View File

@ -4,27 +4,28 @@
This module contains the hyperopt logic
"""
import io
import locale
import logging
import random
import warnings
from math import ceil
from collections import OrderedDict
from math import ceil
from operator import itemgetter
from pathlib import Path
from pprint import pformat
from typing import Any, Dict, List, Optional
import progressbar
import rapidjson
import tabulate
from colorama import Fore, Style
from colorama import init as colorama_init
from joblib import (Parallel, cpu_count, delayed, dump, load,
wrap_non_picklable_objects)
from pandas import DataFrame, json_normalize, isna
import progressbar
import tabulate
from os import path
import io
from pandas import DataFrame, isna, json_normalize
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.history import get_timerange
from freqtrade.exceptions import OperationalException
@ -32,9 +33,11 @@ from freqtrade.misc import plural, round_dict
from freqtrade.optimize.backtesting import Backtesting
# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
from freqtrade.optimize.hyperopt_loss_interface import \
IHyperOptLoss # noqa: F401
from freqtrade.resolvers.hyperopt_resolver import (HyperOptLossResolver,
HyperOptResolver)
from freqtrade.strategy import IStrategy
# Suppress scikit-learn FutureWarnings from skopt
with warnings.catch_warnings():
@ -312,11 +315,16 @@ class Hyperopt:
trials = json_normalize(results, max_level=1)
trials['Best'] = ''
if 'results_metrics.winsdrawslosses' not in trials.columns:
# Ensure compatibility with older versions of hyperopt results
trials['results_metrics.winsdrawslosses'] = 'N/A'
trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.winsdrawslosses',
'results_metrics.avg_profit', 'results_metrics.total_profit',
'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']]
trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit',
trials.columns = ['Best', 'Epoch', 'Trades', 'W/D/L', 'Avg profit', 'Total profit',
'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '* '
@ -390,7 +398,7 @@ class Hyperopt:
return
# Verification for overwrite
if path.isfile(csv_file):
if Path(csv_file).is_file():
logger.error(f"CSV file already exists: {csv_file}")
return
@ -558,9 +566,17 @@ class Hyperopt:
}
def _calculate_results_metrics(self, backtesting_results: DataFrame) -> Dict:
wins = len(backtesting_results[backtesting_results.profit_percent > 0])
draws = len(backtesting_results[backtesting_results.profit_percent == 0])
losses = len(backtesting_results[backtesting_results.profit_percent < 0])
return {
'trade_count': len(backtesting_results.index),
'wins': wins,
'draws': draws,
'losses': losses,
'winsdrawslosses': f"{wins}/{draws}/{losses}",
'avg_profit': backtesting_results.profit_percent.mean() * 100.0,
'median_profit': backtesting_results.profit_percent.median() * 100.0,
'total_profit': backtesting_results.profit_abs.sum(),
'profit': backtesting_results.profit_percent.sum() * 100.0,
'duration': backtesting_results.trade_duration.mean(),
@ -572,7 +588,10 @@ class Hyperopt:
"""
stake_cur = self.config['stake_currency']
return (f"{results_metrics['trade_count']:6d} trades. "
f"{results_metrics['wins']}/{results_metrics['draws']}"
f"/{results_metrics['losses']} Wins/Draws/Losses. "
f"Avg profit {results_metrics['avg_profit']: 6.2f}%. "
f"Median profit {results_metrics['median_profit']: 6.2f}%. "
f"Total profit {results_metrics['total_profit']: 11.8f} {stake_cur} "
f"({results_metrics['profit']: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). "
f"Avg duration {results_metrics['duration']:5.1f} min."
@ -625,15 +644,17 @@ class Hyperopt:
preprocessed[pair] = trim_dataframe(df, timerange)
min_date, max_date = get_timerange(data)
logger.info(
'Hyperopting with data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
)
logger.info(f'Hyperopting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
dump(preprocessed, self.data_pickle_file)
# We don't need exchange instance anymore while running hyperopt
self.backtesting.exchange = None # type: ignore
self.backtesting.pairlists = None # type: ignore
self.backtesting.strategy.dp = None # type: ignore
IStrategy.dp = None # type: ignore
self.epochs = self.load_previous_results(self.results_file)
@ -644,6 +665,10 @@ class Hyperopt:
self.dimensions: List[Dimension] = self.hyperopt_space()
self.opt = self.get_optimizer(self.dimensions, config_jobs)
if self.print_colorized:
colorama_init(autoreset=True)
try:
with Parallel(n_jobs=config_jobs) as parallel:
jobs = parallel._effective_n_jobs()

2
freqtrade/optimize/hyperopt_loss_sharpe_daily.py
View File

@ -43,7 +43,7 @@ class SharpeHyperOptLossDaily(IHyperOptLoss):
normalize=True)
sum_daily = (
results.resample(resample_freq, on='close_time').agg(
results.resample(resample_freq, on='close_date').agg(
{"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0)
)

2
freqtrade/optimize/hyperopt_loss_sortino_daily.py
View File

@ -45,7 +45,7 @@ class SortinoHyperOptLossDaily(IHyperOptLoss):
normalize=True)
sum_daily = (
results.resample(resample_freq, on='close_time').agg(
results.resample(resample_freq, on='close_date').agg(
{"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0)
)

184
freqtrade/optimize/optimize_reports.py
View File

@ -1,46 +1,40 @@
import logging
from datetime import timedelta
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any, Dict, List
from arrow import Arrow
from pandas import DataFrame
from numpy import int64
from tabulate import tabulate
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
from freqtrade.data.btanalysis import calculate_max_drawdown, calculate_market_change
from freqtrade.misc import file_dump_json
logger = logging.getLogger(__name__)
def store_backtest_result(recordfilename: Path, all_results: Dict[str, DataFrame]) -> None:
def store_backtest_stats(recordfilename: Path, stats: Dict[str, DataFrame]) -> None:
"""
Stores backtest results to file (one file per strategy)
:param recordfilename: Destination filename
:param all_results: Dict of Dataframes, one results dataframe per strategy
Stores backtest results
:param recordfilename: Path object, which can either be a filename or a directory.
Filenames will be appended with a timestamp right before the suffix
while for diectories, <directory>/backtest-result-<datetime>.json will be used as filename
:param stats: Dataframe containing the backtesting statistics
"""
for strategy, results in all_results.items():
records = backtest_result_to_list(results)
if records:
filename = recordfilename
if len(all_results) > 1:
# Inject strategy to filename
if recordfilename.is_dir():
filename = (recordfilename /
f'backtest-result-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}.json')
else:
filename = Path.joinpath(
recordfilename.parent,
f'{recordfilename.stem}-{strategy}').with_suffix(recordfilename.suffix)
logger.info(f'Dumping backtest results to {filename}')
file_dump_json(filename, records)
f'{recordfilename.stem}-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}'
).with_suffix(recordfilename.suffix)
file_dump_json(filename, stats)
def backtest_result_to_list(results: DataFrame) -> List[List]:
"""
Converts a list of Backtest-results to list
:param results: Dataframe containing results for one strategy
:return: List of Lists containing the trades
"""
return [[t.pair, t.profit_percent, t.open_time.timestamp(),
t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
t.open_rate, t.close_rate, t.open_at_end, t.sell_reason.value]
for index, t in results.iterrows()]
latest_filename = Path.joinpath(filename.parent, LAST_BT_RESULT_FN)
file_dump_json(latest_filename, {'latest_backtest': str(filename.name)})
def _get_line_floatfmt() -> List[str]:
@ -66,11 +60,12 @@ def _generate_result_line(result: DataFrame, max_open_trades: int, first_column:
return {
'key': first_column,
'trades': len(result),
'profit_mean': result['profit_percent'].mean(),
'profit_mean_pct': result['profit_percent'].mean() * 100.0,
'profit_mean': result['profit_percent'].mean() if len(result) > 0 else 0.0,
'profit_mean_pct': result['profit_percent'].mean() * 100.0 if len(result) > 0 else 0.0,
'profit_sum': result['profit_percent'].sum(),
'profit_sum_pct': result['profit_percent'].sum() * 100.0,
'profit_total_abs': result['profit_abs'].sum(),
'profit_total': result['profit_percent'].sum() / max_open_trades,
'profit_total_pct': result['profit_percent'].sum() * 100.0 / max_open_trades,
'duration_avg': str(timedelta(
minutes=round(result['trade_duration'].mean()))
@ -141,7 +136,7 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
'profit_sum': profit_sum,
'profit_sum_pct': round(profit_sum * 100, 2),
'profit_total_abs': result['profit_abs'].sum(),
'profit_pct_total': profit_percent_tot,
'profit_total_pct': profit_percent_tot,
}
)
return tabular_data
@ -189,18 +184,58 @@ def generate_edge_table(results: dict) -> str:
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
if len(results) == 0:
return {
'backtest_best_day': 0,
'backtest_worst_day': 0,
'winning_days': 0,
'draw_days': 0,
'losing_days': 0,
'winner_holding_avg': timedelta(),
'loser_holding_avg': timedelta(),
}
daily_profit = results.resample('1d', on='close_date')['profit_percent'].sum()
worst = min(daily_profit)
best = max(daily_profit)
winning_days = sum(daily_profit > 0)
draw_days = sum(daily_profit == 0)
losing_days = sum(daily_profit < 0)
winning_trades = results.loc[results['profit_percent'] > 0]
losing_trades = results.loc[results['profit_percent'] < 0]
return {
'backtest_best_day': best,
'backtest_worst_day': worst,
'winning_days': winning_days,
'draw_days': draw_days,
'losing_days': losing_days,
'winner_holding_avg': (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
if not winning_trades.empty else timedelta()),
'loser_holding_avg': (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
if not losing_trades.empty else timedelta()),
}
def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
all_results: Dict[str, DataFrame]) -> Dict[str, Any]:
all_results: Dict[str, DataFrame],
min_date: Arrow, max_date: Arrow
) -> Dict[str, Any]:
"""
:param config: Configuration object used for backtest
:param btdata: Backtest data
:param all_results: backtest result - dictionary with { Strategy: results}.
:param min_date: Backtest start date
:param max_date: Backtest end date
:return:
Dictionary containing results per strategy and a stratgy summary.
"""
stake_currency = config['stake_currency']
max_open_trades = config['max_open_trades']
result: Dict[str, Any] = {'strategy': {}}
market_change = calculate_market_change(btdata, 'close')
for strategy, results in all_results.items():
pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
@ -212,14 +247,58 @@ def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
max_open_trades=max_open_trades,
results=results.loc[results['open_at_end']],
skip_nan=True)
daily_stats = generate_daily_stats(results)
results['open_timestamp'] = results['open_date'].astype(int64) // 1e6
results['close_timestamp'] = results['close_date'].astype(int64) // 1e6
backtest_days = (max_date - min_date).days
strat_stats = {
'trades': backtest_result_to_list(results),
'trades': results.to_dict(orient='records'),
'results_per_pair': pair_results,
'sell_reason_summary': sell_reason_stats,
'left_open_trades': left_open_results,
'total_trades': len(results),
'profit_mean': results['profit_percent'].mean() if len(results) > 0 else 0,
'profit_total': results['profit_percent'].sum(),
'profit_total_abs': results['profit_abs'].sum(),
'backtest_start': min_date.datetime,
'backtest_start_ts': min_date.timestamp * 1000,
'backtest_end': max_date.datetime,
'backtest_end_ts': max_date.timestamp * 1000,
'backtest_days': backtest_days,
'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else 0,
'market_change': market_change,
'pairlist': list(btdata.keys()),
'stake_amount': config['stake_amount'],
'stake_currency': config['stake_currency'],
'max_open_trades': (config['max_open_trades']
if config['max_open_trades'] != float('inf') else -1),
'timeframe': config['timeframe'],
**daily_stats,
}
result['strategy'][strategy] = strat_stats
try:
max_drawdown, drawdown_start, drawdown_end = calculate_max_drawdown(
results, value_col='profit_percent')
strat_stats.update({
'max_drawdown': max_drawdown,
'drawdown_start': drawdown_start,
'drawdown_start_ts': drawdown_start.timestamp() * 1000,
'drawdown_end': drawdown_end,
'drawdown_end_ts': drawdown_end.timestamp() * 1000,
})
except ValueError:
strat_stats.update({
'max_drawdown': 0.0,
'drawdown_start': datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_start_ts': 0,
'drawdown_end': datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_end_ts': 0,
})
strategy_results = generate_strategy_metrics(stake_currency=stake_currency,
max_open_trades=max_open_trades,
all_results=all_results)
@ -273,7 +352,7 @@ def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_curren
output = [[
t['sell_reason'], t['trades'], t['wins'], t['draws'], t['losses'],
t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'], t['profit_pct_total'],
t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'], t['profit_total_pct'],
] for t in sell_reason_stats]
return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
@ -298,6 +377,35 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
def text_table_add_metrics(strat_results: Dict) -> str:
if len(strat_results['trades']) > 0:
min_trade = min(strat_results['trades'], key=lambda x: x['open_date'])
metrics = [
('Backtesting from', strat_results['backtest_start'].strftime(DATETIME_PRINT_FORMAT)),
('Backtesting to', strat_results['backtest_end'].strftime(DATETIME_PRINT_FORMAT)),
('Total trades', strat_results['total_trades']),
('First trade', min_trade['open_date'].strftime(DATETIME_PRINT_FORMAT)),
('First trade Pair', min_trade['pair']),
('Total Profit %', f"{round(strat_results['profit_total'] * 100, 2)}%"),
('Trades per day', strat_results['trades_per_day']),
('Best day', f"{round(strat_results['backtest_best_day'] * 100, 2)}%"),
('Worst day', f"{round(strat_results['backtest_worst_day'] * 100, 2)}%"),
('Days win/draw/lose', f"{strat_results['winning_days']} / "
f"{strat_results['draw_days']} / {strat_results['losing_days']}"),
('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),
('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"),
('', ''), # Empty line to improve readability
('Max Drawdown', f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
('Drawdown Start', strat_results['drawdown_start'].strftime(DATETIME_PRINT_FORMAT)),
('Drawdown End', strat_results['drawdown_end'].strftime(DATETIME_PRINT_FORMAT)),
('Market change', f"{round(strat_results['market_change'] * 100, 2)}%"),
]
return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl")
else:
return ''
def show_backtest_results(config: Dict, backtest_stats: Dict):
stake_currency = config['stake_currency']
@ -312,15 +420,21 @@ def show_backtest_results(config: Dict, backtest_stats: Dict):
table = text_table_sell_reason(sell_reason_stats=results['sell_reason_summary'],
stake_currency=stake_currency)
if isinstance(table, str):
if isinstance(table, str) and len(table) > 0:
print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency)
if isinstance(table, str):
if isinstance(table, str) and len(table) > 0:
print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
if isinstance(table, str):
table = text_table_add_metrics(results)
if isinstance(table, str) and len(table) > 0:
print(' SUMMARY METRICS '.center(len(table.splitlines()[0]), '='))
print(table)
if isinstance(table, str) and len(table) > 0:
print('=' * len(table.splitlines()[0]))
print()

5
freqtrade/pairlist/AgeFilter.py
View File

@ -26,12 +26,11 @@ class AgeFilter(IPairList):
self._min_days_listed = pairlistconfig.get('min_days_listed', 10)
if self._min_days_listed < 1:
raise OperationalException("AgeFilter requires min_days_listed must be >= 1")
raise OperationalException("AgeFilter requires min_days_listed to be >= 1")
if self._min_days_listed > exchange.ohlcv_candle_limit:
raise OperationalException("AgeFilter requires min_days_listed must not exceed "
raise OperationalException("AgeFilter requires min_days_listed to not exceed "
"exchange max request size "
f"({exchange.ohlcv_candle_limit})")
self._enabled = self._min_days_listed >= 1
@property
def needstickers(self) -> bool:

5
freqtrade/pairlist/IPairList.py
View File

@ -162,6 +162,11 @@ class IPairList(ABC):
f"{self._exchange.name}. Removing it from whitelist..")
continue
if not self._exchange.market_is_tradable(markets[pair]):
logger.warning(f"Pair {pair} is not tradable with Freqtrade."
"Removing it from whitelist..")
continue
if self._exchange.get_pair_quote_currency(pair) != self._config['stake_currency']:
logger.warning(f"Pair {pair} is not compatible with your stake currency "
f"{self._config['stake_currency']}. Removing it from whitelist..")

13
freqtrade/pairlist/PriceFilter.py
View File

@ -4,6 +4,7 @@ Price pair list filter
import logging
from typing import Any, Dict
from freqtrade.exceptions import OperationalException
from freqtrade.pairlist.IPairList import IPairList
@ -18,11 +19,17 @@ class PriceFilter(IPairList):
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
self._low_price_ratio = pairlistconfig.get('low_price_ratio', 0)
if self._low_price_ratio < 0:
raise OperationalException("PriceFilter requires low_price_ratio to be >= 0")
self._min_price = pairlistconfig.get('min_price', 0)
if self._min_price < 0:
raise OperationalException("PriceFilter requires min_price to be >= 0")
self._max_price = pairlistconfig.get('max_price', 0)
self._enabled = ((self._low_price_ratio != 0) or
(self._min_price != 0) or
(self._max_price != 0))
if self._max_price < 0:
raise OperationalException("PriceFilter requires max_price to be >= 0")
self._enabled = ((self._low_price_ratio > 0) or
(self._min_price > 0) or
(self._max_price > 0))
@property
def needstickers(self) -> bool:

7
freqtrade/pairlist/VolumePairList.py
View File

@ -14,7 +14,7 @@ from freqtrade.pairlist.IPairList import IPairList
logger = logging.getLogger(__name__)
SORT_VALUES = ['askVolume', 'bidVolume', 'quoteVolume']
SORT_VALUES = ['quoteVolume']
class VolumePairList(IPairList):
@ -45,11 +45,6 @@ class VolumePairList(IPairList):
raise OperationalException(
f'key {self._sort_key} not in {SORT_VALUES}')
if self._sort_key != 'quoteVolume':
logger.warning(
"DEPRECATED: using any key other than quoteVolume for VolumePairList is deprecated."
)
@property
def needstickers(self) -> bool:
"""

