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Merge branch 'develop' into interface_ordertimeoutcallback

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Matthias 2020-03-15 14:56:14 +01:00
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13
.github/workflows/ci.yml vendored
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@ -23,7 +23,7 @@ jobs:
python-version: [3.7, 3.8]
steps:
- uses: actions/checkout@v1
- uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v1
@ -115,10 +115,10 @@ jobs:
strategy:
matrix:
os: [ windows-latest ]
python-version: [3.7]
python-version: [3.7, 3.8]
steps:
- uses: actions/checkout@v1
- uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v1
@ -130,8 +130,7 @@ jobs:
if: startsWith(runner.os, 'Windows')
with:
path: ~\AppData\Local\pip\Cache
key: ${{ runner.os }}-pip
restore-keys: ${{ runner.os }}-pip
key: ${{ matrix.os }}-${{ matrix.python-version }}-pip
- name: Installation
run: |
@ -175,7 +174,7 @@ jobs:
docs_check:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v1
- uses: actions/checkout@v2
- name: Documentation syntax
run: |
@ -195,7 +194,7 @@ jobs:
runs-on: ubuntu-18.04
if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'
steps:
- uses: actions/checkout@v1
- uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v1

5
.travis.yml
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@ -1,4 +1,3 @@
sudo: true
os:
- linux
dist: xenial
@ -11,10 +10,10 @@ env:
global:
- IMAGE_NAME=freqtradeorg/freqtrade
install:
- cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
- cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies; cd ..
- export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
- export TA_LIBRARY_PATH=${HOME}/dependencies/lib
- export TA_INCLUDE_PATH=${HOME}/dependencies/lib/include
- export TA_INCLUDE_PATH=${HOME}/dependencies/include
- pip install -r requirements-dev.txt
- pip install -e .
jobs:

2
Dockerfile
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@ -1,4 +1,4 @@
FROM python:3.8.1-slim-buster
FROM python:3.8.2-slim-buster
RUN apt-get update \
&& apt-get -y install curl build-essential libssl-dev \

3
README.md
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@ -25,7 +25,8 @@ hesitate to read the source code and understand the mechanism of this bot.
## Exchange marketplaces supported
- [X] [Bittrex](https://bittrex.com/)
- [X] [Binance](https://www.binance.com/) ([*Note for binance users](#a-note-on-binance))
- [X] [Binance](https://www.binance.com/) ([*Note for binance users](docs/exchanges.md#blacklists))
- [X] [Kraken](https://kraken.com/)
- [ ] [113 others to tests](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
## Documentation

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build_helpers/TA_Lib-0.4.17-cp38-cp38-win_amd64.whl Normal file
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10
build_helpers/install_windows.ps1
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@ -3,7 +3,15 @@
# Invoke-WebRequest -Uri "https://download.lfd.uci.edu/pythonlibs/xxxxxxx/TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl" -OutFile "TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl"
python -m pip install --upgrade pip
pip install build_helpers\TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl
$pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
if ($pyv -eq '3.7') {
pip install build_helpers\TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl
}
if ($pyv -eq '3.8') {
pip install build_helpers\TA_Lib-0.4.17-cp38-cp38-win_amd64.whl
}
pip install -r requirements-dev.txt
pip install -e .

2
config.json.example
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@ -23,7 +23,7 @@
"ask_strategy":{
"use_order_book": false,
"order_book_min": 1,
"order_book_max": 9,
"order_book_max": 1,
"use_sell_signal": true,
"sell_profit_only": false,
"ignore_roi_if_buy_signal": false

2
config_binance.json.example
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@ -23,7 +23,7 @@
"ask_strategy":{
"use_order_book": false,
"order_book_min": 1,
"order_book_max": 9,
"order_book_max": 1,
"use_sell_signal": true,
"sell_profit_only": false,
"ignore_roi_if_buy_signal": false

4
config_full.json.example
View File

@ -25,6 +25,7 @@
"sell": 30
},
"bid_strategy": {
"price_side": "bid",
"use_order_book": false,
"ask_last_balance": 0.0,
"order_book_top": 1,
@ -34,9 +35,10 @@
}
},
"ask_strategy":{
"price_side": "ask",
"use_order_book": false,
"order_book_min": 1,
"order_book_max": 9,
"order_book_max": 1,
"use_sell_signal": true,
"sell_profit_only": false,
"ignore_roi_if_buy_signal": false

2
config_kraken.json.example
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@ -23,7 +23,7 @@
"ask_strategy":{
"use_order_book": false,
"order_book_min": 1,
"order_book_max": 9,
"order_book_max": 1,
"use_sell_signal": true,
"sell_profit_only": false,
"ignore_roi_if_buy_signal": false