31
freqtrade/persistence.py
View File

@ -2,7 +2,7 @@
This module contains the class to persist trades into SQLite
"""
import logging
from datetime import datetime
from datetime import datetime, timezone
from decimal import Decimal
from typing import Any, Dict, List, Optional
@ -17,6 +17,7 @@ from sqlalchemy.orm.session import sessionmaker
from sqlalchemy.pool import StaticPool
from freqtrade.exceptions import OperationalException
from freqtrade.misc import safe_value_fallback
logger = logging.getLogger(__name__)
@ -86,7 +87,7 @@ def check_migrate(engine) -> None:
logger.debug(f'trying {table_back_name}')
# Check for latest column
if not has_column(cols, 'timeframe'):
if not has_column(cols, 'amount_requested'):
logger.info(f'Running database migration - backup available as {table_back_name}')
fee_open = get_column_def(cols, 'fee_open', 'fee')
@ -119,6 +120,7 @@ def check_migrate(engine) -> None:
cols, 'close_profit_abs',
f"(amount * close_rate * (1 - {fee_close})) - {open_trade_price}")
sell_order_status = get_column_def(cols, 'sell_order_status', 'null')
amount_requested = get_column_def(cols, 'amount_requested', 'amount')
# Schema migration necessary
engine.execute(f"alter table trades rename to {table_back_name}")
@ -134,7 +136,7 @@ def check_migrate(engine) -> None:
fee_open, fee_open_cost, fee_open_currency,
fee_close, fee_close_cost, fee_open_currency, open_rate,
open_rate_requested, close_rate, close_rate_requested, close_profit,
stake_amount, amount, open_date, close_date, open_order_id,
stake_amount, amount, amount_requested, open_date, close_date, open_order_id,
stop_loss, stop_loss_pct, initial_stop_loss, initial_stop_loss_pct,
stoploss_order_id, stoploss_last_update,
max_rate, min_rate, sell_reason, sell_order_status, strategy,
@ -153,7 +155,7 @@ def check_migrate(engine) -> None:
{fee_close_cost} fee_close_cost, {fee_close_currency} fee_close_currency,
open_rate, {open_rate_requested} open_rate_requested, close_rate,
{close_rate_requested} close_rate_requested, close_profit,
stake_amount, amount, open_date, close_date, open_order_id,
stake_amount, amount, {amount_requested}, open_date, close_date, open_order_id,
{stop_loss} stop_loss, {stop_loss_pct} stop_loss_pct,
{initial_stop_loss} initial_stop_loss,
{initial_stop_loss_pct} initial_stop_loss_pct,
@ -215,6 +217,7 @@ class Trade(_DECL_BASE):
close_profit_abs = Column(Float)
stake_amount = Column(Float, nullable=False)
amount = Column(Float)
amount_requested = Column(Float)
open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
close_date = Column(DateTime)
open_order_id = Column(String)
@ -256,6 +259,7 @@ class Trade(_DECL_BASE):
'is_open': self.is_open,
'exchange': self.exchange,
'amount': round(self.amount, 8),
'amount_requested': round(self.amount_requested, 8) if self.amount_requested else None,
'stake_amount': round(self.stake_amount, 8),
'strategy': self.strategy,
'ticker_interval': self.timeframe, # DEPRECATED
@ -270,16 +274,17 @@ class Trade(_DECL_BASE):
'open_date_hum': arrow.get(self.open_date).humanize(),
'open_date': self.open_date.strftime("%Y-%m-%d %H:%M:%S"),
'open_timestamp': int(self.open_date.timestamp() * 1000),
'open_timestamp': int(self.open_date.replace(tzinfo=timezone.utc).timestamp() * 1000),
'open_rate': self.open_rate,
'open_rate_requested': self.open_rate_requested,
'open_trade_price': self.open_trade_price,
'open_trade_price': round(self.open_trade_price, 8),
'close_date_hum': (arrow.get(self.close_date).humanize()
if self.close_date else None),
'close_date': (self.close_date.strftime("%Y-%m-%d %H:%M:%S")
if self.close_date else None),
'close_timestamp': int(self.close_date.timestamp() * 1000) if self.close_date else None,
'close_timestamp': int(self.close_date.replace(
tzinfo=timezone.utc).timestamp() * 1000) if self.close_date else None,
'close_rate': self.close_rate,
'close_rate_requested': self.close_rate_requested,
'close_profit': self.close_profit,
@ -294,8 +299,8 @@ class Trade(_DECL_BASE):
'stoploss_order_id': self.stoploss_order_id,
'stoploss_last_update': (self.stoploss_last_update.strftime("%Y-%m-%d %H:%M:%S")
if self.stoploss_last_update else None),
'stoploss_last_update_timestamp': (int(self.stoploss_last_update.timestamp() * 1000)
if self.stoploss_last_update else None),
'stoploss_last_update_timestamp': int(self.stoploss_last_update.replace(
tzinfo=timezone.utc).timestamp() * 1000) if self.stoploss_last_update else None,
'initial_stop_loss': self.initial_stop_loss, # Deprecated - should not be used
'initial_stop_loss_abs': self.initial_stop_loss,
'initial_stop_loss_ratio': (self.initial_stop_loss_pct
@ -365,20 +370,20 @@ class Trade(_DECL_BASE):
"""
order_type = order['type']
# Ignore open and cancelled orders
if order['status'] == 'open' or order['price'] is None:
if order['status'] == 'open' or safe_value_fallback(order, 'average', 'price') is None:
return
logger.info('Updating trade (id=%s) ...', self.id)
if order_type in ('market', 'limit') and order['side'] == 'buy':
# Update open rate and actual amount
self.open_rate = Decimal(order['price'])
self.amount = Decimal(order.get('filled', order['amount']))
self.open_rate = Decimal(safe_value_fallback(order, 'average', 'price'))
self.amount = Decimal(safe_value_fallback(order, 'filled', 'amount'))
self.recalc_open_trade_price()
logger.info('%s_BUY has been fulfilled for %s.', order_type.upper(), self)
self.open_order_id = None
elif order_type in ('market', 'limit') and order['side'] == 'sell':
self.close(order['price'])
self.close(safe_value_fallback(order, 'average', 'price'))
logger.info('%s_SELL has been fulfilled for %s.', order_type.upper(), self)
elif order_type in ('stop_loss_limit', 'stop-loss', 'stop'):
self.stoploss_order_id = None

45
freqtrade/plot/plotting.py
View File

@ -8,7 +8,8 @@ from freqtrade.configuration import TimeRange
from freqtrade.data.btanalysis import (calculate_max_drawdown,
combine_dataframes_with_mean,
create_cum_profit,
extract_trades_of_period, load_trades)
extract_trades_of_period,
load_trades)
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.dataprovider import DataProvider
from freqtrade.data.history import load_data
@ -37,15 +38,15 @@ def init_plotscript(config):
"""
if "pairs" in config:
pairs = config["pairs"]
pairs = config['pairs']
else:
pairs = config["exchange"]["pair_whitelist"]
pairs = config['exchange']['pair_whitelist']
# Set timerange to use
timerange = TimeRange.parse_timerange(config.get("timerange"))
timerange = TimeRange.parse_timerange(config.get('timerange'))
data = load_data(
datadir=config.get("datadir"),
datadir=config.get('datadir'),
pairs=pairs,
timeframe=config.get('timeframe', '5m'),
timerange=timerange,
@ -53,19 +54,22 @@ def init_plotscript(config):
)
no_trades = False
filename = config.get('exportfilename')
if config.get('no_trades', False):
no_trades = True
elif not config['exportfilename'].is_file() and config['trade_source'] == 'file':
elif config['trade_source'] == 'file':
if not filename.is_dir() and not filename.is_file():
logger.warning("Backtest file is missing skipping trades.")
no_trades = True
trades = load_trades(
config['trade_source'],
db_url=config.get('db_url'),
exportfilename=config.get('exportfilename'),
no_trades=no_trades
exportfilename=filename,
no_trades=no_trades,
strategy=config.get('strategy'),
)
trades = trim_dataframe(trades, timerange, 'open_time')
trades = trim_dataframe(trades, timerange, 'open_date')
return {"ohlcv": data,
"trades": trades,
@ -165,10 +169,11 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
if trades is not None and len(trades) > 0:
# Create description for sell summarizing the trade
trades['desc'] = trades.apply(lambda row: f"{round(row['profit_percent'] * 100, 1)}%, "
f"{row['sell_reason']}, {row['duration']} min",
f"{row['sell_reason']}, "
f"{row['trade_duration']} min",
axis=1)
trade_buys = go.Scatter(
x=trades["open_time"],
x=trades["open_date"],
y=trades["open_rate"],
mode='markers',
name='Trade buy',
@ -183,7 +188,7 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
)
trade_sells = go.Scatter(
x=trades.loc[trades['profit_percent'] > 0, "close_time"],
x=trades.loc[trades['profit_percent'] > 0, "close_date"],
y=trades.loc[trades['profit_percent'] > 0, "close_rate"],
text=trades.loc[trades['profit_percent'] > 0, "desc"],
mode='markers',
@ -196,7 +201,7 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
)
)
trade_sells_loss = go.Scatter(
x=trades.loc[trades['profit_percent'] <= 0, "close_time"],
x=trades.loc[trades['profit_percent'] <= 0, "close_date"],
y=trades.loc[trades['profit_percent'] <= 0, "close_rate"],
text=trades.loc[trades['profit_percent'] <= 0, "desc"],
mode='markers',
@ -486,13 +491,13 @@ def load_and_plot_trades(config: Dict[str, Any]):
pair=pair,
data=df_analyzed,
trades=trades_pair,
indicators1=config.get("indicators1", []),
indicators2=config.get("indicators2", []),
indicators1=config.get('indicators1', []),
indicators2=config.get('indicators2', []),
plot_config=strategy.plot_config if hasattr(strategy, 'plot_config') else {}
)
store_plot_file(fig, filename=generate_plot_filename(pair, config['timeframe']),
directory=config['user_data_dir'] / "plot")
directory=config['user_data_dir'] / 'plot')
logger.info('End of plotting process. %s plots generated', pair_counter)
@ -509,8 +514,8 @@ def plot_profit(config: Dict[str, Any]) -> None:
# Filter trades to relevant pairs
# Remove open pairs - we don't know the profit yet so can't calculate profit for these.
# Also, If only one open pair is left, then the profit-generation would fail.
trades = trades[(trades['pair'].isin(plot_elements["pairs"]))
& (~trades['close_time'].isnull())
trades = trades[(trades['pair'].isin(plot_elements['pairs']))
& (~trades['close_date'].isnull())
]
if len(trades) == 0:
raise OperationalException("No trades found, cannot generate Profit-plot without "
@ -518,7 +523,7 @@ def plot_profit(config: Dict[str, Any]) -> None:
# Create an average close price of all the pairs that were involved.
# this could be useful to gauge the overall market trend
fig = generate_profit_graph(plot_elements["pairs"], plot_elements["ohlcv"],
fig = generate_profit_graph(plot_elements['pairs'], plot_elements['ohlcv'],
trades, config.get('timeframe', '5m'))
store_plot_file(fig, filename='freqtrade-profit-plot.html',
directory=config['user_data_dir'] / "plot", auto_open=True)
directory=config['user_data_dir'] / 'plot', auto_open=True)

2
freqtrade/resolvers/hyperopt_resolver.py
View File

@ -23,7 +23,7 @@ class HyperOptResolver(IResolver):
object_type = IHyperOpt
object_type_str = "Hyperopt"
user_subdir = USERPATH_HYPEROPTS
initial_search_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
initial_search_path = None
@staticmethod
def load_hyperopt(config: Dict) -> IHyperOpt:

2
freqtrade/resolvers/iresolver.py
View File

@ -59,7 +59,7 @@ class IResolver:
module = importlib.util.module_from_spec(spec)
try:
spec.loader.exec_module(module) # type: ignore # importlib does not use typehints
except (ModuleNotFoundError, SyntaxError) as err:
except (ModuleNotFoundError, SyntaxError, ImportError) as err:
# Catch errors in case a specific module is not installed
logger.warning(f"Could not import {module_path} due to '{err}'")
if enum_failed:

26
freqtrade/rpc/api_server.py
View File

@ -16,8 +16,10 @@ from werkzeug.security import safe_str_cmp
from werkzeug.serving import make_server
from freqtrade.__init__ import __version__
from freqtrade.rpc.rpc import RPC, RPCException
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.persistence import Trade
from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
from freqtrade.rpc.rpc import RPC, RPCException
logger = logging.getLogger(__name__)
@ -32,7 +34,7 @@ class ArrowJSONEncoder(JSONEncoder):
elif isinstance(obj, date):
return obj.strftime("%Y-%m-%d")
elif isinstance(obj, datetime):
return obj.strftime("%Y-%m-%d %H:%M:%S")
return obj.strftime(DATETIME_PRINT_FORMAT)
iterable = iter(obj)
except TypeError:
pass
@ -69,6 +71,11 @@ def rpc_catch_errors(func: Callable[..., Any]):
return func_wrapper
def shutdown_session(exception=None):
# Remove scoped session
Trade.session.remove()
class ApiServer(RPC):
"""
This class runs api server and provides rpc.rpc functionality to it
@ -103,6 +110,8 @@ class ApiServer(RPC):
self.jwt = JWTManager(self.app)
self.app.json_encoder = ArrowJSONEncoder
self.app.teardown_appcontext(shutdown_session)
# Register application handling
self.register_rest_rpc_urls()
@ -186,6 +195,7 @@ class ApiServer(RPC):
self.app.add_url_rule(f'{BASE_URI}/count', 'count', view_func=self._count, methods=['GET'])
self.app.add_url_rule(f'{BASE_URI}/daily', 'daily', view_func=self._daily, methods=['GET'])
self.app.add_url_rule(f'{BASE_URI}/edge', 'edge', view_func=self._edge, methods=['GET'])
self.app.add_url_rule(f'{BASE_URI}/logs', 'log', view_func=self._get_logs, methods=['GET'])
self.app.add_url_rule(f'{BASE_URI}/profit', 'profit',
view_func=self._profit, methods=['GET'])
self.app.add_url_rule(f'{BASE_URI}/performance', 'performance',
@ -348,6 +358,18 @@ class ApiServer(RPC):
return self.rest_dump(stats)
@require_login
@rpc_catch_errors
def _get_logs(self):
"""
Returns latest logs
get:
param:
limit: Only get a certain number of records
"""
limit = int(request.args.get('limit', 0)) or None
return self.rest_dump(self._rpc_get_logs(limit))
@require_login
@rpc_catch_errors
def _edge(self):

66
freqtrade/rpc/rpc.py
View File

@ -11,9 +11,10 @@ from typing import Any, Dict, List, Optional, Tuple, Union
import arrow
from numpy import NAN, mean
from freqtrade.exceptions import (ExchangeError, InvalidOrderException,
PricingError)
from freqtrade.constants import CANCEL_REASON
from freqtrade.exceptions import ExchangeError, PricingError
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_msecs
from freqtrade.loggers import bufferHandler
from freqtrade.misc import shorten_date
from freqtrade.persistence import Trade
from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
@ -158,6 +159,7 @@ class RPC:
current_profit_abs=current_profit_abs,
stoploss_current_dist=stoploss_current_dist,
stoploss_current_dist_ratio=round(stoploss_current_dist_ratio, 8),
stoploss_current_dist_pct=round(stoploss_current_dist_ratio * 100, 2),
stoploss_entry_dist=stoploss_entry_dist,
stoploss_entry_dist_ratio=round(stoploss_entry_dist_ratio, 8),
open_order='({} {} rem={:.8f})'.format(
@ -222,24 +224,23 @@ class RPC:
Trade.close_date >= profitday,
Trade.close_date < (profitday + timedelta(days=1))
]).order_by(Trade.close_date).all()
curdayprofit = sum(trade.close_profit_abs for trade in trades)
curdayprofit = sum(
trade.close_profit_abs for trade in trades if trade.close_profit_abs is not None)
profit_days[profitday] = {
'amount': f'{curdayprofit:.8f}',
'amount': curdayprofit,
'trades': len(trades)
}
data = [
{
'date': key,
'abs_profit': f'{float(value["amount"]):.8f}',
'fiat_value': '{value:.3f}'.format(
value=self._fiat_converter.convert_amount(
'abs_profit': value["amount"],
'fiat_value': self._fiat_converter.convert_amount(
value['amount'],
stake_currency,
fiat_display_currency
) if self._fiat_converter else 0,
),
'trade_count': f'{value["trades"]}',
'trade_count': value["trades"],
}
for key, value in profit_days.items()
]
@ -435,7 +436,7 @@ class RPC:
def _rpc_reload_config(self) -> Dict[str, str]:
""" Handler for reload_config. """
self._freqtrade.state = State.RELOAD_CONFIG
return {'status': 'reloading config ...'}
return {'status': 'Reloading config ...'}
def _rpc_stopbuy(self) -> Dict[str, str]:
"""
@ -454,26 +455,19 @@ class RPC:
"""
def _exec_forcesell(trade: Trade) -> None:
# Check if there is there is an open order
fully_canceled = False
if trade.open_order_id:
order = self._freqtrade.exchange.fetch_order(trade.open_order_id, trade.pair)
# Cancel open LIMIT_BUY orders and close trade
if order and order['status'] == 'open' \
and order['type'] == 'limit' \
and order['side'] == 'buy':
self._freqtrade.exchange.cancel_order(trade.open_order_id, trade.pair)
trade.close(order.get('price') or trade.open_rate)
# Do the best effort, if we don't know 'filled' amount, don't try selling
if order['filled'] is None:
return
trade.amount = order['filled']
if order['side'] == 'buy':
fully_canceled = self._freqtrade.handle_cancel_buy(
trade, order, CANCEL_REASON['FORCE_SELL'])
# Ignore trades with an attached LIMIT_SELL order
if order and order['status'] == 'open' \
and order['type'] == 'limit' \
and order['side'] == 'sell':
return
if order['side'] == 'sell':
# Cancel order - so it is placed anew with a fresh price.
self._freqtrade.handle_cancel_sell(trade, order, CANCEL_REASON['FORCE_SELL'])
if not fully_canceled:
# Get current rate and execute sell
current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
self._freqtrade.execute_sell(trade, current_rate, SellType.FORCE_SELL)
@ -555,7 +549,7 @@ class RPC:
try:
self._freqtrade.exchange.cancel_order(trade.open_order_id, trade.pair)
c_count += 1
except (ExchangeError, InvalidOrderException):
except (ExchangeError):
pass
# cancel stoploss on exchange ...
@ -565,7 +559,7 @@ class RPC:
self._freqtrade.exchange.cancel_stoploss_order(trade.stoploss_order_id,
trade.pair)
c_count += 1
except (ExchangeError, InvalidOrderException):
except (ExchangeError):
pass
Trade.session.delete(trade)
@ -633,6 +627,24 @@ class RPC:
}
return res
def _rpc_get_logs(self, limit: Optional[int]) -> Dict[str, Any]:
"""Returns the last X logs"""
if limit:
buffer = bufferHandler.buffer[-limit:]
else:
buffer = bufferHandler.buffer
records = [[datetime.fromtimestamp(r.created).strftime("%Y-%m-%d %H:%M:%S"),
r.created * 1000, r.name, r.levelname,
r.message + ('\n' + r.exc_text if r.exc_text else '')]
for r in buffer]
# Log format:
# [logtime-formatted, logepoch, logger-name, loglevel, message \n + exception]
# e.g. ["2020-08-27 11:35:01", 1598520901097.9397,
# "freqtrade.worker", "INFO", "Starting worker develop"]
return {'log_count': len(records), 'logs': records}
def _rpc_edge(self) -> List[Dict[str, Any]]:
""" Returns information related to Edge """
if not self._freqtrade.edge:

58
freqtrade/rpc/telegram.py
View File

@ -12,6 +12,7 @@ from tabulate import tabulate
from telegram import ParseMode, ReplyKeyboardMarkup, Update
from telegram.error import NetworkError, TelegramError
from telegram.ext import CallbackContext, CommandHandler, Updater
from telegram.utils.helpers import escape_markdown
from freqtrade.__init__ import __version__
from freqtrade.rpc import RPC, RPCException, RPCMessageType
@ -103,6 +104,7 @@ class Telegram(RPC):
CommandHandler('stopbuy', self._stopbuy),
CommandHandler('whitelist', self._whitelist),
CommandHandler('blacklist', self._blacklist),
CommandHandler('logs', self._logs),
CommandHandler('edge', self._edge),
CommandHandler('help', self._help),
CommandHandler('version', self._version),
@ -149,7 +151,7 @@ class Telegram(RPC):
elif msg['type'] == RPCMessageType.BUY_CANCEL_NOTIFICATION:
message = ("\N{WARNING SIGN} *{exchange}:* "
"Cancelling Open Buy Order for {pair}".format(**msg))
"Cancelling open buy Order for {pair}. Reason: {reason}.".format(**msg))
elif msg['type'] == RPCMessageType.SELL_NOTIFICATION:
msg['amount'] = round(msg['amount'], 8)
@ -239,17 +241,18 @@ class Telegram(RPC):
("*Close Profit:* `{close_profit_pct}`"
if r['close_profit_pct'] is not None else ""),
"*Current Profit:* `{current_profit_pct:.2f}%`",
]
if (r['stop_loss'] != r['initial_stop_loss']
and r['initial_stop_loss_pct'] is not None):
# Adding initial stoploss only if it is different from stoploss
"*Initial Stoploss:* `{initial_stop_loss:.8f}` " +
("`({initial_stop_loss_pct:.2f}%)`") if (
r['stop_loss'] != r['initial_stop_loss']
and r['initial_stop_loss_pct'] is not None) else "",
lines.append("*Initial Stoploss:* `{initial_stop_loss:.8f}` "
"`({initial_stop_loss_pct:.2f}%)`")
# Adding stoploss and stoploss percentage only if it is not None
"*Stoploss:* `{stop_loss:.8f}` " +
("`({stop_loss_pct:.2f}%)`" if r['stop_loss_pct'] else ""),
]
lines.append("*Stoploss:* `{stop_loss:.8f}` " +
("`({stop_loss_pct:.2f}%)`" if r['stop_loss_pct'] else ""))
lines.append("*Stoploss distance:* `{stoploss_current_dist:.8f}` "
"`({stoploss_current_dist_pct:.2f}%)`")
if r['open_order']:
if r['sell_order_status']:
lines.append("*Open Order:* `{open_order}` - `{sell_order_status}`")
@ -305,8 +308,8 @@ class Telegram(RPC):
)
stats_tab = tabulate(
[[day['date'],
f"{day['abs_profit']} {stats['stake_currency']}",
f"{day['fiat_value']} {stats['fiat_display_currency']}",
f"{day['abs_profit']:.8f} {stats['stake_currency']}",
f"{day['fiat_value']:.3f} {stats['fiat_display_currency']}",
f"{day['trade_count']} trades"] for day in stats['data']],
headers=[
'Day',
@ -637,6 +640,38 @@ class Telegram(RPC):
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _logs(self, update: Update, context: CallbackContext) -> None:
"""
Handler for /logs
Shows the latest logs
"""
try:
try:
limit = int(context.args[0])
except (TypeError, ValueError, IndexError):
limit = 10
logs = self._rpc_get_logs(limit)['logs']
msgs = ''
msg_template = "*{}* {}: {} \\- `{}`"
for logrec in logs:
msg = msg_template.format(escape_markdown(logrec[0], version=2),
escape_markdown(logrec[2], version=2),
escape_markdown(logrec[3], version=2),
escape_markdown(logrec[4], version=2))
if len(msgs + msg) + 10 >= MAX_TELEGRAM_MESSAGE_LENGTH:
# Send message immediately if it would become too long
self._send_msg(msgs, parse_mode=ParseMode.MARKDOWN_V2)
msgs = msg + '\n'
else:
# Append message to messages to send
msgs += msg + '\n'
if msgs:
self._send_msg(msgs, parse_mode=ParseMode.MARKDOWN_V2)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _edge(self, update: Update, context: CallbackContext) -> None:
"""
@ -682,6 +717,7 @@ class Telegram(RPC):
"*/stopbuy:* `Stops buying, but handles open trades gracefully` \n"
"*/reload_config:* `Reload configuration file` \n"
"*/show_config:* `Show running configuration` \n"
"*/logs [limit]:* `Show latest logs - defaults to 10` \n"
"*/whitelist:* `Show current whitelist` \n"
"*/blacklist [pair]:* `Show current blacklist, or adds one or more pairs "
"to the blacklist.` \n"

6
freqtrade/strategy/__init__.py
View File

@ -1 +1,5 @@
from freqtrade.strategy.interface import IStrategy # noqa: F401
# flake8: noqa: F401
from freqtrade.exchange import (timeframe_to_minutes, timeframe_to_prev_date,
timeframe_to_seconds, timeframe_to_next_date, timeframe_to_msecs)
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy.strategy_helper import merge_informative_pair

23
freqtrade/strategy/interface.py
View File

@ -14,8 +14,9 @@ from pandas import DataFrame
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exceptions import StrategyError, OperationalException
from freqtrade.exceptions import OperationalException, StrategyError
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.exchange.exchange import timeframe_to_next_date
from freqtrade.persistence import Trade
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.wallets import Wallets
@ -44,6 +45,10 @@ class SellType(Enum):
EMERGENCY_SELL = "emergency_sell"
NONE = ""
def __str__(self):
# explicitly convert to String to help with exporting data.
return self.value
class SellCheckTuple(NamedTuple):
"""
@ -293,13 +298,25 @@ class IStrategy(ABC):
if pair in self._pair_locked_until:
del self._pair_locked_until[pair]
def is_pair_locked(self, pair: str) -> bool:
def is_pair_locked(self, pair: str, candle_date: datetime = None) -> bool:
"""
Checks if a pair is currently locked
The 2nd, optional parameter ensures that locks are applied until the new candle arrives,
and not stop at 14:00:00 - while the next candle arrives at 14:00:02 leaving a gap
of 2 seconds for a buy to happen on an old signal.
:param: pair: "Pair to check"
:param candle_date: Date of the last candle. Optional, defaults to current date
:returns: locking state of the pair in question.
"""
if pair not in self._pair_locked_until:
return False
if not candle_date:
return self._pair_locked_until[pair] >= datetime.now(timezone.utc)
else:
# Locking should happen until a new candle arrives
lock_time = timeframe_to_next_date(self.timeframe, candle_date)
# lock_time = candle_date + timedelta(minutes=timeframe_to_minutes(self.timeframe))
return self._pair_locked_until[pair] > lock_time
def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
@ -430,7 +447,7 @@ class IStrategy(ABC):
if latest_date < (arrow.utcnow().shift(minutes=-(timeframe_minutes * 2 + offset))):
logger.warning(
'Outdated history for pair %s. Last tick is %s minutes old',
pair, (arrow.utcnow() - latest_date).seconds // 60
pair, int((arrow.utcnow() - latest_date).total_seconds() // 60)
)
return False, False

48
freqtrade/strategy/strategy_helper.py Normal file
View File

@ -0,0 +1,48 @@
import pandas as pd
from freqtrade.exchange import timeframe_to_minutes
def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
timeframe: str, timeframe_inf: str, ffill: bool = True) -> pd.DataFrame:
"""
Correctly merge informative samples to the original dataframe, avoiding lookahead bias.
Since dates are candle open dates, merging a 15m candle that starts at 15:00, and a
1h candle that starts at 15:00 will result in all candles to know the close at 16:00
which they should not know.
Moves the date of the informative pair by 1 time interval forward.
This way, the 14:00 1h candle is merged to 15:00 15m candle, since the 14:00 1h candle is the
last candle that's closed at 15:00, 15:15, 15:30 or 15:45.
Assuming inf_tf = '1d' - then the resulting columns will be:
date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d
:param dataframe: Original dataframe
:param informative: Informative pair, most likely loaded via dp.get_pair_dataframe
:param timeframe: Timeframe of the original pair sample.
:param timeframe_inf: Timeframe of the informative pair sample.
:param ffill: Forwardfill missing values - optional but usually required
"""
minutes_inf = timeframe_to_minutes(timeframe_inf)
minutes = timeframe_to_minutes(timeframe)
if minutes >= minutes_inf:
# No need to forwardshift if the timeframes are identical
informative['date_merge'] = informative["date"]
else:
informative['date_merge'] = informative["date"] + pd.to_timedelta(minutes_inf, 'm')
# Rename columns to be unique
informative.columns = [f"{col}_{timeframe_inf}" for col in informative.columns]
# Combine the 2 dataframes
# all indicators on the informative sample MUST be calculated before this point
dataframe = pd.merge(dataframe, informative, left_on='date',
right_on=f'date_merge_{timeframe_inf}', how='left')
dataframe = dataframe.drop(f'date_merge_{timeframe_inf}', axis=1)
if ffill:
dataframe = dataframe.ffill()
return dataframe

51
freqtrade/templates/strategy_analysis_example.ipynb
View File

@ -34,7 +34,7 @@
"# config = Configuration.from_files([\"config.json\"])\n",
"\n",
"# Define some constants\n",
"config[\"ticker_interval\"] = \"5m\"\n",
"config[\"timeframe\"] = \"5m\"\n",
"# Name of the strategy class\n",
"config[\"strategy\"] = \"SampleStrategy\"\n",
"# Location of the data\n",
@ -53,7 +53,7 @@
"from freqtrade.data.history import load_pair_history\n",
"\n",
"candles = load_pair_history(datadir=data_location,\n",
" timeframe=config[\"ticker_interval\"],\n",
" timeframe=config[\"timeframe\"],\n",
" pair=pair)\n",
"\n",
"# Confirm success\n",
@ -136,10 +136,51 @@
"metadata": {},
"outputs": [],
"source": [
"from freqtrade.data.btanalysis import load_backtest_data\n",
"from freqtrade.data.btanalysis import load_backtest_data, load_backtest_stats\n",
"\n",
"# Load backtest results\n",
"trades = load_backtest_data(config[\"user_data_dir\"] / \"backtest_results/backtest-result.json\")\n",
"# if backtest_dir points to a directory, it'll automatically load the last backtest file.\n",
"backtest_dir = config[\"user_data_dir\"] / \"backtest_results\"\n",
"# backtest_dir can also point to a specific file \n",
"# backtest_dir = config[\"user_data_dir\"] / \"backtest_results/backtest-result-2020-07-01_20-04-22.json\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# You can get the full backtest statistics by using the following command.\n",
"# This contains all information used to generate the backtest result.\n",
"stats = load_backtest_stats(backtest_dir)\n",
"\n",
"strategy = 'SampleStrategy'\n",
"# All statistics are available per strategy, so if `--strategy-list` was used during backtest, this will be reflected here as well.\n",
"# Example usages:\n",
"print(stats['strategy'][strategy]['results_per_pair'])\n",
"# Get pairlist used for this backtest\n",
"print(stats['strategy'][strategy]['pairlist'])\n",
"# Get market change (average change of all pairs from start to end of the backtest period)\n",
"print(stats['strategy'][strategy]['market_change'])\n",
"# Maximum drawdown ()\n",
"print(stats['strategy'][strategy]['max_drawdown'])\n",
"# Maximum drawdown start and end\n",
"print(stats['strategy'][strategy]['drawdown_start'])\n",
"print(stats['strategy'][strategy]['drawdown_end'])\n",
"\n",
"\n",
"# Get strategy comparison (only relevant if multiple strategies were compared)\n",
"print(stats['strategy_comparison'])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load backtested trades as dataframe\n",
"trades = load_backtest_data(backtest_dir)\n",
"\n",
"# Show value-counts per pair\n",
"trades.groupby(\"pair\")[\"sell_reason\"].value_counts()"

2
freqtrade/vendor/qtpylib/indicators.py vendored
View File

@ -222,7 +222,7 @@ def crossed(series1, series2, direction=None):
if isinstance(series1, np.ndarray):
series1 = pd.Series(series1)
if isinstance(series2, (float, int, np.ndarray)):
if isinstance(series2, (float, int, np.ndarray, np.integer, np.floating)):
series2 = pd.Series(index=series1.index, data=series2)
if direction is None or direction == "above":

16
mkdocs.yml
View File

@ -1,8 +1,11 @@
site_name: Freqtrade
nav:
- Home: index.md
- Installation Docker: docker.md
- Installation: installation.md
- Quickstart with Docker: docker_quickstart.md
- Installation:
- Docker without docker-compose: docker.md
- Linux/MacOS/Raspberry: installation.md
- Windows: windows_installation.md
- Freqtrade Basics: bot-basics.md
- Configuration: configuration.md
- Strategy Customization: strategy-customization.md
@ -39,13 +42,19 @@ theme:
accent: 'tear'
extra_css:
- 'stylesheets/ft.extra.css'
extra_javascript:
- javascripts/config.js
- https://polyfill.io/v3/polyfill.min.js?features=es6
- https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js
markdown_extensions:
- admonition
- footnotes
- codehilite:
guess_lang: false
- toc:
permalink: true
- pymdownx.arithmatex
- pymdownx.arithmatex:
generic: true
- pymdownx.caret
- pymdownx.critic
- pymdownx.details
@ -53,6 +62,7 @@ markdown_extensions:
- pymdownx.magiclink
- pymdownx.mark
- pymdownx.smartsymbols
- pymdownx.tabbed
- pymdownx.superfences
- pymdownx.tasklist:
custom_checkbox: true

12
requirements-common.txt
View File

@ -1,9 +1,9 @@
# requirements without requirements installable via conda
# mainly used for Raspberry pi installs
ccxt==1.32.88
SQLAlchemy==1.3.18
ccxt==1.34.11
SQLAlchemy==1.3.19
python-telegram-bot==12.8
arrow==0.15.8
arrow==0.16.0
cachetools==4.1.1
requests==2.24.0
urllib3==1.25.10
@ -13,6 +13,8 @@ TA-Lib==0.4.18
tabulate==0.8.7
pycoingecko==1.3.0
jinja2==2.11.2
tables==3.6.1
blosc==1.9.2
# find first, C search in arrays
py_find_1st==1.1.4
@ -26,10 +28,10 @@ sdnotify==0.3.2
# Api server
flask==1.1.2
flask-jwt-extended==3.24.1
flask-cors==3.0.8
flask-cors==3.0.9
# Support for colorized terminal output
colorama==0.4.3
# Building config files interactively
questionary==1.5.2
prompt-toolkit==3.0.5
prompt-toolkit==3.0.7

6
requirements-dev.txt
View File

@ -3,15 +3,15 @@
-r requirements-plot.txt
-r requirements-hyperopt.txt
coveralls==2.1.1
coveralls==2.1.2
flake8==3.8.3
flake8-type-annotations==0.1.0
flake8-tidy-imports==4.1.0
mypy==0.782
pytest==6.0.1
pytest-asyncio==0.14.0
pytest-cov==2.10.0
pytest-mock==3.2.0
pytest-cov==2.10.1
pytest-mock==3.3.1
pytest-random-order==1.0.4
# Convert jupyter notebooks to markdown documents

6
requirements-hyperopt.txt
View File

@ -3,8 +3,8 @@
# Required for hyperopt
scipy==1.5.2
scikit-learn==0.23.1
scikit-optimize==0.7.4
scikit-learn==0.23.2
scikit-optimize==0.8.1
filelock==3.0.12
joblib==0.16.0
progressbar2==3.51.4
progressbar2==3.53.1

2
requirements.txt
View File

@ -2,4 +2,4 @@
-r requirements-common.txt
numpy==1.19.1
pandas==1.1.0
pandas==1.1.2

18
scripts/rest_client.py
View File

@ -159,6 +159,14 @@ class FtRestClient():
"""
return self._get("show_config")
def logs(self, limit=None):
"""Show latest logs.
:param limit: Limits log messages to the last <limit> logs. No limit to get all the trades.
:return: json object
"""
return self._get("logs", params={"limit": limit} if limit else 0)
def trades(self, limit=None):
"""Return trades history.
@ -276,11 +284,11 @@ def main(args):
print_commands()
sys.exit()
config = load_config(args["config"])
url = config.get("api_server", {}).get("server_url", "127.0.0.1")
port = config.get("api_server", {}).get("listen_port", "8080")
username = config.get("api_server", {}).get("username")
password = config.get("api_server", {}).get("password")
config = load_config(args['config'])
url = config.get('api_server', {}).get('server_url', '127.0.0.1')
port = config.get('api_server', {}).get('listen_port', '8080')
username = config.get('api_server', {}).get('username')
password = config.get('api_server', {}).get('password')
server_url = f"http://{url}:{port}"
client = FtRestClient(server_url, username, password)

2
setup.py
View File

@ -85,6 +85,8 @@ setup(name='freqtrade',
# from requirements.txt
'numpy',
'pandas',
'tables',
'blosc',
],
extras_require={
'api': api,

5
tests/commands/test_commands.py
View File

@ -667,7 +667,7 @@ def test_start_list_hyperopts(mocker, caplog, capsys):
args = [
"list-hyperopts",
"--hyperopt-path",
str(Path(__file__).parent.parent / "optimize"),
str(Path(__file__).parent.parent / "optimize" / "hyperopts"),
"-1"
]
pargs = get_args(args)
@ -683,7 +683,7 @@ def test_start_list_hyperopts(mocker, caplog, capsys):
args = [
"list-hyperopts",
"--hyperopt-path",
str(Path(__file__).parent.parent / "optimize"),
str(Path(__file__).parent.parent / "optimize" / "hyperopts"),
]
pargs = get_args(args)
# pargs['config'] = None
@ -692,7 +692,6 @@ def test_start_list_hyperopts(mocker, caplog, capsys):
assert "TestHyperoptLegacy" not in captured.out
assert "legacy_hyperopt.py" not in captured.out
assert "DefaultHyperOpt" in captured.out
assert "test_hyperopt.py" in captured.out
def test_start_test_pairlist(mocker, caplog, tickers, default_conf, capsys):

14
tests/conftest.py
View File

@ -78,7 +78,7 @@ def patch_exchange(mocker, api_mock=None, id='bittrex', mock_markets=True) -> No
def get_patched_exchange(mocker, config, api_mock=None, id='bittrex',
mock_markets=True) -> Exchange:
patch_exchange(mocker, api_mock, id, mock_markets)
config["exchange"]["name"] = id
config['exchange']['name'] = id
try:
exchange = ExchangeResolver.load_exchange(id, config)
except ImportError:
@ -176,11 +176,13 @@ def create_mock_trades(fee):
pair='ETH/BTC',
stake_amount=0.001,
amount=123.0,
amount_requested=123.0,
fee_open=fee.return_value,
fee_close=fee.return_value,
open_rate=0.123,
exchange='bittrex',
open_order_id='dry_run_buy_12345'
open_order_id='dry_run_buy_12345',
strategy='DefaultStrategy',
)
Trade.session.add(trade)
@ -188,6 +190,7 @@ def create_mock_trades(fee):
pair='ETC/BTC',
stake_amount=0.001,
amount=123.0,
amount_requested=123.0,
fee_open=fee.return_value,
fee_close=fee.return_value,
open_rate=0.123,
@ -195,7 +198,8 @@ def create_mock_trades(fee):
close_profit=0.005,
exchange='bittrex',
is_open=False,
open_order_id='dry_run_sell_12345'
open_order_id='dry_run_sell_12345',
strategy='DefaultStrategy',
)
Trade.session.add(trade)
@ -218,11 +222,13 @@ def create_mock_trades(fee):
pair='ETC/BTC',
stake_amount=0.001,
amount=123.0,
amount_requested=124.0,
fee_open=fee.return_value,
fee_close=fee.return_value,
open_rate=0.123,
exchange='bittrex',
open_order_id='prod_buy_12345'
open_order_id='prod_buy_12345',
strategy='DefaultStrategy',
)
Trade.session.add(trade)