12
docs/backtesting.md
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@ -11,8 +11,8 @@ Now you have good Buy and Sell strategies and some historic data, you want to te
real data. This is what we call
[backtesting](https://en.wikipedia.org/wiki/Backtesting).
Backtesting will use the crypto-currencies (pairs) from your config file and load ticker data from `user_data/data/<exchange>` by default.
If no data is available for the exchange / pair / ticker interval combination, backtesting will ask you to download them first using `freqtrade download-data`.
Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHCLV) data from `user_data/data/<exchange>` by default.
If no data is available for the exchange / pair / timeframe (ticker interval) combination, backtesting will ask you to download them first using `freqtrade download-data`.
For details on downloading, please refer to the [Data Downloading](data-download.md) section in the documentation.
The result of backtesting will confirm if your bot has better odds of making a profit than a loss.
@ -22,19 +22,19 @@ The result of backtesting will confirm if your bot has better odds of making a p
### Run a backtesting against the currencies listed in your config file
#### With 5 min tickers (Per default)
#### With 5 min candle (OHLCV) data (per default)
```bash
freqtrade backtesting
```
#### With 1 min tickers
#### With 1 min candle (OHLCV) data
```bash
freqtrade backtesting --ticker-interval 1m
```
#### Using a different on-disk ticker-data source
#### Using a different on-disk historical candle (OHLCV) data source
Assume you downloaded the history data from the Bittrex exchange and kept it in the `user_data/data/bittrex-20180101` directory.
You can then use this data for backtesting as follows:
@ -223,7 +223,7 @@ You can then load the trades to perform further analysis as shown in our [data a
To compare multiple strategies, a list of Strategies can be provided to backtesting.
This is limited to 1 ticker-interval per run, however, data is only loaded once from disk so if you have multiple
This is limited to 1 timeframe (ticker interval) value per run. However, data is only loaded once from disk so if you have multiple
strategies you'd like to compare, this will give a nice runtime boost.
All listed Strategies need to be in the same directory.