110
tests/data/test_btanalysis.py
View File

@ -6,24 +6,48 @@ from arrow import Arrow
from pandas import DataFrame, DateOffset, Timestamp, to_datetime
from freqtrade.configuration import TimeRange
from freqtrade.constants import LAST_BT_RESULT_FN
from freqtrade.data.btanalysis import (BT_DATA_COLUMNS,
analyze_trade_parallelism,
calculate_market_change,
calculate_max_drawdown,
combine_dataframes_with_mean,
create_cum_profit,
extract_trades_of_period,
get_latest_backtest_filename,
load_backtest_data, load_trades,
load_trades_from_db)
from freqtrade.data.history import load_data, load_pair_history
from freqtrade.optimize.backtesting import BacktestResult
from tests.conftest import create_mock_trades
def test_load_backtest_data(testdatadir):
def test_get_latest_backtest_filename(testdatadir, mocker):
with pytest.raises(ValueError, match=r"Directory .* does not exist\."):
get_latest_backtest_filename(testdatadir / 'does_not_exist')
with pytest.raises(ValueError,
match=r"Directory .* does not seem to contain .*"):
get_latest_backtest_filename(testdatadir.parent)
res = get_latest_backtest_filename(testdatadir)
assert res == 'backtest-result_new.json'
res = get_latest_backtest_filename(str(testdatadir))
assert res == 'backtest-result_new.json'
mocker.patch("freqtrade.data.btanalysis.json_load", return_value={})
with pytest.raises(ValueError, match=r"Invalid '.last_result.json' format."):
get_latest_backtest_filename(testdatadir)
def test_load_backtest_data_old_format(testdatadir):
filename = testdatadir / "backtest-result_test.json"
bt_data = load_backtest_data(filename)
assert isinstance(bt_data, DataFrame)
assert list(bt_data.columns) == BT_DATA_COLUMNS + ["profit"]
assert list(bt_data.columns) == BT_DATA_COLUMNS + ["profit_abs"]
assert len(bt_data) == 179
# Test loading from string (must yield same result)
@ -34,6 +58,49 @@ def test_load_backtest_data(testdatadir):
load_backtest_data(str("filename") + "nofile")
def test_load_backtest_data_new_format(testdatadir):
filename = testdatadir / "backtest-result_new.json"
bt_data = load_backtest_data(filename)
assert isinstance(bt_data, DataFrame)
assert set(bt_data.columns) == set(list(BacktestResult._fields) + ["profit_abs"])
assert len(bt_data) == 179
# Test loading from string (must yield same result)
bt_data2 = load_backtest_data(str(filename))
assert bt_data.equals(bt_data2)
# Test loading from folder (must yield same result)
bt_data3 = load_backtest_data(testdatadir)
assert bt_data.equals(bt_data3)
with pytest.raises(ValueError, match=r"File .* does not exist\."):
load_backtest_data(str("filename") + "nofile")
with pytest.raises(ValueError, match=r"Unknown dataformat."):
load_backtest_data(testdatadir / LAST_BT_RESULT_FN)
def test_load_backtest_data_multi(testdatadir):
filename = testdatadir / "backtest-result_multistrat.json"
for strategy in ('DefaultStrategy', 'TestStrategy'):
bt_data = load_backtest_data(filename, strategy=strategy)
assert isinstance(bt_data, DataFrame)
assert set(bt_data.columns) == set(list(BacktestResult._fields) + ["profit_abs"])
assert len(bt_data) == 179
# Test loading from string (must yield same result)
bt_data2 = load_backtest_data(str(filename), strategy=strategy)
assert bt_data.equals(bt_data2)
with pytest.raises(ValueError, match=r"Strategy XYZ not available in the backtest result\."):
load_backtest_data(filename, strategy='XYZ')
with pytest.raises(ValueError, match=r"Detected backtest result with more than one strategy.*"):
load_backtest_data(filename)
@pytest.mark.usefixtures("init_persistence")
def test_load_trades_from_db(default_conf, fee, mocker):
@ -46,12 +113,16 @@ def test_load_trades_from_db(default_conf, fee, mocker):
assert len(trades) == 4
assert isinstance(trades, DataFrame)
assert "pair" in trades.columns
assert "open_time" in trades.columns
assert "open_date" in trades.columns
assert "profit_percent" in trades.columns
for col in BT_DATA_COLUMNS:
if col not in ['index', 'open_at_end']:
assert col in trades.columns
trades = load_trades_from_db(db_url=default_conf['db_url'], strategy='DefaultStrategy')
assert len(trades) == 3
trades = load_trades_from_db(db_url=default_conf['db_url'], strategy='NoneStrategy')
assert len(trades) == 0
def test_extract_trades_of_period(testdatadir):
@ -66,13 +137,13 @@ def test_extract_trades_of_period(testdatadir):
{'pair': [pair, pair, pair, pair],
'profit_percent': [0.0, 0.1, -0.2, -0.5],
'profit_abs': [0.0, 1, -2, -5],
'open_time': to_datetime([Arrow(2017, 11, 13, 15, 40, 0).datetime,
'open_date': to_datetime([Arrow(2017, 11, 13, 15, 40, 0).datetime,
Arrow(2017, 11, 14, 9, 41, 0).datetime,
Arrow(2017, 11, 14, 14, 20, 0).datetime,
Arrow(2017, 11, 15, 3, 40, 0).datetime,
], utc=True
),
'close_time': to_datetime([Arrow(2017, 11, 13, 16, 40, 0).datetime,
'close_date': to_datetime([Arrow(2017, 11, 13, 16, 40, 0).datetime,
Arrow(2017, 11, 14, 10, 41, 0).datetime,
Arrow(2017, 11, 14, 15, 25, 0).datetime,
Arrow(2017, 11, 15, 3, 55, 0).datetime,
@ -81,10 +152,10 @@ def test_extract_trades_of_period(testdatadir):
trades1 = extract_trades_of_period(data, trades)
# First and last trade are dropped as they are out of range
assert len(trades1) == 2
assert trades1.iloc[0].open_time == Arrow(2017, 11, 14, 9, 41, 0).datetime
assert trades1.iloc[0].close_time == Arrow(2017, 11, 14, 10, 41, 0).datetime
assert trades1.iloc[-1].open_time == Arrow(2017, 11, 14, 14, 20, 0).datetime
assert trades1.iloc[-1].close_time == Arrow(2017, 11, 14, 15, 25, 0).datetime
assert trades1.iloc[0].open_date == Arrow(2017, 11, 14, 9, 41, 0).datetime
assert trades1.iloc[0].close_date == Arrow(2017, 11, 14, 10, 41, 0).datetime
assert trades1.iloc[-1].open_date == Arrow(2017, 11, 14, 14, 20, 0).datetime
assert trades1.iloc[-1].close_date == Arrow(2017, 11, 14, 15, 25, 0).datetime
def test_analyze_trade_parallelism(default_conf, mocker, testdatadir):
@ -105,7 +176,8 @@ def test_load_trades(default_conf, mocker):
load_trades("DB",
db_url=default_conf.get('db_url'),
exportfilename=default_conf.get('exportfilename'),
no_trades=False
no_trades=False,
strategy="DefaultStrategy",
)
assert db_mock.call_count == 1
@ -135,6 +207,14 @@ def test_load_trades(default_conf, mocker):
assert bt_mock.call_count == 0
def test_calculate_market_change(testdatadir):
pairs = ["ETH/BTC", "ADA/BTC"]
data = load_data(datadir=testdatadir, pairs=pairs, timeframe='5m')
result = calculate_market_change(data)
assert isinstance(result, float)
assert pytest.approx(result) == 0.00955514
def test_combine_dataframes_with_mean(testdatadir):
pairs = ["ETH/BTC", "ADA/BTC"]
data = load_data(datadir=testdatadir, pairs=pairs, timeframe='5m')
@ -165,7 +245,7 @@ def test_create_cum_profit1(testdatadir):
filename = testdatadir / "backtest-result_test.json"
bt_data = load_backtest_data(filename)
# Move close-time to "off" the candle, to make sure the logic still works
bt_data.loc[:, 'close_time'] = bt_data.loc[:, 'close_time'] + DateOffset(seconds=20)
bt_data.loc[:, 'close_date'] = bt_data.loc[:, 'close_date'] + DateOffset(seconds=20)
timerange = TimeRange.parse_timerange("20180110-20180112")
df = load_pair_history(pair="TRX/BTC", timeframe='5m',
@ -204,11 +284,11 @@ def test_calculate_max_drawdown2():
-0.033961, 0.010680, 0.010886, -0.029274, 0.011178, 0.010693, 0.010711]
dates = [Arrow(2020, 1, 1).shift(days=i) for i in range(len(values))]
df = DataFrame(zip(values, dates), columns=['profit', 'open_time'])
df = DataFrame(zip(values, dates), columns=['profit', 'open_date'])
# sort by profit and reset index
df = df.sort_values('profit').reset_index(drop=True)
df1 = df.copy()
drawdown, h, low = calculate_max_drawdown(df, date_col='open_time', value_col='profit')
drawdown, h, low = calculate_max_drawdown(df, date_col='open_date', value_col='profit')
# Ensure df has not been altered.
assert df.equals(df1)
@ -217,6 +297,6 @@ def test_calculate_max_drawdown2():
assert h < low
assert drawdown == 0.091755
df = DataFrame(zip(values[:5], dates[:5]), columns=['profit', 'open_time'])
df = DataFrame(zip(values[:5], dates[:5]), columns=['profit', 'open_date'])
with pytest.raises(ValueError, match='No losing trade, therefore no drawdown.'):
calculate_max_drawdown(df, date_col='open_time', value_col='profit')
calculate_max_drawdown(df, date_col='open_date', value_col='profit')

153
tests/data/test_history.py
View File

@ -12,7 +12,9 @@ from pandas import DataFrame
from pandas.testing import assert_frame_equal
from freqtrade.configuration import TimeRange
from freqtrade.constants import AVAILABLE_DATAHANDLERS
from freqtrade.data.converter import ohlcv_to_dataframe
from freqtrade.data.history.hdf5datahandler import HDF5DataHandler
from freqtrade.data.history.history_utils import (
_download_pair_history, _download_trades_history,
_load_cached_data_for_updating, convert_trades_to_ohlcv, get_timerange,
@ -36,7 +38,7 @@ def _backup_file(file: Path, copy_file: bool = False) -> None:
"""
Backup existing file to avoid deleting the user file
:param file: complete path to the file
:param touch_file: create an empty file in replacement
:param copy_file: keep file in place too.
:return: None
"""
file_swp = str(file) + '.swp'
@ -620,7 +622,7 @@ def test_convert_trades_to_ohlcv(mocker, default_conf, testdatadir, caplog):
_clean_test_file(file5)
def test_jsondatahandler_ohlcv_get_pairs(testdatadir):
def test_datahandler_ohlcv_get_pairs(testdatadir):
pairs = JsonDataHandler.ohlcv_get_pairs(testdatadir, '5m')
# Convert to set to avoid failures due to sorting
assert set(pairs) == {'UNITTEST/BTC', 'XLM/BTC', 'ETH/BTC', 'TRX/BTC', 'LTC/BTC',
@ -630,8 +632,11 @@ def test_jsondatahandler_ohlcv_get_pairs(testdatadir):
pairs = JsonGzDataHandler.ohlcv_get_pairs(testdatadir, '8m')
assert set(pairs) == {'UNITTEST/BTC'}
pairs = HDF5DataHandler.ohlcv_get_pairs(testdatadir, '5m')
assert set(pairs) == {'UNITTEST/BTC'}
def test_jsondatahandler_ohlcv_get_available_data(testdatadir):
def test_datahandler_ohlcv_get_available_data(testdatadir):
paircombs = JsonDataHandler.ohlcv_get_available_data(testdatadir)
# Convert to set to avoid failures due to sorting
assert set(paircombs) == {('UNITTEST/BTC', '5m'), ('ETH/BTC', '5m'), ('XLM/BTC', '5m'),
@ -643,6 +648,8 @@ def test_jsondatahandler_ohlcv_get_available_data(testdatadir):
paircombs = JsonGzDataHandler.ohlcv_get_available_data(testdatadir)
assert set(paircombs) == {('UNITTEST/BTC', '8m')}
paircombs = HDF5DataHandler.ohlcv_get_available_data(testdatadir)
assert set(paircombs) == {('UNITTEST/BTC', '5m')}
def test_jsondatahandler_trades_get_pairs(testdatadir):
@ -653,15 +660,17 @@ def test_jsondatahandler_trades_get_pairs(testdatadir):
def test_jsondatahandler_ohlcv_purge(mocker, testdatadir):
mocker.patch.object(Path, "exists", MagicMock(return_value=False))
mocker.patch.object(Path, "unlink", MagicMock())
unlinkmock = mocker.patch.object(Path, "unlink", MagicMock())
dh = JsonGzDataHandler(testdatadir)
assert not dh.ohlcv_purge('UNITTEST/NONEXIST', '5m')
assert unlinkmock.call_count == 0
mocker.patch.object(Path, "exists", MagicMock(return_value=True))
assert dh.ohlcv_purge('UNITTEST/NONEXIST', '5m')
assert unlinkmock.call_count == 1
def test_jsondatahandler_trades_load(mocker, testdatadir, caplog):
def test_jsondatahandler_trades_load(testdatadir, caplog):
dh = JsonGzDataHandler(testdatadir)
logmsg = "Old trades format detected - converting"
dh.trades_load('XRP/ETH')
@ -674,26 +683,144 @@ def test_jsondatahandler_trades_load(mocker, testdatadir, caplog):
def test_jsondatahandler_trades_purge(mocker, testdatadir):
mocker.patch.object(Path, "exists", MagicMock(return_value=False))
mocker.patch.object(Path, "unlink", MagicMock())
unlinkmock = mocker.patch.object(Path, "unlink", MagicMock())
dh = JsonGzDataHandler(testdatadir)
assert not dh.trades_purge('UNITTEST/NONEXIST')
assert unlinkmock.call_count == 0
mocker.patch.object(Path, "exists", MagicMock(return_value=True))
assert dh.trades_purge('UNITTEST/NONEXIST')
assert unlinkmock.call_count == 1
def test_jsondatahandler_ohlcv_append(testdatadir):
dh = JsonGzDataHandler(testdatadir)
@pytest.mark.parametrize('datahandler', AVAILABLE_DATAHANDLERS)
def test_datahandler_ohlcv_append(datahandler, testdatadir, ):
dh = get_datahandler(testdatadir, datahandler)
with pytest.raises(NotImplementedError):
dh.ohlcv_append('UNITTEST/ETH', '5m', DataFrame())
def test_jsondatahandler_trades_append(testdatadir):
dh = JsonGzDataHandler(testdatadir)
@pytest.mark.parametrize('datahandler', AVAILABLE_DATAHANDLERS)
def test_datahandler_trades_append(datahandler, testdatadir):
dh = get_datahandler(testdatadir, datahandler)
with pytest.raises(NotImplementedError):
dh.trades_append('UNITTEST/ETH', [])
def test_hdf5datahandler_trades_get_pairs(testdatadir):
pairs = HDF5DataHandler.trades_get_pairs(testdatadir)
# Convert to set to avoid failures due to sorting
assert set(pairs) == {'XRP/ETH'}
def test_hdf5datahandler_trades_load(testdatadir):
dh = HDF5DataHandler(testdatadir)
trades = dh.trades_load('XRP/ETH')
assert isinstance(trades, list)
trades1 = dh.trades_load('UNITTEST/NONEXIST')
assert trades1 == []
# data goes from 2019-10-11 - 2019-10-13
timerange = TimeRange.parse_timerange('20191011-20191012')
trades2 = dh._trades_load('XRP/ETH', timerange)
assert len(trades) > len(trades2)
# unfiltered load has trades before starttime
assert len([t for t in trades if t[0] < timerange.startts * 1000]) >= 0
# filtered list does not have trades before starttime
assert len([t for t in trades2 if t[0] < timerange.startts * 1000]) == 0
# unfiltered load has trades after endtime
assert len([t for t in trades if t[0] > timerange.stopts * 1000]) > 0
# filtered list does not have trades after endtime
assert len([t for t in trades2 if t[0] > timerange.stopts * 1000]) == 0
def test_hdf5datahandler_trades_store(testdatadir):
dh = HDF5DataHandler(testdatadir)
trades = dh.trades_load('XRP/ETH')
dh.trades_store('XRP/NEW', trades)
file = testdatadir / 'XRP_NEW-trades.h5'
assert file.is_file()
# Load trades back
trades_new = dh.trades_load('XRP/NEW')
assert len(trades_new) == len(trades)
assert trades[0][0] == trades_new[0][0]
assert trades[0][1] == trades_new[0][1]
# assert trades[0][2] == trades_new[0][2] # This is nan - so comparison does not make sense
assert trades[0][3] == trades_new[0][3]
assert trades[0][4] == trades_new[0][4]
assert trades[0][5] == trades_new[0][5]
assert trades[0][6] == trades_new[0][6]
assert trades[-1][0] == trades_new[-1][0]
assert trades[-1][1] == trades_new[-1][1]
# assert trades[-1][2] == trades_new[-1][2] # This is nan - so comparison does not make sense
assert trades[-1][3] == trades_new[-1][3]
assert trades[-1][4] == trades_new[-1][4]
assert trades[-1][5] == trades_new[-1][5]
assert trades[-1][6] == trades_new[-1][6]
_clean_test_file(file)
def test_hdf5datahandler_trades_purge(mocker, testdatadir):
mocker.patch.object(Path, "exists", MagicMock(return_value=False))
unlinkmock = mocker.patch.object(Path, "unlink", MagicMock())
dh = HDF5DataHandler(testdatadir)
assert not dh.trades_purge('UNITTEST/NONEXIST')
assert unlinkmock.call_count == 0
mocker.patch.object(Path, "exists", MagicMock(return_value=True))
assert dh.trades_purge('UNITTEST/NONEXIST')
assert unlinkmock.call_count == 1
def test_hdf5datahandler_ohlcv_load_and_resave(testdatadir):
dh = HDF5DataHandler(testdatadir)
ohlcv = dh.ohlcv_load('UNITTEST/BTC', '5m')
assert isinstance(ohlcv, DataFrame)
assert len(ohlcv) > 0
file = testdatadir / 'UNITTEST_NEW-5m.h5'
assert not file.is_file()
dh.ohlcv_store('UNITTEST/NEW', '5m', ohlcv)
assert file.is_file()
assert not ohlcv[ohlcv['date'] < '2018-01-15'].empty
# Data gores from 2018-01-10 - 2018-01-30
timerange = TimeRange.parse_timerange('20180115-20180119')
# Call private function to ensure timerange is filtered in hdf5
ohlcv = dh._ohlcv_load('UNITTEST/BTC', '5m', timerange)
ohlcv1 = dh._ohlcv_load('UNITTEST/NEW', '5m', timerange)
assert len(ohlcv) == len(ohlcv1)
assert ohlcv.equals(ohlcv1)
assert ohlcv[ohlcv['date'] < '2018-01-15'].empty
assert ohlcv[ohlcv['date'] > '2018-01-19'].empty
_clean_test_file(file)
# Try loading inexisting file
ohlcv = dh.ohlcv_load('UNITTEST/NONEXIST', '5m')
assert ohlcv.empty
def test_hdf5datahandler_ohlcv_purge(mocker, testdatadir):
mocker.patch.object(Path, "exists", MagicMock(return_value=False))
unlinkmock = mocker.patch.object(Path, "unlink", MagicMock())
dh = HDF5DataHandler(testdatadir)
assert not dh.ohlcv_purge('UNITTEST/NONEXIST', '5m')
assert unlinkmock.call_count == 0
mocker.patch.object(Path, "exists", MagicMock(return_value=True))
assert dh.ohlcv_purge('UNITTEST/NONEXIST', '5m')
assert unlinkmock.call_count == 1
def test_gethandlerclass():
cl = get_datahandlerclass('json')
assert cl == JsonDataHandler
@ -702,6 +829,9 @@ def test_gethandlerclass():
assert cl == JsonGzDataHandler
assert issubclass(cl, IDataHandler)
assert issubclass(cl, JsonDataHandler)
cl = get_datahandlerclass('hdf5')
assert cl == HDF5DataHandler
assert issubclass(cl, IDataHandler)
with pytest.raises(ValueError, match=r"No datahandler for .*"):
get_datahandlerclass('DeadBeef')
@ -713,3 +843,6 @@ def test_get_datahandler(testdatadir):
assert type(dh) == JsonGzDataHandler
dh1 = get_datahandler(testdatadir, 'jsongz', dh)
assert id(dh1) == id(dh)
dh = get_datahandler(testdatadir, 'hdf5')
assert type(dh) == HDF5DataHandler

46
tests/edge/test_edge.py
View File

@ -163,8 +163,8 @@ def test_edge_results(edge_conf, mocker, caplog, data) -> None:
for c, trade in enumerate(data.trades):
res = results.iloc[c]
assert res.exit_type == trade.sell_reason
assert res.open_time == _get_frame_time_from_offset(trade.open_tick).replace(tzinfo=None)
assert res.close_time == _get_frame_time_from_offset(trade.close_tick).replace(tzinfo=None)
assert res.open_date == _get_frame_time_from_offset(trade.open_tick).replace(tzinfo=None)
assert res.close_date == _get_frame_time_from_offset(trade.close_tick).replace(tzinfo=None)
def test_adjust(mocker, edge_conf):
@ -354,10 +354,8 @@ def test_process_expectancy(mocker, edge_conf, fee, risk_reward_ratio, expectanc
'stoploss': -0.9,
'profit_percent': '',
'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:05:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:10:00.000000000'),
'open_index': 1,
'close_index': 1,
'open_date': np.datetime64('2018-10-03T00:05:00.000000000'),
'close_date': np.datetime64('2018-10-03T00:10:00.000000000'),
'trade_duration': '',
'open_rate': 17,
'close_rate': 17,
@ -367,10 +365,8 @@ def test_process_expectancy(mocker, edge_conf, fee, risk_reward_ratio, expectanc
'stoploss': -0.9,
'profit_percent': '',
'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:20:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:25:00.000000000'),
'open_index': 4,
'close_index': 4,
'open_date': np.datetime64('2018-10-03T00:20:00.000000000'),
'close_date': np.datetime64('2018-10-03T00:25:00.000000000'),
'trade_duration': '',
'open_rate': 20,
'close_rate': 20,
@ -380,10 +376,8 @@ def test_process_expectancy(mocker, edge_conf, fee, risk_reward_ratio, expectanc
'stoploss': -0.9,
'profit_percent': '',
'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:30:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:40:00.000000000'),
'open_index': 6,
'close_index': 7,
'open_date': np.datetime64('2018-10-03T00:30:00.000000000'),
'close_date': np.datetime64('2018-10-03T00:40:00.000000000'),
'trade_duration': '',
'open_rate': 26,
'close_rate': 34,
@ -424,8 +418,8 @@ def test_process_expectancy_remove_pumps(mocker, edge_conf, fee,):
'stoploss': -0.9,
'profit_percent': '',
'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:05:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:10:00.000000000'),
'open_date': np.datetime64('2018-10-03T00:05:00.000000000'),
'close_date': np.datetime64('2018-10-03T00:10:00.000000000'),
'open_index': 1,
'close_index': 1,
'trade_duration': '',
@ -437,8 +431,8 @@ def test_process_expectancy_remove_pumps(mocker, edge_conf, fee,):
'stoploss': -0.9,
'profit_percent': '',
'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:20:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:25:00.000000000'),
'open_date': np.datetime64('2018-10-03T00:20:00.000000000'),
'close_date': np.datetime64('2018-10-03T00:25:00.000000000'),
'open_index': 4,
'close_index': 4,
'trade_duration': '',
@ -449,8 +443,8 @@ def test_process_expectancy_remove_pumps(mocker, edge_conf, fee,):
'stoploss': -0.9,
'profit_percent': '',
'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:20:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:25:00.000000000'),
'open_date': np.datetime64('2018-10-03T00:20:00.000000000'),
'close_date': np.datetime64('2018-10-03T00:25:00.000000000'),
'open_index': 4,
'close_index': 4,
'trade_duration': '',
@ -461,8 +455,8 @@ def test_process_expectancy_remove_pumps(mocker, edge_conf, fee,):
'stoploss': -0.9,
'profit_percent': '',
'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:20:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:25:00.000000000'),
'open_date': np.datetime64('2018-10-03T00:20:00.000000000'),
'close_date': np.datetime64('2018-10-03T00:25:00.000000000'),
'open_index': 4,
'close_index': 4,
'trade_duration': '',
@ -473,8 +467,8 @@ def test_process_expectancy_remove_pumps(mocker, edge_conf, fee,):
'stoploss': -0.9,
'profit_percent': '',
'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:20:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:25:00.000000000'),
'open_date': np.datetime64('2018-10-03T00:20:00.000000000'),
'close_date': np.datetime64('2018-10-03T00:25:00.000000000'),
'open_index': 4,
'close_index': 4,
'trade_duration': '',
@ -486,8 +480,8 @@ def test_process_expectancy_remove_pumps(mocker, edge_conf, fee,):
'stoploss': -0.9,
'profit_percent': '',
'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:30:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:40:00.000000000'),
'open_date': np.datetime64('2018-10-03T00:30:00.000000000'),
'close_date': np.datetime64('2018-10-03T00:40:00.000000000'),
'open_index': 6,
'close_index': 7,
'trade_duration': '',

68
tests/exchange/test_exchange.py
View File

@ -11,11 +11,12 @@ import ccxt
import pytest
from pandas import DataFrame
from freqtrade.exceptions import (DependencyException, InvalidOrderException, DDosProtection,
OperationalException, TemporaryError)
from freqtrade.exceptions import (DDosProtection, DependencyException,
InvalidOrderException, OperationalException,
TemporaryError)
from freqtrade.exchange import Binance, Exchange, Kraken
from freqtrade.exchange.common import API_RETRY_COUNT, calculate_backoff
from freqtrade.exchange.exchange import (market_is_active, symbol_is_pair,
from freqtrade.exchange.exchange import (market_is_active,
timeframe_to_minutes,
timeframe_to_msecs,
timeframe_to_next_date,
@ -1760,6 +1761,14 @@ def test_cancel_order_dry_run(default_conf, mocker, exchange_name):
assert exchange.cancel_order(order_id='123', pair='TKN/BTC') == {}
assert exchange.cancel_stoploss_order(order_id='123', pair='TKN/BTC') == {}
order = exchange.buy('ETH/BTC', 'limit', 5, 0.55, 'gtc')
cancel_order = exchange.cancel_order(order_id=order['id'], pair='ETH/BTC')
assert order['id'] == cancel_order['id']
assert order['amount'] == cancel_order['amount']
assert order['pair'] == cancel_order['pair']
assert cancel_order['status'] == 'canceled'
@pytest.mark.parametrize("exchange_name", EXCHANGES)
@pytest.mark.parametrize("order,result", [
@ -2218,25 +2227,42 @@ def test_timeframe_to_next_date():
assert timeframe_to_next_date("5m") > date
@pytest.mark.parametrize("market_symbol,base_currency,quote_currency,expected_result", [
("BTC/USDT", None, None, True),
("USDT/BTC", None, None, True),
("BTCUSDT", None, None, False),
("BTC/USDT", None, "USDT", True),
("USDT/BTC", None, "USDT", False),
("BTCUSDT", None, "USDT", False),
("BTC/USDT", "BTC", None, True),
("USDT/BTC", "BTC", None, False),
("BTCUSDT", "BTC", None, False),
("BTC/USDT", "BTC", "USDT", True),
("BTC/USDT", "USDT", "BTC", False),
("BTC/USDT", "BTC", "USD", False),
("BTCUSDT", "BTC", "USDT", False),
("BTC/", None, None, False),
("/USDT", None, None, False),
@pytest.mark.parametrize("market_symbol,base,quote,exchange,add_dict,expected_result", [
("BTC/USDT", 'BTC', 'USDT', "binance", {}, True),
("USDT/BTC", 'USDT', 'BTC', "binance", {}, True),
("USDT/BTC", 'BTC', 'USDT', "binance", {}, False), # Reversed currencies
("BTCUSDT", 'BTC', 'USDT', "binance", {}, False), # No seperating /
("BTCUSDT", None, "USDT", "binance", {}, False), #
("USDT/BTC", "BTC", None, "binance", {}, False),
("BTCUSDT", "BTC", None, "binance", {}, False),
("BTC/USDT", "BTC", "USDT", "binance", {}, True),
("BTC/USDT", "USDT", "BTC", "binance", {}, False), # reversed currencies
("BTC/USDT", "BTC", "USD", "binance", {}, False), # Wrong quote currency
("BTC/", "BTC", 'UNK', "binance", {}, False),
("/USDT", 'UNK', 'USDT', "binance", {}, False),
("BTC/EUR", 'BTC', 'EUR', "kraken", {"darkpool": False}, True),
("EUR/BTC", 'EUR', 'BTC', "kraken", {"darkpool": False}, True),
("EUR/BTC", 'BTC', 'EUR', "kraken", {"darkpool": False}, False), # Reversed currencies
("BTC/EUR", 'BTC', 'USD', "kraken", {"darkpool": False}, False), # wrong quote currency
("BTC/EUR", 'BTC', 'EUR', "kraken", {"darkpool": True}, False), # no darkpools
("BTC/EUR.d", 'BTC', 'EUR', "kraken", {"darkpool": True}, False), # no darkpools
("BTC/USD", 'BTC', 'USD', "ftx", {'spot': True}, True),
("USD/BTC", 'USD', 'BTC', "ftx", {'spot': True}, True),
("BTC/USD", 'BTC', 'USDT', "ftx", {'spot': True}, False), # Wrong quote currency
("BTC/USD", 'USD', 'BTC', "ftx", {'spot': True}, False), # Reversed currencies
("BTC/USD", 'BTC', 'USD', "ftx", {'spot': False}, False), # Can only trade spot markets
("BTC-PERP", 'BTC', 'USD', "ftx", {'spot': False}, False), # Can only trade spot markets
])
def test_symbol_is_pair(market_symbol, base_currency, quote_currency, expected_result) -> None:
assert symbol_is_pair(market_symbol, base_currency, quote_currency) == expected_result
def test_market_is_tradable(mocker, default_conf, market_symbol, base,
quote, add_dict, exchange, expected_result) -> None:
ex = get_patched_exchange(mocker, default_conf, id=exchange)
market = {
'symbol': market_symbol,
'base': base,
'quote': quote,
**(add_dict),
}
assert ex.market_is_tradable(market) == expected_result
@pytest.mark.parametrize("market,expected_result", [

0
freqtrade/optimize/default_hyperopt.py → tests/optimize/hyperopts/default_hyperopt.py
View File

4
tests/optimize/test_backtest_detail.py
View File

@ -395,5 +395,5 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
for c, trade in enumerate(data.trades):
res = results.iloc[c]
assert res.sell_reason == trade.sell_reason
assert res.open_time == _get_frame_time_from_offset(trade.open_tick)
assert res.close_time == _get_frame_time_from_offset(trade.close_tick)
assert res.open_date == _get_frame_time_from_offset(trade.open_tick)
assert res.close_date == _get_frame_time_from_offset(trade.close_tick)

59
tests/optimize/test_backtesting.py
View File

@ -354,11 +354,12 @@ def test_backtesting_start(default_conf, mocker, testdatadir, caplog) -> None:
exists = [
'Using stake_currency: BTC ...',
'Using stake_amount: 0.001 ...',
'Backtesting with data from 2017-11-14T21:17:00+00:00 '
'up to 2017-11-14T22:59:00+00:00 (0 days)..'
'Backtesting with data from 2017-11-14 21:17:00 '
'up to 2017-11-14 22:59:00 (0 days)..'
]
for line in exists:
assert log_has(line, caplog)
assert backtesting.strategy.dp._pairlists is not None
def test_backtesting_start_no_data(default_conf, mocker, caplog, testdatadir) -> None:
@ -464,28 +465,29 @@ def test_backtest(default_conf, fee, mocker, testdatadir) -> None:
{'pair': [pair, pair],
'profit_percent': [0.0, 0.0],
'profit_abs': [0.0, 0.0],
'open_time': pd.to_datetime([Arrow(2018, 1, 29, 18, 40, 0).datetime,
'open_date': pd.to_datetime([Arrow(2018, 1, 29, 18, 40, 0).datetime,
Arrow(2018, 1, 30, 3, 30, 0).datetime], utc=True
),
'close_time': pd.to_datetime([Arrow(2018, 1, 29, 22, 35, 0).datetime,
'open_rate': [0.104445, 0.10302485],
'open_fee': [0.0025, 0.0025],
'close_date': pd.to_datetime([Arrow(2018, 1, 29, 22, 35, 0).datetime,
Arrow(2018, 1, 30, 4, 10, 0).datetime], utc=True),
'open_index': [78, 184],
'close_index': [125, 192],
'close_rate': [0.104969, 0.103541],
'close_fee': [0.0025, 0.0025],
'amount': [0.00957442, 0.0097064],
'trade_duration': [235, 40],
'open_at_end': [False, False],
'open_rate': [0.104445, 0.10302485],
'close_rate': [0.104969, 0.103541],
'sell_reason': [SellType.ROI, SellType.ROI]
})
pd.testing.assert_frame_equal(results, expected)
data_pair = processed[pair]
for _, t in results.iterrows():
ln = data_pair.loc[data_pair["date"] == t["open_time"]]
ln = data_pair.loc[data_pair["date"] == t["open_date"]]
# Check open trade rate alignes to open rate
assert ln is not None
assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6)
# check close trade rate alignes to close rate or is between high and low
ln = data_pair.loc[data_pair["date"] == t["close_time"]]
ln = data_pair.loc[data_pair["date"] == t["close_date"]]
assert (round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6) or
round(ln.iloc[0]["low"], 6) < round(
t["close_rate"], 6) < round(ln.iloc[0]["high"], 6))
@ -677,10 +679,10 @@ def test_backtest_start_timerange(default_conf, mocker, caplog, testdatadir):
f'Using data directory: {testdatadir} ...',
'Using stake_currency: BTC ...',
'Using stake_amount: 0.001 ...',
'Loading data from 2017-11-14T20:57:00+00:00 '
'up to 2017-11-14T22:58:00+00:00 (0 days)..',
'Backtesting with data from 2017-11-14T21:17:00+00:00 '
'up to 2017-11-14T22:58:00+00:00 (0 days)..',
'Loading data from 2017-11-14 20:57:00 '
'up to 2017-11-14 22:58:00 (0 days)..',
'Backtesting with data from 2017-11-14 21:17:00 '
'up to 2017-11-14 22:58:00 (0 days)..',
'Parameter --enable-position-stacking detected ...'
]
@ -707,6 +709,7 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
generate_pair_metrics=MagicMock(),
generate_sell_reason_stats=sell_reason_mock,
generate_strategy_metrics=strat_summary,
generate_daily_stats=MagicMock(),
)
patched_configuration_load_config_file(mocker, default_conf)
@ -740,10 +743,10 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
f'Using data directory: {testdatadir} ...',
'Using stake_currency: BTC ...',
'Using stake_amount: 0.001 ...',
'Loading data from 2017-11-14T20:57:00+00:00 '
'up to 2017-11-14T22:58:00+00:00 (0 days)..',
'Backtesting with data from 2017-11-14T21:17:00+00:00 '
'up to 2017-11-14T22:58:00+00:00 (0 days)..',
'Loading data from 2017-11-14 20:57:00 '
'up to 2017-11-14 22:58:00 (0 days)..',
'Backtesting with data from 2017-11-14 21:17:00 '
'up to 2017-11-14 22:58:00 (0 days)..',
'Parameter --enable-position-stacking detected ...',
'Running backtesting for Strategy DefaultStrategy',
'Running backtesting for Strategy TestStrategyLegacy',
@ -761,13 +764,11 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC'],
'profit_percent': [0.0, 0.0],
'profit_abs': [0.0, 0.0],
'open_time': pd.to_datetime(['2018-01-29 18:40:00',
'open_date': pd.to_datetime(['2018-01-29 18:40:00',
'2018-01-30 03:30:00', ], utc=True
),
'close_time': pd.to_datetime(['2018-01-29 20:45:00',
'close_date': pd.to_datetime(['2018-01-29 20:45:00',
'2018-01-30 05:35:00', ], utc=True),
'open_index': [78, 184],
'close_index': [125, 192],
'trade_duration': [235, 40],
'open_at_end': [False, False],
'open_rate': [0.104445, 0.10302485],
@ -777,15 +778,13 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC', 'ETH/BTC'],
'profit_percent': [0.03, 0.01, 0.1],
'profit_abs': [0.01, 0.02, 0.2],
'open_time': pd.to_datetime(['2018-01-29 18:40:00',
'open_date': pd.to_datetime(['2018-01-29 18:40:00',
'2018-01-30 03:30:00',
'2018-01-30 05:30:00'], utc=True
),
'close_time': pd.to_datetime(['2018-01-29 20:45:00',
'close_date': pd.to_datetime(['2018-01-29 20:45:00',
'2018-01-30 05:35:00',
'2018-01-30 08:30:00'], utc=True),
'open_index': [78, 184, 185],
'close_index': [125, 224, 205],
'trade_duration': [47, 40, 20],
'open_at_end': [False, False, False],
'open_rate': [0.104445, 0.10302485, 0.122541],
@ -823,10 +822,10 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
f'Using data directory: {testdatadir} ...',
'Using stake_currency: BTC ...',
'Using stake_amount: 0.001 ...',
'Loading data from 2017-11-14T20:57:00+00:00 '
'up to 2017-11-14T22:58:00+00:00 (0 days)..',
'Backtesting with data from 2017-11-14T21:17:00+00:00 '
'up to 2017-11-14T22:58:00+00:00 (0 days)..',
'Loading data from 2017-11-14 20:57:00 '
'up to 2017-11-14 22:58:00 (0 days)..',
'Backtesting with data from 2017-11-14 21:17:00 '
'up to 2017-11-14 22:58:00 (0 days)..',
'Parameter --enable-position-stacking detected ...',
'Running backtesting for Strategy DefaultStrategy',
'Running backtesting for Strategy TestStrategyLegacy',

14
tests/optimize/test_edge_cli.py
View File

@ -105,3 +105,17 @@ def test_edge_init_fee(mocker, edge_conf) -> None:
edge_cli = EdgeCli(edge_conf)
assert edge_cli.edge.fee == 0.1234
assert fee_mock.call_count == 0
def test_edge_start(mocker, edge_conf) -> None:
mock_calculate = mocker.patch('freqtrade.edge.edge_positioning.Edge.calculate',
return_value=True)
table_mock = mocker.patch('freqtrade.optimize.edge_cli.generate_edge_table')
patch_exchange(mocker)
edge_conf['stake_amount'] = 20
edge_cli = EdgeCli(edge_conf)
edge_cli.start()
assert mock_calculate.call_count == 1
assert table_mock.call_count == 1