13
docs/bot-usage.md
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@ -275,7 +275,7 @@ Check the corresponding [Data Downloading](data-download.md) section for more de
## Hyperopt commands
To optimize your strategy, you can use hyperopt parameter hyperoptimization
to find optimal parameter values for your stategy.
to find optimal parameter values for your strategy.
```
usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
@ -323,7 +323,7 @@ optional arguments:
--print-all Print all results, not only the best ones.
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--print-json Print best result detailization in JSON format.
--print-json Print best results in JSON format.
-j JOBS, --job-workers JOBS
The number of concurrently running jobs for
hyperoptimization (hyperopt worker processes). If -1
@ -341,10 +341,11 @@ optional arguments:
class (IHyperOptLoss). Different functions can
generate completely different results, since the
target for optimization is different. Built-in
Hyperopt-loss-functions are: DefaultHyperOptLoss,
OnlyProfitHyperOptLoss, SharpeHyperOptLoss,
SharpeHyperOptLossDaily.(default:
`DefaultHyperOptLoss`).
Hyperopt-loss-functions are:
DefaultHyperOptLoss, OnlyProfitHyperOptLoss,
SharpeHyperOptLoss, SharpeHyperOptLossDaily,
SortinoHyperOptLoss, SortinoHyperOptLossDaily.
(default: `DefaultHyperOptLoss`).
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).

109
docs/configuration.md
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@ -47,7 +47,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `amend_last_stake_amount` | Use reduced last stake amount if necessary. [More information below](#configuring-amount-per-trade). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `last_stake_amount_min_ratio` | Defines minimum stake amount that has to be left and executed. Applies only to the last stake amount when it's amended to a reduced value (i.e. if `amend_last_stake_amount` is set to `true`). [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.5`.* <br> **Datatype:** Float (as ratio)
| `amount_reserve_percent` | Reserve some amount in min pair stake amount. The bot will reserve `amount_reserve_percent` + stoploss value when calculating min pair stake amount in order to avoid possible trade refusals. <br>*Defaults to `0.05` (5%).* <br> **Datatype:** Positive Float as ratio.
| `ticker_interval` | The ticker interval to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
| `ticker_interval` | The timeframe (ticker interval) to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
| `fiat_display_currency` | Fiat currency used to show your profits. [More information below](#what-values-can-be-used-for-fiat_display_currency). <br> **Datatype:** String
| `dry_run` | **Required.** Define if the bot must be in Dry Run or production mode. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
| `dry_run_wallet` | Define the starting amount in stake currency for the simulated wallet used by the bot running in the Dry Run mode.<br>*Defaults to `1000`.* <br> **Datatype:** Float
@ -60,11 +60,13 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `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
| `bid_strategy.ask_last_balance` | **Required.** Set the bidding price. More information [below](#buy-price-without-orderbook).
| `bid_strategy.price_side` | Select the side of the spread the bot should look at to get the buy rate. [More information below](#buy-price-side).<br> *Defaults to `bid`.* <br> **Datatype:** String (either `ask` or `bid`).
| `bid_strategy.ask_last_balance` | **Required.** Set the bidding price. More information [below](#buy-price-without-orderbook-enabled).
| `bid_strategy.use_order_book` | Enable buying using the rates in [Order Book Bids](#buy-price-with-orderbook-enabled). <br> **Datatype:** Boolean
| `bid_strategy.order_book_top` | Bot will use the top N rate in Order Book Bids to buy. I.e. a value of 2 will allow the bot to pick the 2nd bid rate in [Order Book Bids](#buy-price-with-orderbook-enabled). <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
| `bid_strategy. check_depth_of_market.enabled` | Do not buy if the difference of buy orders and sell orders is met in Order Book. [Check market depth](#check-depth-of-market). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | The difference ratio of buy orders and sell orders found in Order Book. A value below 1 means sell order size is greater, while value greater than 1 means buy order size is higher. [Check market depth](#check-depth-of-market) <br> *Defaults to `0`.* <br> **Datatype:** Float (as ratio)
| `ask_strategy.price_side` | Select the side of the spread the bot should look at to get the sell rate. [More information below](#sell-price-side).<br> *Defaults to `ask`.* <br> **Datatype:** String (either `ask` or `bid`).
| `ask_strategy.use_order_book` | Enable selling of open trades using [Order Book Asks](#sell-price-with-orderbook-enabled). <br> **Datatype:** Boolean
| `ask_strategy.order_book_min` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
| `ask_strategy.order_book_max` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
@ -111,8 +113,8 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `internals.sd_notify` | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details. <br> **Datatype:** Boolean
| `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> **Datatype:** String
| `user_data_dir` | Directory containing user data. <br> *Defaults to `./user_data/`*. <br> **Datatype:** String
| `dataformat_ohlcv` | Data format to use to store OHLCV historic data. <br> *Defaults to `json`*. <br> **Datatype:** String
| `dataformat_trades` | Data format to use to store trades historic data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
| `dataformat_ohlcv` | Data format to use to store historical candle (OHLCV) data. <br> *Defaults to `json`*. <br> **Datatype:** String
| `dataformat_trades` | Data format to use to store historical trades data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
### Parameters in the strategy
@ -340,7 +342,7 @@ This is most of the time the default time in force. It means the order will rema
on exchange till it is canceled by user. It can be fully or partially fulfilled.
If partially fulfilled, the remaining will stay on the exchange till cancelled.
**FOK (Full Or Kill):**
**FOK (Fill Or Kill):**
It means if the order is not executed immediately AND fully then it is canceled by the exchange.
@ -370,16 +372,18 @@ The possible values are: `gtc` (default), `fok` or `ioc`.
Freqtrade is based on [CCXT library](https://github.com/ccxt/ccxt) that supports over 100 cryptocurrency
exchange markets and trading APIs. The complete up-to-date list can be found in the
[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python). However, the bot was tested
with only Bittrex and Binance.
The bot was tested with the following exchanges:
[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python).
However, the bot was tested by the development team with only Bittrex, Binance and Kraken,
so the these are the only officially supported exhanges:
- [Bittrex](https://bittrex.com/): "bittrex"
- [Binance](https://www.binance.com/): "binance"
- [Kraken](https://kraken.com/): "kraken"
Feel free to test other exchanges and submit your PR to improve the bot.
Some exchanges require special configuration, which can be found on the [Exchange-specific Notes](exchanges.md) documentation page.
#### Sample exchange configuration
A exchange configuration for "binance" would look as follows:
@ -409,7 +413,7 @@ Advanced options can be configured using the `_ft_has_params` setting, which wil
Available options are listed in the exchange-class as `_ft_has_default`.
For example, to test the order type `FOK` with Kraken, and modify candle_limit to 200 (so you only get 200 candles per call):