232
tests/optimize/test_hyperopt.py
View File

@ -3,6 +3,7 @@ import locale
import logging
from datetime import datetime
from pathlib import Path
from copy import deepcopy
from typing import Dict, List
from unittest.mock import MagicMock, PropertyMock
@ -16,7 +17,6 @@ from freqtrade.commands.optimize_commands import (setup_optimize_configuration,
start_hyperopt)
from freqtrade.data.history import load_data
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.optimize.default_hyperopt import DefaultHyperOpt
from freqtrade.optimize.default_hyperopt_loss import DefaultHyperOptLoss
from freqtrade.optimize.hyperopt import Hyperopt
from freqtrade.resolvers.hyperopt_resolver import (HyperOptLossResolver,
@ -26,15 +26,28 @@ from freqtrade.strategy.interface import SellType
from tests.conftest import (get_args, log_has, log_has_re, patch_exchange,
patched_configuration_load_config_file)
from .hyperopts.default_hyperopt import DefaultHyperOpt
@pytest.fixture(scope='function')
def hyperopt(default_conf, mocker):
default_conf.update({
'spaces': ['default'],
def hyperopt_conf(default_conf):
hyperconf = deepcopy(default_conf)
hyperconf.update({
'hyperopt': 'DefaultHyperOpt',
'hyperopt_path': str(Path(__file__).parent / 'hyperopts'),
'epochs': 1,
'timerange': None,
'spaces': ['default'],
'hyperopt_jobs': 1,
})
return hyperconf
@pytest.fixture(scope='function')
def hyperopt(hyperopt_conf, mocker):
patch_exchange(mocker)
return Hyperopt(default_conf)
return Hyperopt(hyperopt_conf)
@pytest.fixture(scope='function')
@ -46,7 +59,7 @@ def hyperopt_results():
'profit_abs': [-0.2, 0.4, 0.6],
'trade_duration': [10, 30, 10],
'sell_reason': [SellType.STOP_LOSS, SellType.ROI, SellType.ROI],
'close_time':
'close_date':
[
datetime(2019, 1, 1, 9, 26, 3, 478039),
datetime(2019, 2, 1, 9, 26, 3, 478039),
@ -160,7 +173,7 @@ def test_setup_hyperopt_configuration_with_arguments(mocker, default_conf, caplo
assert log_has('Parameter --print-all detected ...', caplog)
def test_setup_hyperopt_configuration_unlimited_stake_amount(mocker, default_conf, caplog) -> None:
def test_setup_hyperopt_configuration_unlimited_stake_amount(mocker, default_conf) -> None:
default_conf['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
patched_configuration_load_config_file(mocker, default_conf)
@ -201,7 +214,7 @@ def test_hyperoptresolver(mocker, default_conf, caplog) -> None:
assert hasattr(x, "timeframe")
def test_hyperoptresolver_wrongname(mocker, default_conf, caplog) -> None:
def test_hyperoptresolver_wrongname(default_conf) -> None:
default_conf.update({'hyperopt': "NonExistingHyperoptClass"})
with pytest.raises(OperationalException, match=r'Impossible to load Hyperopt.*'):
@ -216,7 +229,7 @@ def test_hyperoptresolver_noname(default_conf):
HyperOptResolver.load_hyperopt(default_conf)
def test_hyperoptlossresolver(mocker, default_conf, caplog) -> None:
def test_hyperoptlossresolver(mocker, default_conf) -> None:
hl = DefaultHyperOptLoss
mocker.patch(
@ -227,14 +240,14 @@ def test_hyperoptlossresolver(mocker, default_conf, caplog) -> None:
assert hasattr(x, "hyperopt_loss_function")
def test_hyperoptlossresolver_wrongname(mocker, default_conf, caplog) -> None:
def test_hyperoptlossresolver_wrongname(default_conf) -> None:
default_conf.update({'hyperopt_loss': "NonExistingLossClass"})
with pytest.raises(OperationalException, match=r'Impossible to load HyperoptLoss.*'):
HyperOptLossResolver.load_hyperoptloss(default_conf)
def test_start_not_installed(mocker, default_conf, caplog, import_fails) -> None:
def test_start_not_installed(mocker, default_conf, import_fails) -> None:
start_mock = MagicMock()
patched_configuration_load_config_file(mocker, default_conf)
@ -245,6 +258,8 @@ def test_start_not_installed(mocker, default_conf, caplog, import_fails) -> None
'hyperopt',
'--config', 'config.json',
'--hyperopt', 'DefaultHyperOpt',
'--hyperopt-path',
str(Path(__file__).parent / "hyperopts"),
'--epochs', '5'
]
pargs = get_args(args)
@ -253,9 +268,9 @@ def test_start_not_installed(mocker, default_conf, caplog, import_fails) -> None
start_hyperopt(pargs)
def test_start(mocker, default_conf, caplog) -> None:
def test_start(mocker, hyperopt_conf, caplog) -> None:
start_mock = MagicMock()
patched_configuration_load_config_file(mocker, default_conf)
patched_configuration_load_config_file(mocker, hyperopt_conf)
mocker.patch('freqtrade.optimize.hyperopt.Hyperopt.start', start_mock)
patch_exchange(mocker)
@ -272,8 +287,8 @@ def test_start(mocker, default_conf, caplog) -> None:
assert start_mock.call_count == 1
def test_start_no_data(mocker, default_conf, caplog) -> None:
patched_configuration_load_config_file(mocker, default_conf)
def test_start_no_data(mocker, hyperopt_conf) -> None:
patched_configuration_load_config_file(mocker, hyperopt_conf)
mocker.patch('freqtrade.data.history.load_pair_history', MagicMock(return_value=pd.DataFrame))
mocker.patch(
'freqtrade.optimize.hyperopt.get_timerange',
@ -293,9 +308,9 @@ def test_start_no_data(mocker, default_conf, caplog) -> None:
start_hyperopt(pargs)
def test_start_filelock(mocker, default_conf, caplog) -> None:
start_mock = MagicMock(side_effect=Timeout(Hyperopt.get_lock_filename(default_conf)))
patched_configuration_load_config_file(mocker, default_conf)
def test_start_filelock(mocker, hyperopt_conf, caplog) -> None:
start_mock = MagicMock(side_effect=Timeout(Hyperopt.get_lock_filename(hyperopt_conf)))
patched_configuration_load_config_file(mocker, hyperopt_conf)
mocker.patch('freqtrade.optimize.hyperopt.Hyperopt.start', start_mock)
patch_exchange(mocker)
@ -519,7 +534,7 @@ def test_roi_table_generation(hyperopt) -> None:
assert hyperopt.custom_hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0}
def test_start_calls_optimizer(mocker, default_conf, caplog, capsys) -> None:
def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
MagicMock(return_value=(MagicMock(), None)))
@ -545,15 +560,9 @@ def test_start_calls_optimizer(mocker, default_conf, caplog, capsys) -> None:
)
patch_exchange(mocker)
# Co-test loading timeframe from strategy
del default_conf['timeframe']
default_conf.update({'config': 'config.json.example',
'hyperopt': 'DefaultHyperOpt',
'epochs': 1,
'timerange': None,
'spaces': 'default',
'hyperopt_jobs': 1, })
del hyperopt_conf['timeframe']
hyperopt = Hyperopt(default_conf)
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
@ -569,7 +578,7 @@ def test_start_calls_optimizer(mocker, default_conf, caplog, capsys) -> None:
assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
assert hasattr(hyperopt, "max_open_trades")
assert hyperopt.max_open_trades == default_conf['max_open_trades']
assert hyperopt.max_open_trades == hyperopt_conf['max_open_trades']
assert hasattr(hyperopt, "position_stacking")
@ -686,11 +695,34 @@ def test_buy_strategy_generator(hyperopt, testdatadir) -> None:
assert 1 in result['buy']
def test_generate_optimizer(mocker, default_conf) -> None:
default_conf.update({'config': 'config.json.example',
'hyperopt': 'DefaultHyperOpt',
'timerange': None,
'spaces': 'all',
def test_sell_strategy_generator(hyperopt, testdatadir) -> None:
data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], fill_up_missing=True)
dataframes = hyperopt.backtesting.strategy.ohlcvdata_to_dataframe(data)
dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
{'pair': 'UNITTEST/BTC'})
populate_sell_trend = hyperopt.custom_hyperopt.sell_strategy_generator(
{
'sell-adx-value': 20,
'sell-fastd-value': 75,
'sell-mfi-value': 80,
'sell-rsi-value': 20,
'sell-adx-enabled': True,
'sell-fastd-enabled': True,
'sell-mfi-enabled': True,
'sell-rsi-enabled': True,
'sell-trigger': 'sell-bb_upper'
}
)
result = populate_sell_trend(dataframe, {'pair': 'UNITTEST/BTC'})
# Check if some indicators are generated. We will not test all of them
print(result)
assert 'sell' in result
assert 1 in result['sell']
def test_generate_optimizer(mocker, hyperopt_conf) -> None:
hyperopt_conf.update({'spaces': 'all',
'hyperopt_min_trades': 1,
})
@ -744,8 +776,10 @@ def test_generate_optimizer(mocker, default_conf) -> None:
}
response_expected = {
'loss': 1.9840569076926293,
'results_explanation': (' 1 trades. Avg profit 2.31%. Total profit 0.00023300 BTC '
'( 2.31\N{GREEK CAPITAL LETTER SIGMA}%). Avg duration 100.0 min.'
'results_explanation': (' 1 trades. 1/0/0 Wins/Draws/Losses. '
'Avg profit 2.31%. Median profit 2.31%. Total profit '
'0.00023300 BTC ( 2.31\N{GREEK CAPITAL LETTER SIGMA}%). '
'Avg duration 100.0 min.'
).encode(locale.getpreferredencoding(), 'replace').decode('utf-8'),
'params_details': {'buy': {'adx-enabled': False,
'adx-value': 0,
@ -776,55 +810,47 @@ def test_generate_optimizer(mocker, default_conf) -> None:
'trailing_stop_positive_offset': 0.07}},
'params_dict': optimizer_param,
'results_metrics': {'avg_profit': 2.3117,
'draws': 0,
'duration': 100.0,
'losses': 0,
'winsdrawslosses': '1/0/0',
'median_profit': 2.3117,
'profit': 2.3117,
'total_profit': 0.000233,
'trade_count': 1},
'trade_count': 1,
'wins': 1},
'total_profit': 0.00023300
}
hyperopt = Hyperopt(default_conf)
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.dimensions = hyperopt.hyperopt_space()
generate_optimizer_value = hyperopt.generate_optimizer(list(optimizer_param.values()))
assert generate_optimizer_value == response_expected
def test_clean_hyperopt(mocker, default_conf, caplog):
def test_clean_hyperopt(mocker, hyperopt_conf, caplog):
patch_exchange(mocker)
default_conf.update({'config': 'config.json.example',
'hyperopt': 'DefaultHyperOpt',
'epochs': 1,
'timerange': None,
'spaces': 'default',
'hyperopt_jobs': 1,
})
mocker.patch("freqtrade.optimize.hyperopt.Path.is_file", MagicMock(return_value=True))
unlinkmock = mocker.patch("freqtrade.optimize.hyperopt.Path.unlink", MagicMock())
h = Hyperopt(default_conf)
h = Hyperopt(hyperopt_conf)
assert unlinkmock.call_count == 2
assert log_has(f"Removing `{h.data_pickle_file}`.", caplog)
def test_continue_hyperopt(mocker, default_conf, caplog):
def test_continue_hyperopt(mocker, hyperopt_conf, caplog):
patch_exchange(mocker)
default_conf.update({'config': 'config.json.example',
'hyperopt': 'DefaultHyperOpt',
'epochs': 1,
'timerange': None,
'spaces': 'default',
'hyperopt_jobs': 1,
'hyperopt_continue': True
})
hyperopt_conf.update({'hyperopt_continue': True})
mocker.patch("freqtrade.optimize.hyperopt.Path.is_file", MagicMock(return_value=True))
unlinkmock = mocker.patch("freqtrade.optimize.hyperopt.Path.unlink", MagicMock())
Hyperopt(default_conf)
Hyperopt(hyperopt_conf)
assert unlinkmock.call_count == 0
assert log_has("Continuing on previous hyperopt results.", caplog)
def test_print_json_spaces_all(mocker, default_conf, caplog, capsys) -> None:
def test_print_json_spaces_all(mocker, hyperopt_conf, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
MagicMock(return_value=(MagicMock(), None)))
@ -855,16 +881,12 @@ def test_print_json_spaces_all(mocker, default_conf, caplog, capsys) -> None:
)
patch_exchange(mocker)
default_conf.update({'config': 'config.json.example',
'hyperopt': 'DefaultHyperOpt',
'epochs': 1,
'timerange': None,
'spaces': 'all',
hyperopt_conf.update({'spaces': 'all',
'hyperopt_jobs': 1,
'print_json': True,
})
hyperopt = Hyperopt(default_conf)
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
@ -883,7 +905,7 @@ def test_print_json_spaces_all(mocker, default_conf, caplog, capsys) -> None:
assert dumper.call_count == 2
def test_print_json_spaces_default(mocker, default_conf, caplog, capsys) -> None:
def test_print_json_spaces_default(mocker, hyperopt_conf, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
MagicMock(return_value=(MagicMock(), None)))
@ -913,16 +935,9 @@ def test_print_json_spaces_default(mocker, default_conf, caplog, capsys) -> None
)
patch_exchange(mocker)
default_conf.update({'config': 'config.json.example',
'hyperopt': 'DefaultHyperOpt',
'epochs': 1,
'timerange': None,
'spaces': 'default',
'hyperopt_jobs': 1,
'print_json': True,
})
hyperopt_conf.update({'print_json': True})
hyperopt = Hyperopt(default_conf)
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
@ -937,7 +952,7 @@ def test_print_json_spaces_default(mocker, default_conf, caplog, capsys) -> None
assert dumper.call_count == 2
def test_print_json_spaces_roi_stoploss(mocker, default_conf, caplog, capsys) -> None:
def test_print_json_spaces_roi_stoploss(mocker, hyperopt_conf, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
MagicMock(return_value=(MagicMock(), None)))
@ -963,16 +978,12 @@ def test_print_json_spaces_roi_stoploss(mocker, default_conf, caplog, capsys) ->
)
patch_exchange(mocker)
default_conf.update({'config': 'config.json.example',
'hyperopt': 'DefaultHyperOpt',
'epochs': 1,
'timerange': None,
'spaces': 'roi stoploss',
hyperopt_conf.update({'spaces': 'roi stoploss',
'hyperopt_jobs': 1,
'print_json': True,
})
hyperopt = Hyperopt(default_conf)
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
@ -987,7 +998,7 @@ def test_print_json_spaces_roi_stoploss(mocker, default_conf, caplog, capsys) ->
assert dumper.call_count == 2
def test_simplified_interface_roi_stoploss(mocker, default_conf, caplog, capsys) -> None:
def test_simplified_interface_roi_stoploss(mocker, hyperopt_conf, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
MagicMock(return_value=(MagicMock(), None)))
@ -1012,14 +1023,9 @@ def test_simplified_interface_roi_stoploss(mocker, default_conf, caplog, capsys)
)
patch_exchange(mocker)
default_conf.update({'config': 'config.json.example',
'hyperopt': 'DefaultHyperOpt',
'epochs': 1,
'timerange': None,
'spaces': 'roi stoploss',
'hyperopt_jobs': 1, })
hyperopt_conf.update({'spaces': 'roi stoploss'})
hyperopt = Hyperopt(default_conf)
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
@ -1040,11 +1046,11 @@ def test_simplified_interface_roi_stoploss(mocker, default_conf, caplog, capsys)
assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
assert hasattr(hyperopt, "max_open_trades")
assert hyperopt.max_open_trades == default_conf['max_open_trades']
assert hyperopt.max_open_trades == hyperopt_conf['max_open_trades']
assert hasattr(hyperopt, "position_stacking")
def test_simplified_interface_all_failed(mocker, default_conf, caplog, capsys) -> None:
def test_simplified_interface_all_failed(mocker, hyperopt_conf) -> None:
mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
MagicMock(return_value=(MagicMock(), None)))
@ -1055,14 +1061,9 @@ def test_simplified_interface_all_failed(mocker, default_conf, caplog, capsys) -
patch_exchange(mocker)
default_conf.update({'config': 'config.json.example',
'hyperopt': 'DefaultHyperOpt',
'epochs': 1,
'timerange': None,
'spaces': 'all',
'hyperopt_jobs': 1, })
hyperopt_conf.update({'spaces': 'all', })
hyperopt = Hyperopt(default_conf)
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
@ -1075,7 +1076,7 @@ def test_simplified_interface_all_failed(mocker, default_conf, caplog, capsys) -
hyperopt.start()
def test_simplified_interface_buy(mocker, default_conf, caplog, capsys) -> None:
def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
MagicMock(return_value=(MagicMock(), None)))
@ -1100,14 +1101,9 @@ def test_simplified_interface_buy(mocker, default_conf, caplog, capsys) -> None:
)
patch_exchange(mocker)
default_conf.update({'config': 'config.json.example',
'hyperopt': 'DefaultHyperOpt',
'epochs': 1,
'timerange': None,
'spaces': 'buy',
'hyperopt_jobs': 1, })
hyperopt_conf.update({'spaces': 'buy'})
hyperopt = Hyperopt(default_conf)
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
@ -1128,11 +1124,11 @@ def test_simplified_interface_buy(mocker, default_conf, caplog, capsys) -> None:
assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
assert hasattr(hyperopt, "max_open_trades")
assert hyperopt.max_open_trades == default_conf['max_open_trades']
assert hyperopt.max_open_trades == hyperopt_conf['max_open_trades']
assert hasattr(hyperopt, "position_stacking")
def test_simplified_interface_sell(mocker, default_conf, caplog, capsys) -> None:
def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
MagicMock(return_value=(MagicMock(), None)))
@ -1157,14 +1153,9 @@ def test_simplified_interface_sell(mocker, default_conf, caplog, capsys) -> None
)
patch_exchange(mocker)
default_conf.update({'config': 'config.json.example',
'hyperopt': 'DefaultHyperOpt',
'epochs': 1,
'timerange': None,
'spaces': 'sell',
'hyperopt_jobs': 1, })
hyperopt_conf.update({'spaces': 'sell', })
hyperopt = Hyperopt(default_conf)
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
@ -1185,7 +1176,7 @@ def test_simplified_interface_sell(mocker, default_conf, caplog, capsys) -> None
assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
assert hasattr(hyperopt, "max_open_trades")
assert hyperopt.max_open_trades == default_conf['max_open_trades']
assert hyperopt.max_open_trades == hyperopt_conf['max_open_trades']
assert hasattr(hyperopt, "position_stacking")
@ -1195,7 +1186,7 @@ def test_simplified_interface_sell(mocker, default_conf, caplog, capsys) -> None
('sell_strategy_generator', 'sell'),
('sell_indicator_space', 'sell'),
])
def test_simplified_interface_failed(mocker, default_conf, caplog, capsys, method, space) -> None:
def test_simplified_interface_failed(mocker, hyperopt_conf, method, space) -> None:
mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
MagicMock(return_value=(MagicMock(), None)))
@ -1206,14 +1197,9 @@ def test_simplified_interface_failed(mocker, default_conf, caplog, capsys, metho
patch_exchange(mocker)
default_conf.update({'config': 'config.json.example',
'hyperopt': 'DefaultHyperOpt',
'epochs': 1,
'timerange': None,
'spaces': space,
'hyperopt_jobs': 1, })
hyperopt_conf.update({'spaces': space})
hyperopt = Hyperopt(default_conf)
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})