For example, to test the order type `FOK` with Kraken, and modify candle limit to 200 (so you only get 200 candles per API call):
```json
"exchange": {
@ -461,34 +465,89 @@ Orderbook `bid` (buy) side depth is then divided by the orderbook `ask` (sell) s
!!! Note
A delta value below 1 means that `ask` (sell) orderbook side depth is greater than the depth of the `bid` (buy) orderbook side, while a value greater than 1 means opposite (depth of the buy side is higher than the depth of the sell side).
#### Buy price side
The configuration setting `bid_strategy.price_side` defines the side of the spread the bot looks for when buying.
The following displays an orderbook.
``` explanation
...
103
102
101 # ask
-------------Current spread
99 # bid
98
97
...
```
If `bid_strategy.price_side` is set to `"bid"`, then the bot will use 99 as buying price.
In line with that, if `bid_strategy.price_side` is set to `"ask"`, then the bot will use 101 as buying price.
Using `ask` price often guarantees quicker filled orders, but the bot can also end up paying more than what would have been necessary.
Taker fees instead of maker fees will most likely apply even when using limit buy orders.
Also, prices at the "ask" side of the spread are higher than prices at the "bid" side in the orderbook, so the order behaves similar to a market order (however with a maximum price).
#### Buy price with Orderbook enabled
When buying with the orderbook enabled (`bid_strategy.use_order_book=True`), Freqtrade fetches the `bid_strategy.order_book_top` entries from the orderbook and then uses the entry specified as `bid_strategy.order_book_top` on the `bid` (buy) side of the orderbook. 1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
When buying with the orderbook enabled (`bid_strategy.use_order_book=True`), Freqtrade fetches the `bid_strategy.order_book_top` entries from the orderbook and then uses the entry specified as `bid_strategy.order_book_top` on the configured side (`bid_strategy.price_side`) of the orderbook. 1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
#### Buy price without Orderbook enabled
When not using orderbook (`bid_strategy.use_order_book=False`), Freqtrade uses the best `ask` (sell) price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `ask` price is not below the `last` price), it calculates a rate between `ask` and `last` price.
The following section uses `side` as the configured `bid_strategy.price_side`.
The `bid_strategy.ask_last_balance` configuration parameter controls this. A value of `0.0` will use `ask` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
When not using orderbook (`bid_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price.
Using `ask` price often guarantees quicker success in the bid, but the bot can also end up paying more than what would have been necessary.
The `bid_strategy.ask_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
### Sell price
#### Sell price side
The configuration setting `ask_strategy.price_side` defines the side of the spread the bot looks for when selling.
The following displays an orderbook:
``` explanation
...
103
102
101 # ask
-------------Current spread
99 # bid
98
97
...
```
If `ask_strategy.price_side` is set to `"ask"`, then the bot will use 101 as selling price.
In line with that, if `ask_strategy.price_side` is set to `"bid"`, then the bot will use 99 as selling price.
#### Sell price with Orderbook enabled
When selling with the orderbook enabled (`ask_strategy.use_order_book=True`), Freqtrade fetches the `ask_strategy.order_book_max` entries in the orderbook. Then each of the orderbook steps between `ask_strategy.order_book_min` and `ask_strategy.order_book_max` on the `ask` orderbook side are validated for a profitable sell-possibility based on the strategy configuration and the sell order is placed at the first profitable spot.
When selling with the orderbook enabled (`ask_strategy.use_order_book=True`), Freqtrade fetches the `ask_strategy.order_book_max` entries in the orderbook. Then each of the orderbook steps between `ask_strategy.order_book_min` and `ask_strategy.order_book_max` on the configured orderbook side are validated for a profitable sell-possibility based on the strategy configuration (`minimal_roi` conditions) and the sell order is placed at the first profitable spot.
!!! Note
Using `order_book_max` higher than `order_book_min` only makes sense when ask_strategy.price_side is set to `"ask"`.
The idea here is to place the sell order early, to be ahead in the queue.
A fixed slot (mirroring `bid_strategy.order_book_top`) can be defined by setting `ask_strategy.order_book_min` and `ask_strategy.order_book_max` to the same number.
!!! Warning "Orderbook and stoploss_on_exchange"
Using `ask_strategy.order_book_max` higher than 1 may increase the risk, since an eventual [stoploss on exchange](#understand-order_types) will be needed to be cancelled as soon as the order is placed.
!!! Warning "Order_book_max > 1 - increased risks for stoplosses!"
Using `ask_strategy.order_book_max` higher than 1 will increase the risk the stoploss on exchange is cancelled too early, since an eventual [stoploss on exchange](#understand-order_types) will be cancelled as soon as the order is placed.
Also, the sell order will remain on the exchange for `unfilledtimeout.sell` (or until it's filled) - which can lead to missed stoplosses (with or without using stoploss on exchange).
!!! Warning "Order_book_max > 1 in dry-run"
Using `ask_strategy.order_book_max` higher than 1 will result in improper dry-run results (significantly better than real orders executed on exchange), since dry-run assumes orders to be filled almost instantly.
It is therefore advised to not use this setting for dry-runs.
#### Sell price without Orderbook enabled
When not using orderbook (`ask_strategy.use_order_book=False`), the `bid` price from the ticker will be used as the sell price.
When not using orderbook (`ask_strategy.use_order_book=False`), the price at the `ask_strategy.price_side` side (defaults to `"ask"`) from the ticker will be used as the sell price.
## Pairlists
@ -532,6 +591,12 @@ It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklis
`refresh_period` allows setting the period (in seconds), at which the pairlist will be refreshed. Defaults to 1800s (30 minutes).
`VolumePairList` is based on the ticker data, as reported by the ccxt library:
* The `bidVolume` is the volume (amount) of current best bid in the orderbook.
* The `askVolume` is the volume (amount) of current best ask in the orderbook.
* The `quoteVolume` is the amount of quote (stake) currency traded (bought or sold) in last 24 hours.
```json
"pairlists": [{
"method": "VolumePairList",
@ -626,6 +691,11 @@ In production mode, the bot will engage your money. Be careful, since a wrong
strategy can lose all your money. Be aware of what you are doing when
you run it in production mode.
### Setup your exchange account
You will need to create API Keys (usually you get `key` and `secret`, some exchanges require an additional `password`) from the Exchange website and you'll need to insert this into the appropriate fields in the configuration or when asked by the `freqtrade new-config` command.
API Keys are usually only required for live trading (trading for real money, bot running in "production mode", executing real orders on the exchange) and are not required for the bot running in dry-run (trade simulation) mode. When you setup the bot in dry-run mode, you may fill these fields with empty values.
### To switch your bot in production mode
**Edit your `config.json` file.**
@ -647,9 +717,6 @@ you run it in production mode.
}
```
!!! Note
If you have an exchange API key yet, [see our tutorial](installation.md#setup-your-exchange-account).
You should also make sure to read the [Exchanges](exchanges.md) section of the documentation to be aware of potential configuration details specific to your exchange.
### Using proxy with Freqtrade

12
docs/data-download.md
View File

@ -33,7 +33,7 @@ optional arguments:
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 ohlcv data. (default: `json`).
Storage format for downloaded candle (OHLCV) data. (default: `json`).
--data-format-trades {json,jsongz}
Storage format for downloaded trades data. (default: `jsongz`).
@ -105,7 +105,7 @@ Common arguments:
##### Example converting data
The following command will convert all ohlcv (candle) data available in `~/.freqtrade/data/binance` from json to jsongz, saving diskspace in the process.
The following command will convert all candle (OHLCV) data available in `~/.freqtrade/data/binance` from json to jsongz, saving diskspace in the process.
It'll also remove original json data files (`--erase` parameter).
``` bash
@ -192,15 +192,15 @@ Then run:
freqtrade download-data --exchange binance
```
This will download ticker data for all the currency pairs you defined in `pairs.json`.
This will download historical candle (OHLCV) data for all the currency pairs you defined in `pairs.json`.
### Other Notes
- To use a different directory than the exchange specific default, use `--datadir user_data/data/some_directory`.
- To change the exchange used to download the tickers, please use a different configuration file (you'll probably need to adjust ratelimits etc.)
- To change the exchange used to download the historical data from, please use a different configuration file (you'll probably need to adjust ratelimits etc.)
- To use `pairs.json` from some other directory, use `--pairs-file some_other_dir/pairs.json`.
- To download ticker data for only 10 days, use `--days 10` (defaults to 30 days).
- Use `--timeframes` to specify which tickers to download. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute tickers.
- To download historical candle (OHLCV) data for only 10 days, use `--days 10` (defaults to 30 days).
- Use `--timeframes` to specify what timeframe download the historical candle (OHLCV) data for. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute data.
- To use exchange, timeframe and list of pairs as defined in your configuration file, use the `-c/--config` option. With this, the script uses the whitelist defined in the config as the list of currency pairs to download data for and does not require the pairs.json file. You can combine `-c/--config` with most other options.
### Trades (tick) data

13
docs/developer.md
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@ -165,7 +165,7 @@ Since CCXT does not provide unification for Stoploss On Exchange yet, we'll need
### Incomplete candles
While fetching OHLCV data, we're may end up getting incomplete candles (Depending on the exchange).
While fetching candle (OHLCV) data, we may end up getting incomplete candles (depending on the exchange).
To demonstrate this, we'll use daily candles (`"1d"`) to keep things simple.
We query the api (`ct.fetch_ohlcv()`) for the timeframe and look at the date of the last entry. If this entry changes or shows the date of a "incomplete" candle, then we should drop this since having incomplete candles is problematic because indicators assume that only complete candles are passed to them, and will generate a lot of false buy signals. By default, we're therefore removing the last candle assuming it's incomplete.
@ -174,14 +174,14 @@ To check how the new exchange behaves, you can use the following snippet:
``` python
import ccxt
from datetime import datetime
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.data.converter import ohlcv_to_dataframe
ct = ccxt.binance()
timeframe = "1d"
pair = "XLM/BTC" # Make sure to use a pair that exists on that exchange!
raw = ct.fetch_ohlcv(pair, timeframe=timeframe)
# convert to dataframe
df1 = parse_ticker_dataframe(raw, timeframe, pair=pair, drop_incomplete=False)
df1 = ohlcv_to_dataframe(raw, timeframe, pair=pair, drop_incomplete=False)
print(df1.tail(1))
print(datetime.utcnow())
@ -234,7 +234,7 @@ 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.
* 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.
* 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
@ -268,11 +268,6 @@ Once the PR against master is merged (best right after merging):
* Use "master" as reference (this step comes after the above PR is merged).
* Use the above changelog as release comment (as codeblock)
### After-release
* Update version in develop by postfixing that with `-dev` (`2019.6 -> 2019.6-dev`).
* Create a PR against develop to update that branch.
## Releases
### pypi

2
docs/edge.md
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@ -156,7 +156,7 @@ Edge module has following configuration options:
| `minimum_winrate` | It filters out pairs which don't have at least minimum_winrate. <br>This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio. <br>*Defaults to `0.60`.* <br> **Datatype:** Float
| `minimum_expectancy` | It filters out pairs which have the expectancy lower than this number. <br>Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return. <br>*Defaults to `0.20`.* <br> **Datatype:** Float
| `min_trade_number` | When calculating *W*, *R* and *E* (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable. <br>Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something. <br>*Defaults to `10` (it is highly recommended not to decrease this number).* <br> **Datatype:** Integer
| `max_trade_duration_minute` | Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br>**NOTICE:** While configuring this value, you should take into consideration your ticker interval. As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).<br>*Defaults to `1440` (one day).* <br> **Datatype:** Integer
| `max_trade_duration_minute` | Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br>**NOTICE:** While configuring this value, you should take into consideration your timeframe (ticker interval). As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).<br>*Defaults to `1440` (one day).* <br> **Datatype:** Integer
| `remove_pumps` | Edge will remove sudden pumps in a given market while going through historical data. However, given that pumps happen very often in crypto markets, we recommend you keep this off.<br>*Defaults to `false`.* <br> **Datatype:** Boolean
## Running Edge independently

9
docs/exchanges.md
View File

@ -62,6 +62,11 @@ res = [ f"{x['MarketCurrency']}/{x['BaseCurrency']}" for x in ct.publicGetMarket
print(res)
```
## All exchanges
Should you experience constant errors with Nonce (like `InvalidNonce`), it is best to regenerate the API keys. Resetting Nonce is difficult and it's usually easier to regenerate the API keys.
## Random notes for other exchanges
* The Ocean (exchange id: `theocean`) exchange uses Web3 functionality and requires `web3` python package to be installed:
@ -71,8 +76,8 @@ $ pip3 install web3
### Send incomplete candles to the strategy
Most exchanges return incomplete candles via their ohlcv / klines interface.
By default, Freqtrade assumes that incomplete candles are returned and removes the last candle assuming it's an incomplete candle.
Most exchanges return current incomplete candle via their OHLCV/klines API interface.
By default, Freqtrade assumes that incomplete candle is fetched from the exchange and removes the last candle assuming it's the incomplete candle.
Whether your exchange returns incomplete candles or not can be checked using [the helper script](developer.md#Incomplete-candles) from the Contributor documentation.