229
tests/optimize/test_optimize_reports.py
View File

@ -1,16 +1,29 @@
import re
from datetime import timedelta
from pathlib import Path
import pandas as pd
import pytest
from arrow import Arrow
from freqtrade.configuration import TimeRange
from freqtrade.constants import LAST_BT_RESULT_FN
from freqtrade.data import history
from freqtrade.data.btanalysis import (get_latest_backtest_filename,
load_backtest_data)
from freqtrade.edge import PairInfo
from freqtrade.optimize.optimize_reports import (
generate_pair_metrics, generate_edge_table, generate_sell_reason_stats,
text_table_bt_results, text_table_sell_reason, generate_strategy_metrics,
text_table_strategy, store_backtest_result)
from freqtrade.optimize.optimize_reports import (generate_backtest_stats,
generate_daily_stats,
generate_edge_table,
generate_pair_metrics,
generate_sell_reason_stats,
generate_strategy_metrics,
store_backtest_stats,
text_table_bt_results,
text_table_sell_reason,
text_table_strategy)
from freqtrade.strategy.interface import SellType
from tests.conftest import patch_exchange
from tests.data.test_history import _backup_file, _clean_test_file
def test_text_table_bt_results(default_conf, mocker):
@ -43,6 +56,115 @@ def test_text_table_bt_results(default_conf, mocker):
assert text_table_bt_results(pair_results, stake_currency='BTC') == result_str
def test_generate_backtest_stats(default_conf, testdatadir):
results = {'DefStrat': pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
"UNITTEST/BTC", "UNITTEST/BTC"],
"profit_percent": [0.003312, 0.010801, 0.013803, 0.002780],
"profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
"open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
Arrow(2017, 11, 14, 21, 36, 00).datetime,
Arrow(2017, 11, 14, 22, 12, 00).datetime,
Arrow(2017, 11, 14, 22, 44, 00).datetime],
"close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
Arrow(2017, 11, 14, 22, 10, 00).datetime,
Arrow(2017, 11, 14, 22, 43, 00).datetime,
Arrow(2017, 11, 14, 22, 58, 00).datetime],
"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
"close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
"trade_duration": [123, 34, 31, 14],
"open_at_end": [False, False, False, True],
"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
SellType.ROI, SellType.FORCE_SELL]
})}
timerange = TimeRange.parse_timerange('1510688220-1510700340')
min_date = Arrow.fromtimestamp(1510688220)
max_date = Arrow.fromtimestamp(1510700340)
btdata = history.load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange,
fill_up_missing=True)
stats = generate_backtest_stats(default_conf, btdata, results, min_date, max_date)
assert isinstance(stats, dict)
assert 'strategy' in stats
assert 'DefStrat' in stats['strategy']
assert 'strategy_comparison' in stats
strat_stats = stats['strategy']['DefStrat']
assert strat_stats['backtest_start'] == min_date.datetime
assert strat_stats['backtest_end'] == max_date.datetime
assert strat_stats['total_trades'] == len(results['DefStrat'])
# Above sample had no loosing trade
assert strat_stats['max_drawdown'] == 0.0
results = {'DefStrat': pd.DataFrame(
{"pair": ["UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC"],
"profit_percent": [0.003312, 0.010801, -0.013803, 0.002780],
"profit_abs": [0.000003, 0.000011, -0.000014, 0.000003],
"open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
Arrow(2017, 11, 14, 21, 36, 00).datetime,
Arrow(2017, 11, 14, 22, 12, 00).datetime,
Arrow(2017, 11, 14, 22, 44, 00).datetime],
"close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
Arrow(2017, 11, 14, 22, 10, 00).datetime,
Arrow(2017, 11, 14, 22, 43, 00).datetime,
Arrow(2017, 11, 14, 22, 58, 00).datetime],
"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
"close_rate": [0.002546, 0.003014, 0.0032903, 0.003217],
"trade_duration": [123, 34, 31, 14],
"open_at_end": [False, False, False, True],
"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
SellType.ROI, SellType.FORCE_SELL]
})}
assert strat_stats['max_drawdown'] == 0.0
assert strat_stats['drawdown_start'] == Arrow.fromtimestamp(0).datetime
assert strat_stats['drawdown_end'] == Arrow.fromtimestamp(0).datetime
assert strat_stats['drawdown_end_ts'] == 0
assert strat_stats['drawdown_start_ts'] == 0
assert strat_stats['pairlist'] == ['UNITTEST/BTC']
# Test storing stats
filename = Path(testdatadir / 'btresult.json')
filename_last = Path(testdatadir / LAST_BT_RESULT_FN)
_backup_file(filename_last, copy_file=True)
assert not filename.is_file()
store_backtest_stats(filename, stats)
# get real Filename (it's btresult-<date>.json)
last_fn = get_latest_backtest_filename(filename_last.parent)
assert re.match(r"btresult-.*\.json", last_fn)
filename1 = (testdatadir / last_fn)
assert filename1.is_file()
content = filename1.read_text()
assert 'max_drawdown' in content
assert 'strategy' in content
assert 'pairlist' in content
assert filename_last.is_file()
_clean_test_file(filename_last)
filename1.unlink()
def test_store_backtest_stats(testdatadir, mocker):
dump_mock = mocker.patch('freqtrade.optimize.optimize_reports.file_dump_json')
store_backtest_stats(testdatadir, {})
assert dump_mock.call_count == 2
assert isinstance(dump_mock.call_args_list[0][0][0], Path)
assert str(dump_mock.call_args_list[0][0][0]).startswith(str(testdatadir/'backtest-result'))
dump_mock.reset_mock()
filename = testdatadir / 'testresult.json'
store_backtest_stats(filename, {})
assert dump_mock.call_count == 2
assert isinstance(dump_mock.call_args_list[0][0][0], Path)
# result will be testdatadir / testresult-<timestamp>.json
assert str(dump_mock.call_args_list[0][0][0]).startswith(str(testdatadir / 'testresult'))
def test_generate_pair_metrics(default_conf, mocker):
results = pd.DataFrame(
@ -68,6 +190,29 @@ def test_generate_pair_metrics(default_conf, mocker):
pytest.approx(pair_results[-1]['profit_sum_pct']) == pair_results[-1]['profit_sum'] * 100)
def test_generate_daily_stats(testdatadir):
filename = testdatadir / "backtest-result_new.json"
bt_data = load_backtest_data(filename)
res = generate_daily_stats(bt_data)
assert isinstance(res, dict)
assert round(res['backtest_best_day'], 4) == 0.1796
assert round(res['backtest_worst_day'], 4) == -0.1468
assert res['winning_days'] == 14
assert res['draw_days'] == 4
assert res['losing_days'] == 3
assert res['winner_holding_avg'] == timedelta(seconds=1440)
assert res['loser_holding_avg'] == timedelta(days=1, seconds=21420)
# Select empty dataframe!
res = generate_daily_stats(bt_data.loc[bt_data['open_date'] == '2000-01-01', :])
assert isinstance(res, dict)
assert round(res['backtest_best_day'], 4) == 0.0
assert res['winning_days'] == 0
assert res['draw_days'] == 0
assert res['losing_days'] == 0
def test_text_table_sell_reason(default_conf):
results = pd.DataFrame(
@ -188,77 +333,3 @@ def test_generate_edge_table(edge_conf, mocker):
assert generate_edge_table(results).count('| ETH/BTC |') == 1
assert generate_edge_table(results).count(
'| Risk Reward Ratio | Required Risk Reward | Expectancy |') == 1
def test_backtest_record(default_conf, fee, mocker):
names = []
records = []
patch_exchange(mocker)
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
mocker.patch(
'freqtrade.optimize.optimize_reports.file_dump_json',
new=lambda n, r: (names.append(n), records.append(r))
)
results = {'DefStrat': pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
"UNITTEST/BTC", "UNITTEST/BTC"],
"profit_percent": [0.003312, 0.010801, 0.013803, 0.002780],
"profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
"open_time": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
Arrow(2017, 11, 14, 21, 36, 00).datetime,
Arrow(2017, 11, 14, 22, 12, 00).datetime,
Arrow(2017, 11, 14, 22, 44, 00).datetime],
"close_time": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
Arrow(2017, 11, 14, 22, 10, 00).datetime,
Arrow(2017, 11, 14, 22, 43, 00).datetime,
Arrow(2017, 11, 14, 22, 58, 00).datetime],
"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
"close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
"open_index": [1, 119, 153, 185],
"close_index": [118, 151, 184, 199],
"trade_duration": [123, 34, 31, 14],
"open_at_end": [False, False, False, True],
"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
SellType.ROI, SellType.FORCE_SELL]
})}
store_backtest_result(Path("backtest-result.json"), results)
# Assert file_dump_json was only called once
assert names == [Path('backtest-result.json')]
records = records[0]
# Ensure records are of correct type
assert len(records) == 4
# reset test to test with strategy name
names = []
records = []
results['Strat'] = results['DefStrat']
results['Strat2'] = results['DefStrat']
store_backtest_result(Path("backtest-result.json"), results)
assert names == [
Path('backtest-result-DefStrat.json'),
Path('backtest-result-Strat.json'),
Path('backtest-result-Strat2.json'),
]
records = records[0]
# Ensure records are of correct type
assert len(records) == 4
# ('UNITTEST/BTC', 0.00331158, '1510684320', '1510691700', 0, 117)
# Below follows just a typecheck of the schema/type of trade-records
oix = None
for (pair, profit, date_buy, date_sell, buy_index, dur,
openr, closer, open_at_end, sell_reason) in records:
assert pair == 'UNITTEST/BTC'
assert isinstance(profit, float)
# FIX: buy/sell should be converted to ints
assert isinstance(date_buy, float)
assert isinstance(date_sell, float)
assert isinstance(openr, float)
assert isinstance(closer, float)
assert isinstance(open_at_end, bool)
assert isinstance(sell_reason, str)
isinstance(buy_index, pd._libs.tslib.Timestamp)
if oix:
assert buy_index > oix
oix = buy_index
assert dur > 0

62
tests/pairlist/test_pairlist.py
View File

@ -231,9 +231,6 @@ def test_VolumePairList_refresh_empty(mocker, markets_empty, whitelist_conf):
# VolumePairList only
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"}],
"BTC", ['ETH/BTC', 'TKN/BTC', 'LTC/BTC', 'XRP/BTC', 'HOT/BTC']),
# Different sorting depending on quote or bid volume
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "bidVolume"}],
"BTC", ['HOT/BTC', 'FUEL/BTC', 'XRP/BTC', 'LTC/BTC', 'TKN/BTC']),
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"}],
"USDT", ['ETH/USDT', 'NANO/USDT', 'ADAHALF/USDT', 'ADADOUBLE/USDT']),
# No pair for ETH, VolumePairList
@ -263,10 +260,6 @@ def test_VolumePairList_refresh_empty(mocker, markets_empty, whitelist_conf):
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
{"method": "PrecisionFilter"}],
"BTC", ['ETH/BTC', 'TKN/BTC', 'LTC/BTC', 'XRP/BTC']),
# Precisionfilter bid
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "bidVolume"},
{"method": "PrecisionFilter"}],
"BTC", ['FUEL/BTC', 'XRP/BTC', 'LTC/BTC', 'TKN/BTC']),
# PriceFilter and VolumePairList
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
{"method": "PriceFilter", "low_price_ratio": 0.03}],
@ -293,9 +286,6 @@ def test_VolumePairList_refresh_empty(mocker, markets_empty, whitelist_conf):
([{"method": "StaticPairList"}],
"BTC", ['ETH/BTC', 'TKN/BTC', 'HOT/BTC']),
# Static Pairlist before VolumePairList - sorting changes
([{"method": "StaticPairList"},
{"method": "VolumePairList", "number_assets": 5, "sort_key": "bidVolume"}],
"BTC", ['HOT/BTC', 'TKN/BTC', 'ETH/BTC']),
# SpreadFilter
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
{"method": "SpreadFilter", "max_spread_ratio": 0.005}],
@ -344,7 +334,7 @@ def test_VolumePairList_refresh_empty(mocker, markets_empty, whitelist_conf):
([{"method": "SpreadFilter", "max_spread_ratio": 0.005}],
"BTC", 'filter_at_the_beginning'), # OperationalException expected
# Static Pairlist after VolumePairList, on a non-first position
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "bidVolume"},
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
{"method": "StaticPairList"}],
"BTC", 'static_in_the_middle'),
([{"method": "VolumePairList", "number_assets": 20, "sort_key": "quoteVolume"},
@ -468,7 +458,9 @@ def test_pairlist_class(mocker, whitelist_conf, markets, pairlist):
# BCH/BTC not available
(['ETH/BTC', 'TKN/BTC', 'BCH/BTC'], "is not compatible with exchange"),
# BTT/BTC is inactive
(['ETH/BTC', 'TKN/BTC', 'BTT/BTC'], "Market is not active")
(['ETH/BTC', 'TKN/BTC', 'BTT/BTC'], "Market is not active"),
# XLTCUSDT is not a valid pair
(['ETH/BTC', 'TKN/BTC', 'XLTCUSDT'], "is not tradable with Freqtrade"),
])
def test__whitelist_for_active_markets(mocker, whitelist_conf, markets, pairlist, whitelist, caplog,
log_message, tickers):
@ -547,7 +539,7 @@ def test_agefilter_min_days_listed_too_small(mocker, default_conf, markets, tick
)
with pytest.raises(OperationalException,
match=r'AgeFilter requires min_days_listed must be >= 1'):
match=r'AgeFilter requires min_days_listed to be >= 1'):
get_patched_freqtradebot(mocker, default_conf)
@ -562,7 +554,7 @@ def test_agefilter_min_days_listed_too_large(mocker, default_conf, markets, tick
)
with pytest.raises(OperationalException,
match=r'AgeFilter requires min_days_listed must not exceed '
match=r'AgeFilter requires min_days_listed to not exceed '
r'exchange max request size \([0-9]+\)'):
get_patched_freqtradebot(mocker, default_conf)
@ -590,34 +582,58 @@ def test_agefilter_caching(mocker, markets, whitelist_conf_3, tickers, ohlcv_his
assert freqtrade.exchange.get_historic_ohlcv.call_count == previous_call_count
@pytest.mark.parametrize("pairlistconfig,expected", [
@pytest.mark.parametrize("pairlistconfig,desc_expected,exception_expected", [
({"method": "PriceFilter", "low_price_ratio": 0.001, "min_price": 0.00000010,
"max_price": 1.0}, "[{'PriceFilter': 'PriceFilter - Filtering pairs priced below "
"0.1% or below 0.00000010 or above 1.00000000.'}]"
"max_price": 1.0},
"[{'PriceFilter': 'PriceFilter - Filtering pairs priced below "
"0.1% or below 0.00000010 or above 1.00000000.'}]",
None
),
({"method": "PriceFilter", "low_price_ratio": 0.001, "min_price": 0.00000010},
"[{'PriceFilter': 'PriceFilter - Filtering pairs priced below 0.1% or below 0.00000010.'}]"
"[{'PriceFilter': 'PriceFilter - Filtering pairs priced below 0.1% or below 0.00000010.'}]",
None
),
({"method": "PriceFilter", "low_price_ratio": 0.001, "max_price": 1.00010000},
"[{'PriceFilter': 'PriceFilter - Filtering pairs priced below 0.1% or above 1.00010000.'}]"
"[{'PriceFilter': 'PriceFilter - Filtering pairs priced below 0.1% or above 1.00010000.'}]",
None
),
({"method": "PriceFilter", "min_price": 0.00002000},
"[{'PriceFilter': 'PriceFilter - Filtering pairs priced below 0.00002000.'}]"
"[{'PriceFilter': 'PriceFilter - Filtering pairs priced below 0.00002000.'}]",
None
),
({"method": "PriceFilter"},
"[{'PriceFilter': 'PriceFilter - No price filters configured.'}]"
"[{'PriceFilter': 'PriceFilter - No price filters configured.'}]",
None
),
({"method": "PriceFilter", "low_price_ratio": -0.001},
None,
"PriceFilter requires low_price_ratio to be >= 0"
), # OperationalException expected
({"method": "PriceFilter", "min_price": -0.00000010},
None,
"PriceFilter requires min_price to be >= 0"
), # OperationalException expected
({"method": "PriceFilter", "max_price": -1.00010000},
None,
"PriceFilter requires max_price to be >= 0"
), # OperationalException expected
])
def test_pricefilter_desc(mocker, whitelist_conf, markets, pairlistconfig, expected):
def test_pricefilter_desc(mocker, whitelist_conf, markets, pairlistconfig,
desc_expected, exception_expected):
mocker.patch.multiple('freqtrade.exchange.Exchange',
markets=PropertyMock(return_value=markets),
exchange_has=MagicMock(return_value=True)
)
whitelist_conf['pairlists'] = [pairlistconfig]
if desc_expected is not None:
freqtrade = get_patched_freqtradebot(mocker, whitelist_conf)
short_desc = str(freqtrade.pairlists.short_desc())
assert short_desc == expected
assert short_desc == desc_expected
else: # OperationalException expected
with pytest.raises(OperationalException,
match=exception_expected):
freqtrade = get_patched_freqtradebot(mocker, whitelist_conf)
def test_pairlistmanager_no_pairlist(mocker, markets, whitelist_conf, caplog):

49
tests/rpc/test_rpc.py
View File

@ -79,7 +79,8 @@ def test_rpc_trade_status(default_conf, ticker, fee, mocker) -> None:
'open_rate': 1.098e-05,
'close_rate': None,
'current_rate': 1.099e-05,
'amount': 91.07468124,
'amount': 91.07468123,
'amount_requested': 91.07468123,
'stake_amount': 0.001,
'close_profit': None,
'close_profit_pct': None,
@ -100,6 +101,7 @@ def test_rpc_trade_status(default_conf, ticker, fee, mocker) -> None:
'initial_stop_loss_ratio': -0.1,
'stoploss_current_dist': -1.1080000000000002e-06,
'stoploss_current_dist_ratio': -0.10081893,
'stoploss_current_dist_pct': -10.08,
'stoploss_entry_dist': -0.00010475,
'stoploss_entry_dist_ratio': -0.10448878,
'open_order': None,
@ -142,7 +144,8 @@ def test_rpc_trade_status(default_conf, ticker, fee, mocker) -> None:
'open_rate': 1.098e-05,
'close_rate': None,
'current_rate': ANY,
'amount': 91.07468124,
'amount': 91.07468123,
'amount_requested': 91.07468123,
'stake_amount': 0.001,
'close_profit': None,
'close_profit_pct': None,
@ -163,6 +166,7 @@ def test_rpc_trade_status(default_conf, ticker, fee, mocker) -> None:
'initial_stop_loss_ratio': -0.1,
'stoploss_current_dist': ANY,
'stoploss_current_dist_ratio': ANY,
'stoploss_current_dist_pct': ANY,
'stoploss_entry_dist': -0.00010475,
'stoploss_entry_dist_ratio': -0.10448878,
'open_order': None,
@ -253,11 +257,11 @@ def test_rpc_daily_profit(default_conf, update, ticker, fee,
assert days['fiat_display_currency'] == default_conf['fiat_display_currency']
for day in days['data']:
# [datetime.date(2018, 1, 11), '0.00000000 BTC', '0.000 USD']
assert (day['abs_profit'] == '0.00000000' or
day['abs_profit'] == '0.00006217')
assert (day['abs_profit'] == 0.0 or
day['abs_profit'] == 0.00006217)
assert (day['fiat_value'] == '0.000' or
day['fiat_value'] == '0.767')
assert (day['fiat_value'] == 0.0 or
day['fiat_value'] == 0.76748865)
# ensure first day is current date
assert str(days['data'][0]['date']) == str(datetime.utcnow().date())
@ -665,7 +669,8 @@ def test_rpc_forcesell(default_conf, ticker, fee, mocker) -> None:
return_value={
'status': 'closed',
'type': 'limit',
'side': 'buy'
'side': 'buy',
'filled': 0.0,
}
),
get_fee=fee,
@ -691,6 +696,7 @@ def test_rpc_forcesell(default_conf, ticker, fee, mocker) -> None:
msg = rpc._rpc_forcesell('all')
assert msg == {'result': 'Created sell orders for all open trades.'}
freqtradebot.enter_positions()
msg = rpc._rpc_forcesell('1')
assert msg == {'result': 'Created sell order for trade 1.'}
@ -703,17 +709,24 @@ def test_rpc_forcesell(default_conf, ticker, fee, mocker) -> None:
freqtradebot.state = State.RUNNING
assert cancel_order_mock.call_count == 0
freqtradebot.enter_positions()
# make an limit-buy open trade
trade = Trade.query.filter(Trade.id == '1').first()
filled_amount = trade.amount / 2
# Fetch order - it's open first, and closed after cancel_order is called.
mocker.patch(
'freqtrade.exchange.Exchange.fetch_order',
return_value={
side_effect=[{
'status': 'open',
'type': 'limit',
'side': 'buy',
'filled': filled_amount
}
}, {
'status': 'closed',
'type': 'limit',
'side': 'buy',
'filled': filled_amount
}]
)
# check that the trade is called, which is done by ensuring exchange.cancel_order is called
# and trade amount is updated
@ -721,6 +734,16 @@ def test_rpc_forcesell(default_conf, ticker, fee, mocker) -> None:
assert cancel_order_mock.call_count == 1
assert trade.amount == filled_amount
mocker.patch(
'freqtrade.exchange.Exchange.fetch_order',
return_value={
'status': 'open',
'type': 'limit',
'side': 'buy',
'filled': filled_amount
})
freqtradebot.config['max_open_trades'] = 3
freqtradebot.enter_positions()
trade = Trade.query.filter(Trade.id == '2').first()
amount = trade.amount
@ -740,20 +763,22 @@ def test_rpc_forcesell(default_conf, ticker, fee, mocker) -> None:
assert cancel_order_mock.call_count == 2
assert trade.amount == amount
freqtradebot.enter_positions()
# make an limit-sell open trade
mocker.patch(
'freqtrade.exchange.Exchange.fetch_order',
return_value={
'status': 'open',
'type': 'limit',
'side': 'sell'
'side': 'sell',
'amount': amount,
'remaining': amount,
'filled': 0.0
}
)
msg = rpc._rpc_forcesell('3')
assert msg == {'result': 'Created sell order for trade 3.'}
# status quo, no exchange calls
assert cancel_order_mock.call_count == 2
assert cancel_order_mock.call_count == 3
def test_performance_handle(default_conf, ticker, limit_buy_order, fee,