38
docs/hyperopt.md
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@ -31,9 +31,9 @@ This will create a new hyperopt file from a template, which will be located unde
Depending on the space you want to optimize, only some of the below are required:
* fill `buy_strategy_generator` - for buy signal optimization
* fill `indicator_space` - for buy signal optimzation
* fill `indicator_space` - for buy signal optimization
* fill `sell_strategy_generator` - for sell signal optimization
* fill `sell_indicator_space` - for sell signal optimzation
* fill `sell_indicator_space` - for sell signal optimization
!!! Note
`populate_indicators` needs to create all indicators any of thee spaces may use, otherwise hyperopt will not work.
@ -81,11 +81,11 @@ There are two places you need to change in your hyperopt file to add a new buy h
There you have two different types of indicators: 1. `guards` and 2. `triggers`.
1. Guards are conditions like "never buy if ADX < 10", or never buy if current price is over EMA10.
2. Triggers are ones that actually trigger buy in specific moment, like "buy when EMA5 crosses over EMA10" or "buy when close price touches lower bollinger band".
2. Triggers are ones that actually trigger buy in specific moment, like "buy when EMA5 crosses over EMA10" or "buy when close price touches lower Bollinger band".
Hyperoptimization will, for each eval round, pick one trigger and possibly
multiple guards. The constructed strategy will be something like
"*buy exactly when close price touches lower bollinger band, BUT only if
"*buy exactly when close price touches lower Bollinger band, BUT only if
ADX > 10*".
If you have updated the buy strategy, i.e. changed the contents of
@ -103,9 +103,10 @@ Place the corresponding settings into the following methods
The configuration and rules are the same than for buy signals.
To avoid naming collisions in the search-space, please prefix all sell-spaces with `sell-`.
#### Using ticker-interval as part of the Strategy
#### Using timeframe as a part of the Strategy
The Strategy exposes the ticker-interval as `self.ticker_interval`. The same value is available as class-attribute `HyperoptName.ticker_interval`.
The Strategy class exposes the timeframe (ticker interval) value as the `self.ticker_interval` attribute.
The same value is available as class-attribute `HyperoptName.ticker_interval`.
In the case of the linked sample-value this would be `SampleHyperOpt.ticker_interval`.
## Solving a Mystery
@ -159,6 +160,9 @@ So let's write the buy strategy using these values:
dataframe['macd'], dataframe['macdsignal']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
@ -172,7 +176,7 @@ So let's write the buy strategy using these values:
Hyperopting will now call this `populate_buy_trend` as many times you ask it (`epochs`)
with different value combinations. It will then use the given historical data and make
buys based on the buy signals generated with the above function and based on the results
it will end with telling you which paramter combination produced the best profits.
it will end with telling you which parameter combination produced the best profits.
The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators.
When you want to test an indicator that isn't used by the bot currently, remember to
@ -191,8 +195,10 @@ Currently, the following loss functions are builtin:
* `DefaultHyperOptLoss` (default legacy Freqtrade hyperoptimization loss function)
* `OnlyProfitHyperOptLoss` (which takes only amount of profit into consideration)
* `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on the trade returns)
* `SharpeHyperOptLossDaily` (optimizes Sharpe Ratio calculated on daily trade returns)
* `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on trade returns relative to standard deviation)
* `SharpeHyperOptLossDaily` (optimizes Sharpe Ratio calculated on **daily** trade returns relative to standard deviation)
* `SortinoHyperOptLoss` (optimizes Sortino Ratio calculated on trade returns relative to **downside** standard deviation)
* `SortinoHyperOptLossDaily` (optimizes Sortino Ratio calculated on **daily** trade returns relative to **downside** standard deviation)
Creation of a custom loss function is covered in the [Advanced Hyperopt](advanced-hyperopt.md) part of the documentation.
@ -220,11 +226,11 @@ The `--spaces all` option determines that all possible parameters should be opti
!!! Warning
When switching parameters or changing configuration options, make sure to not use the argument `--continue` so temporary results can be removed.
### Execute Hyperopt with Different Ticker-Data Source
### Execute Hyperopt with different historical data source
If you would like to hyperopt parameters using an alternate ticker data that
you have on-disk, use the `--datadir PATH` option. Default hyperopt will
use data from directory `user_data/data`.
If you would like to hyperopt parameters using an alternate historical data set that
you have on-disk, use the `--datadir PATH` option. By default, hyperopt
uses data from directory `user_data/data`.
### Running Hyperopt with Smaller Testset
@ -272,7 +278,7 @@ In some situations, you may need to run Hyperopt (and Backtesting) with the
By default, hyperopt emulates the behavior of the Freqtrade Live Run/Dry Run, where only one
open trade is allowed for every traded pair. The total number of trades open for all pairs
is also limited by the `max_open_trades` setting. During Hyperopt/Backtesting this may lead to
some potential trades to be hidden (or masked) by previosly open trades.
some potential trades to be hidden (or masked) by previously open trades.
The `--eps`/`--enable-position-stacking` argument allows emulation of buying the same pair multiple times,
while `--dmmp`/`--disable-max-market-positions` disables applying `max_open_trades`
@ -378,7 +384,7 @@ As stated in the comment, you can also use it as the value of the `minimal_roi`
#### Default ROI Search Space
If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the ticker_interval used. By default the values vary in the following ranges (for some of the most used ticker intervals, values are rounded to 5 digits after the decimal point):
If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the ticker_interval used. By default the values vary in the following ranges (for some of the most used timeframes, values are rounded to 5 digits after the decimal point):
| # step | 1m | | 5m | | 1h | | 1d | |
| ------ | ------ | ----------------- | -------- | ----------- | ---------- | ----------------- | ------------ | ----------------- |
@ -387,7 +393,7 @@ If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace f
| 3 | 4...20 | 0.00387...0.01547 | 20...100 | 0.01...0.04 | 240...1200 | 0.02294...0.09177 | 5760...28800 | 0.04059...0.16237 |
| 4 | 6...44 | 0.0 | 30...220 | 0.0 | 360...2640 | 0.0 | 8640...63360 | 0.0 |
These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the ticker interval used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the ticker interval used.
These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the timeframe (ticker interval) used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the timeframe used.
If you have the `generate_roi_table()` and `roi_space()` methods in your custom hyperopt file, remove them in order to utilize these adaptive ROI tables and the ROI hyperoptimization space generated by Freqtrade by default.

20
docs/installation.md
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@ -2,6 +2,8 @@
This page explains how to prepare your environment for running the bot.
Please consider using the prebuilt [docker images](docker.md) to get started quickly while trying out freqtrade evaluating how it operates.
## Prerequisite
### Requirements
@ -14,15 +16,7 @@ Click each one for install guide:
* [virtualenv](https://virtualenv.pypa.io/en/stable/installation/) (Recommended)
* [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html) (install instructions below)
### API keys
Before running your bot in production you will need to setup few
external API. In production mode, the bot will require valid Exchange API
credentials. We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot) (optional but recommended).
### Setup your exchange account
You will need to create API Keys (Usually you get `key` and `secret`) from the Exchange website and insert this into the appropriate fields in the configuration or when asked by the installation script.
We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot), which is optional but recommended.
## Quick start
@ -65,11 +59,11 @@ usage:
** --install **
With this option, the script will install everything you need to run the bot:
With this option, the script will install the bot and most dependencies:
You will need to have git and python3.6+ installed beforehand for this to work.
* Mandatory software as: `ta-lib`
* Setup your virtualenv
* Configure your `config.json` file
* Setup your virtualenv under `.env/`
This option is a combination of installation tasks, `--reset` and `--config`.
@ -83,7 +77,7 @@ This option will hard reset your branch (only if you are on either `master` or `
** --config **
Use this option to configure the `config.json` configuration file. The script will interactively ask you questions to setup your bot and create your `config.json`.
DEPRECATED - use `freqtrade new-config -c config.json` instead.
------

1
docs/plotting.md
View File

@ -196,6 +196,7 @@ The first graph is good to get a grip of how the overall market progresses.
The second graph will show if your algorithm works or doesn't.
Perhaps you want an algorithm that steadily makes small profits, or one that acts less often, but makes big swings.
This graph will also highlight the start (and end) of the Max drawdown period.
The third graph can be useful to spot outliers, events in pairs that cause profit spikes.