57
tests/rpc/test_rpc_apiserver.py
View File

@ -10,10 +10,12 @@ from flask import Flask
from requests.auth import _basic_auth_str
from freqtrade.__init__ import __version__
from freqtrade.loggers import setup_logging, setup_logging_pre
from freqtrade.persistence import Trade
from freqtrade.rpc.api_server import BASE_URI, ApiServer
from freqtrade.state import State
from tests.conftest import get_patched_freqtradebot, log_has, patch_get_signal, create_mock_trades
from tests.conftest import (create_mock_trades, get_patched_freqtradebot,
log_has, patch_get_signal)
_TEST_USER = "FreqTrader"
_TEST_PASS = "SuperSecurePassword1!"
@ -21,6 +23,9 @@ _TEST_PASS = "SuperSecurePassword1!"
@pytest.fixture
def botclient(default_conf, mocker):
setup_logging_pre()
setup_logging(default_conf)
default_conf.update({"api_server": {"enabled": True,
"listen_ip_address": "127.0.0.1",
"listen_port": 8080,
@ -87,20 +92,20 @@ def test_api_unauthorized(botclient):
assert rc.json == {'error': 'Unauthorized'}
# Change only username
ftbot.config['api_server']['username'] = "Ftrader"
ftbot.config['api_server']['username'] = 'Ftrader'
rc = client_get(client, f"{BASE_URI}/version")
assert_response(rc, 401)
assert rc.json == {'error': 'Unauthorized'}
# Change only password
ftbot.config['api_server']['username'] = _TEST_USER
ftbot.config['api_server']['password'] = "WrongPassword"
ftbot.config['api_server']['password'] = 'WrongPassword'
rc = client_get(client, f"{BASE_URI}/version")
assert_response(rc, 401)
assert rc.json == {'error': 'Unauthorized'}
ftbot.config['api_server']['username'] = "Ftrader"
ftbot.config['api_server']['password'] = "WrongPassword"
ftbot.config['api_server']['username'] = 'Ftrader'
ftbot.config['api_server']['password'] = 'WrongPassword'
rc = client_get(client, f"{BASE_URI}/version")
assert_response(rc, 401)
@ -261,7 +266,7 @@ def test_api_reloadconf(botclient):
rc = client_post(client, f"{BASE_URI}/reload_config")
assert_response(rc)
assert rc.json == {'status': 'reloading config ...'}
assert rc.json == {'status': 'Reloading config ...'}
assert ftbot.state == State.RELOAD_CONFIG
@ -423,6 +428,34 @@ def test_api_delete_trade(botclient, mocker, fee, markets):
assert stoploss_mock.call_count == 1
def test_api_logs(botclient):
ftbot, client = botclient
rc = client_get(client, f"{BASE_URI}/logs")
assert_response(rc)
assert len(rc.json) == 2
assert 'logs' in rc.json
# Using a fixed comparison here would make this test fail!
assert rc.json['log_count'] > 1
assert len(rc.json['logs']) == rc.json['log_count']
assert isinstance(rc.json['logs'][0], list)
# date
assert isinstance(rc.json['logs'][0][0], str)
# created_timestamp
assert isinstance(rc.json['logs'][0][1], float)
assert isinstance(rc.json['logs'][0][2], str)
assert isinstance(rc.json['logs'][0][3], str)
assert isinstance(rc.json['logs'][0][4], str)
rc = client_get(client, f"{BASE_URI}/logs?limit=5")
assert_response(rc)
assert len(rc.json) == 2
assert 'logs' in rc.json
# Using a fixed comparison here would make this test fail!
assert rc.json['log_count'] == 5
assert len(rc.json['logs']) == rc.json['log_count']
def test_api_edge_disabled(botclient, mocker, ticker, fee, markets):
ftbot, client = botclient
patch_get_signal(ftbot, (True, False))
@ -438,6 +471,7 @@ def test_api_edge_disabled(botclient, mocker, ticker, fee, markets):
assert rc.json == {"error": "Error querying _edge: Edge is not enabled."}
@pytest.mark.usefixtures("init_persistence")
def test_api_profit(botclient, mocker, ticker, fee, markets, limit_buy_order, limit_sell_order):
ftbot, client = botclient
patch_get_signal(ftbot, (True, False))
@ -465,6 +499,7 @@ def test_api_profit(botclient, mocker, ticker, fee, markets, limit_buy_order, li
assert rc.json['best_pair'] == ''
assert rc.json['best_rate'] == 0
trade = Trade.query.first()
trade.update(limit_sell_order)
trade.close_date = datetime.utcnow()
@ -566,7 +601,8 @@ def test_api_status(botclient, mocker, ticker, fee, markets):
rc = client_get(client, f"{BASE_URI}/status")
assert_response(rc)
assert len(rc.json) == 1
assert rc.json == [{'amount': 91.07468124,
assert rc.json == [{'amount': 91.07468123,
'amount_requested': 91.07468123,
'base_currency': 'BTC',
'close_date': None,
'close_date_hum': None,
@ -599,6 +635,7 @@ def test_api_status(botclient, mocker, ticker, fee, markets):
'initial_stop_loss_ratio': -0.1,
'stoploss_current_dist': -1.1080000000000002e-06,
'stoploss_current_dist_ratio': -0.10081893,
'stoploss_current_dist_pct': -10.08,
'stoploss_entry_dist': -0.00010475,
'stoploss_entry_dist_ratio': -0.10448878,
'trade_id': 1,
@ -675,7 +712,7 @@ def test_api_forcebuy(botclient, mocker, fee):
assert rc.json == {"error": "Error querying _forcebuy: Forcebuy not enabled."}
# enable forcebuy
ftbot.config["forcebuy_enable"] = True
ftbot.config['forcebuy_enable'] = True
fbuy_mock = MagicMock(return_value=None)
mocker.patch("freqtrade.rpc.RPC._rpc_forcebuy", fbuy_mock)
@ -688,6 +725,7 @@ def test_api_forcebuy(botclient, mocker, fee):
fbuy_mock = MagicMock(return_value=Trade(
pair='ETH/ETH',
amount=1,
amount_requested=1,
exchange='bittrex',
stake_amount=1,
open_rate=0.245441,
@ -704,6 +742,7 @@ def test_api_forcebuy(botclient, mocker, fee):
data='{"pair": "ETH/BTC"}')
assert_response(rc)
assert rc.json == {'amount': 1,
'amount_requested': 1,
'trade_id': None,
'close_date': None,
'close_date_hum': None,
@ -740,7 +779,7 @@ def test_api_forcebuy(botclient, mocker, fee):
'min_rate': None,
'open_order_id': '123456',
'open_rate_requested': None,
'open_trade_price': 0.2460546025,
'open_trade_price': 0.24605460,
'sell_reason': None,
'sell_order_status': None,
'strategy': None,

68
tests/rpc/test_rpc_telegram.py
View File

@ -14,8 +14,10 @@ from telegram import Chat, Message, Update
from telegram.error import NetworkError
from freqtrade import __version__
from freqtrade.constants import CANCEL_REASON
from freqtrade.edge import PairInfo
from freqtrade.freqtradebot import FreqtradeBot
from freqtrade.loggers import setup_logging
from freqtrade.persistence import Trade
from freqtrade.rpc import RPCMessageType
from freqtrade.rpc.telegram import Telegram, authorized_only
@ -76,7 +78,7 @@ def test_telegram_init(default_conf, mocker, caplog) -> None:
"['balance'], ['start'], ['stop'], ['forcesell'], ['forcebuy'], ['trades'], "
"['delete'], ['performance'], ['daily'], ['count'], ['reload_config', "
"'reload_conf'], ['show_config', 'show_conf'], ['stopbuy'], "
"['whitelist'], ['blacklist'], ['edge'], ['help'], ['version']]")
"['whitelist'], ['blacklist'], ['logs'], ['edge'], ['help'], ['version']]")
assert log_has(message_str, caplog)
@ -145,7 +147,7 @@ def test_authorized_only_exception(default_conf, mocker, caplog) -> None:
assert log_has('Exception occurred within Telegram module', caplog)
def test_status(default_conf, update, mocker, fee, ticker,) -> None:
def test_telegram_status(default_conf, update, mocker, fee, ticker,) -> None:
update.message.chat.id = "123"
default_conf['telegram']['enabled'] = False
default_conf['telegram']['chat_id'] = "123"
@ -175,6 +177,8 @@ def test_status(default_conf, update, mocker, fee, ticker,) -> None:
'stop_loss': 1.099e-05,
'sell_order_status': None,
'initial_stop_loss_pct': -0.05,
'stoploss_current_dist': 1e-08,
'stoploss_current_dist_pct': -0.02,
'stop_loss_pct': -0.01,
'open_order': '(limit buy rem=0.00000000)'
}]),
@ -688,10 +692,10 @@ def test_reload_config_handle(default_conf, update, mocker) -> None:
telegram._reload_config(update=update, context=MagicMock())
assert freqtradebot.state == State.RELOAD_CONFIG
assert msg_mock.call_count == 1
assert 'reloading config' in msg_mock.call_args_list[0][0][0]
assert 'Reloading config' in msg_mock.call_args_list[0][0][0]
def test_forcesell_handle(default_conf, update, ticker, fee,
def test_telegram_forcesell_handle(default_conf, update, ticker, fee,
ticker_sell_up, mocker) -> None:
mocker.patch('freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', return_value=15000.0)
rpc_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg', MagicMock())
@ -722,7 +726,7 @@ def test_forcesell_handle(default_conf, update, ticker, fee,
context.args = ["1"]
telegram._forcesell(update=update, context=context)
assert rpc_mock.call_count == 2
assert rpc_mock.call_count == 3
last_msg = rpc_mock.call_args_list[-1][0][0]
assert {
'type': RPCMessageType.SELL_NOTIFICATION,
@ -731,7 +735,7 @@ def test_forcesell_handle(default_conf, update, ticker, fee,
'pair': 'ETH/BTC',
'gain': 'profit',
'limit': 1.173e-05,
'amount': 91.07468123861567,
'amount': 91.07468123,
'order_type': 'limit',
'open_rate': 1.098e-05,
'current_rate': 1.173e-05,
@ -745,7 +749,7 @@ def test_forcesell_handle(default_conf, update, ticker, fee,
} == last_msg
def test_forcesell_down_handle(default_conf, update, ticker, fee,
def test_telegram_forcesell_down_handle(default_conf, update, ticker, fee,
ticker_sell_down, mocker) -> None:
mocker.patch('freqtrade.rpc.fiat_convert.CryptoToFiatConverter._find_price',
return_value=15000.0)
@ -781,7 +785,7 @@ def test_forcesell_down_handle(default_conf, update, ticker, fee,
context.args = ["1"]
telegram._forcesell(update=update, context=context)
assert rpc_mock.call_count == 2
assert rpc_mock.call_count == 3
last_msg = rpc_mock.call_args_list[-1][0][0]
assert {
@ -791,7 +795,7 @@ def test_forcesell_down_handle(default_conf, update, ticker, fee,
'pair': 'ETH/BTC',
'gain': 'loss',
'limit': 1.043e-05,
'amount': 91.07468123861567,
'amount': 91.07468123,
'order_type': 'limit',
'open_rate': 1.098e-05,
'current_rate': 1.043e-05,
@ -831,8 +835,9 @@ def test_forcesell_all_handle(default_conf, update, ticker, fee, mocker) -> None
context.args = ["all"]
telegram._forcesell(update=update, context=context)
assert rpc_mock.call_count == 4
msg = rpc_mock.call_args_list[0][0][0]
# Called for each trade 3 times
assert rpc_mock.call_count == 8
msg = rpc_mock.call_args_list[1][0][0]
assert {
'type': RPCMessageType.SELL_NOTIFICATION,
'trade_id': 1,
@ -840,7 +845,7 @@ def test_forcesell_all_handle(default_conf, update, ticker, fee, mocker) -> None
'pair': 'ETH/BTC',
'gain': 'loss',
'limit': 1.099e-05,
'amount': 91.07468123861567,
'amount': 91.07468123,
'order_type': 'limit',
'open_rate': 1.098e-05,
'current_rate': 1.099e-05,
@ -1107,6 +1112,40 @@ def test_blacklist_static(default_conf, update, mocker) -> None:
assert freqtradebot.pairlists.blacklist == ["DOGE/BTC", "HOT/BTC", "ETH/BTC"]
def test_telegram_logs(default_conf, update, mocker) -> None:
msg_mock = MagicMock()
mocker.patch.multiple(
'freqtrade.rpc.telegram.Telegram',
_init=MagicMock(),
_send_msg=msg_mock
)
setup_logging(default_conf)
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
telegram = Telegram(freqtradebot)
context = MagicMock()
context.args = []
telegram._logs(update=update, context=context)
assert msg_mock.call_count == 1
assert "freqtrade\\.rpc\\.telegram" in msg_mock.call_args_list[0][0][0]
msg_mock.reset_mock()
context.args = ["1"]
telegram._logs(update=update, context=context)
assert msg_mock.call_count == 1
msg_mock.reset_mock()
# Test with changed MaxMessageLength
mocker.patch('freqtrade.rpc.telegram.MAX_TELEGRAM_MESSAGE_LENGTH', 200)
context = MagicMock()
context.args = []
telegram._logs(update=update, context=context)
# Called at least 3 times. Exact times will change with unrelated changes to setup messages
# Therefore we don't test for this explicitly.
assert msg_mock.call_count > 3
def test_edge_disabled(default_conf, update, mocker) -> None:
msg_mock = MagicMock()
mocker.patch.multiple(
@ -1306,9 +1345,10 @@ def test_send_msg_buy_cancel_notification(default_conf, mocker) -> None:
'type': RPCMessageType.BUY_CANCEL_NOTIFICATION,
'exchange': 'Bittrex',
'pair': 'ETH/BTC',
'reason': CANCEL_REASON['TIMEOUT']
})
assert msg_mock.call_args[0][0] \
== ('\N{WARNING SIGN} *Bittrex:* Cancelling Open Buy Order for ETH/BTC')
assert (msg_mock.call_args[0][0] == '\N{WARNING SIGN} *Bittrex:* '
'Cancelling open buy Order for ETH/BTC. Reason: cancelled due to timeout.')
def test_send_msg_sell_notification(default_conf, mocker) -> None:

28
tests/strategy/test_interface.py
View File

@ -1,6 +1,7 @@
# pragma pylint: disable=missing-docstring, C0103
import logging
from datetime import datetime, timedelta, timezone
from unittest.mock import MagicMock
import arrow
@ -8,12 +9,12 @@ import pytest
from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider
from freqtrade.data.history import load_data
from freqtrade.exceptions import StrategyError
from freqtrade.persistence import Trade
from freqtrade.resolvers import StrategyResolver
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.data.dataprovider import DataProvider
from tests.conftest import log_has, log_has_re
from .strats.default_strategy import DefaultStrategy
@ -387,6 +388,31 @@ def test_is_pair_locked(default_conf):
strategy.unlock_pair(pair)
assert not strategy.is_pair_locked(pair)
pair = 'BTC/USDT'
# Lock until 14:30
lock_time = datetime(2020, 5, 1, 14, 30, 0, tzinfo=timezone.utc)
strategy.lock_pair(pair, lock_time)
# Lock is in the past ...
assert not strategy.is_pair_locked(pair)
# latest candle is from 14:20, lock goes to 14:30
assert strategy.is_pair_locked(pair, lock_time + timedelta(minutes=-10))
assert strategy.is_pair_locked(pair, lock_time + timedelta(minutes=-50))
# latest candle is from 14:25 (lock should be lifted)
# Since this is the "new candle" available at 14:30
assert not strategy.is_pair_locked(pair, lock_time + timedelta(minutes=-4))
# Should not be locked after time expired
assert not strategy.is_pair_locked(pair, lock_time + timedelta(minutes=10))
# Change timeframe to 15m
strategy.timeframe = '15m'
# Candle from 14:14 - lock goes until 14:30
assert strategy.is_pair_locked(pair, lock_time + timedelta(minutes=-16))
assert strategy.is_pair_locked(pair, lock_time + timedelta(minutes=-15, seconds=-2))
# Candle from 14:15 - lock goes until 14:30
assert not strategy.is_pair_locked(pair, lock_time + timedelta(minutes=-15))
def test_is_informative_pairs_callback(default_conf):
default_conf.update({'strategy': 'TestStrategyLegacy'})

88
tests/strategy/test_strategy_helpers.py Normal file
View File

@ -0,0 +1,88 @@
import pandas as pd
import numpy as np
from freqtrade.strategy import merge_informative_pair, timeframe_to_minutes
def generate_test_data(timeframe: str, size: int):
np.random.seed(42)
tf_mins = timeframe_to_minutes(timeframe)
base = np.random.normal(20, 2, size=size)
date = pd.period_range('2020-07-05', periods=size, freq=f'{tf_mins}min').to_timestamp()
df = pd.DataFrame({
'date': date,
'open': base,
'high': base + np.random.normal(2, 1, size=size),
'low': base - np.random.normal(2, 1, size=size),
'close': base + np.random.normal(0, 1, size=size),
'volume': np.random.normal(200, size=size)
}
)
df = df.dropna()
return df
def test_merge_informative_pair():
data = generate_test_data('15m', 40)
informative = generate_test_data('1h', 40)
result = merge_informative_pair(data, informative, '15m', '1h', ffill=True)
assert isinstance(result, pd.DataFrame)
assert len(result) == len(data)
assert 'date' in result.columns
assert result['date'].equals(data['date'])
assert 'date_1h' in result.columns
assert 'open' in result.columns
assert 'open_1h' in result.columns
assert result['open'].equals(data['open'])
assert 'close' in result.columns
assert 'close_1h' in result.columns
assert result['close'].equals(data['close'])
assert 'volume' in result.columns
assert 'volume_1h' in result.columns
assert result['volume'].equals(data['volume'])
# First 4 rows are empty
assert result.iloc[0]['date_1h'] is pd.NaT
assert result.iloc[1]['date_1h'] is pd.NaT
assert result.iloc[2]['date_1h'] is pd.NaT
assert result.iloc[3]['date_1h'] is pd.NaT
# Next 4 rows contain the starting date (0:00)
assert result.iloc[4]['date_1h'] == result.iloc[0]['date']
assert result.iloc[5]['date_1h'] == result.iloc[0]['date']
assert result.iloc[6]['date_1h'] == result.iloc[0]['date']
assert result.iloc[7]['date_1h'] == result.iloc[0]['date']
# Next 4 rows contain the next Hourly date original date row 4
assert result.iloc[8]['date_1h'] == result.iloc[4]['date']
def test_merge_informative_pair_same():
data = generate_test_data('15m', 40)
informative = generate_test_data('15m', 40)
result = merge_informative_pair(data, informative, '15m', '15m', ffill=True)
assert isinstance(result, pd.DataFrame)
assert len(result) == len(data)
assert 'date' in result.columns
assert result['date'].equals(data['date'])
assert 'date_15m' in result.columns
assert 'open' in result.columns
assert 'open_15m' in result.columns
assert result['open'].equals(data['open'])
assert 'close' in result.columns
assert 'close_15m' in result.columns
assert result['close'].equals(data['close'])
assert 'volume' in result.columns
assert 'volume_15m' in result.columns
assert result['volume'].equals(data['volume'])
# Dates match 1:1
assert result['date_15m'].equals(result['date'])

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