34
docs/strategy-customization.md
View File

@ -83,7 +83,7 @@ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
@ -248,6 +248,23 @@ minimal_roi = {
While technically not completely disabled, this would sell once the trade reaches 10000% Profit.
To use times based on candle duration (ticker_interval or 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 ...)
``` python
from freqtrade.exchange import timeframe_to_minutes
class AwesomeStrategy(IStrategy):
ticker_interval = "1d"
ticker_interval_mins = timeframe_to_minutes(ticker_interval)
minimal_roi = {
"0": 0.05, # 5% for the first 3 candles
str(ticker_interval_mins * 3)): 0.02, # 2% after 3 candles
str(ticker_interval_mins * 6)): 0.01, # 1% After 6 candles
}
```
### Stoploss
Setting a stoploss is highly recommended to protect your capital from strong moves against you.
@ -266,13 +283,14 @@ 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).
### Ticker interval
### Timeframe (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.
Please note that the same buy/sell signals may work with one interval, but not the other.
This setting is accessible within the strategy by using `self.ticker_interval`.
Please note that the same buy/sell signals may work well with one timeframe, but not with the others.
This setting is accessible within the strategy methods as the `self.ticker_interval` attribute.
### Metadata dict
@ -317,14 +335,14 @@ Please always check the mode of operation to select the correct method to get da
#### Possible options for DataProvider
- `available_pairs` - Property with tuples listing cached pairs with their intervals (pair, interval).
- `ohlcv(pair, timeframe)` - Currently cached ticker data for the pair, returns DataFrame or empty DataFrame.
- `ohlcv(pair, timeframe)` - Currently cached candle (OHLCV) data for the pair, returns DataFrame or empty DataFrame.
- `historic_ohlcv(pair, timeframe)` - Returns historical data stored on disk.
- `get_pair_dataframe(pair, 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).
- `orderbook(pair, maximum)` - Returns latest orderbook data for the pair, a dict with bids/asks with a total of `maximum` entries.
- `market(pair)` - Returns market data for the pair: fees, limits, precisions, activity flag, etc. See [ccxt documentation](https://github.com/ccxt/ccxt/wiki/Manual#markets) for more details on Market data structure.
- `runmode` - Property containing the current runmode.
#### Example: fetch live ohlcv / historic data for the first informative pair
#### Example: fetch live / historical candle (OHLCV) data for the first informative pair
``` python
if self.dp:
@ -359,8 +377,8 @@ if self.dp:
``` python
if self.dp:
for pair, ticker in self.dp.available_pairs:
print(f"available {pair}, {ticker}")
for pair, timeframe in self.dp.available_pairs:
print(f"available {pair}, {timeframe}")
```
#### Get data for non-tradeable pairs

6
docs/strategy_analysis_example.md
View File

@ -121,7 +121,6 @@ from freqtrade.data.btanalysis import analyze_trade_parallelism
# Analyze the above
parallel_trades = analyze_trade_parallelism(trades, '5m')
parallel_trades.plot()
```
@ -134,11 +133,14 @@ Freqtrade offers interactive plotting capabilities based on plotly.
from freqtrade.plot.plotting import generate_candlestick_graph
# Limit graph period to keep plotly quick and reactive
# Filter trades to one pair
trades_red = trades.loc[trades['pair'] == pair]
data_red = data['2019-06-01':'2019-06-10']
# Generate candlestick graph
graph = generate_candlestick_graph(pair=pair,
data=data_red,
trades=trades,
trades=trades_red,
indicators1=['sma20', 'ema50', 'ema55'],
indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']
)

16
docs/utils.md
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@ -61,8 +61,8 @@ $ freqtrade new-config --config config_binance.json
? Do you want to enable Dry-run (simulated trades)? Yes
? Please insert your stake currency: BTC
? Please insert your stake amount: 0.05
? Please insert max_open_trades (Integer or 'unlimited'): 5
? Please insert your ticker interval: 15m
? Please insert max_open_trades (Integer or 'unlimited'): 3
? Please insert your timeframe (ticker interval): 5m
? Please insert your display Currency (for reporting): USD
? Select exchange binance
? Do you want to enable Telegram? No
@ -264,7 +264,7 @@ All exchanges supported by the ccxt library: _1btcxe, acx, adara, allcoin, anxpr
## List Timeframes
Use the `list-timeframes` subcommand to see the list of ticker intervals (timeframes) available for the exchange.
Use the `list-timeframes` subcommand to see the list of timeframes (ticker intervals) available for the exchange.
```
usage: freqtrade list-timeframes [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [--exchange EXCHANGE] [-1]
@ -435,6 +435,7 @@ usage: freqtrade hyperopt-list [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[--min-total-profit FLOAT]
[--max-total-profit FLOAT] [--no-color]
[--print-json] [--no-details]
[--export-csv FILE]
optional arguments:
-h, --help show this help message and exit
@ -456,6 +457,8 @@ optional arguments:
useful if you are redirecting output to a file.
--print-json Print best result detailization in JSON format.
--no-details Do not print best epoch details.
--export-csv FILE Export to CSV-File. This will disable table print.
Example: --export-csv hyperopt.csv
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
@ -464,9 +467,10 @@ 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

8
docs/webhook-config.md
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@ -23,12 +23,12 @@ Sample configuration (tested using IFTTT).
"webhooksell": {
"value1": "Selling {pair}",
"value2": "limit {limit:8f}",
"value3": "profit: {profit_amount:8f} {stake_currency}"
"value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
},
"webhooksellcancel": {
"value1": "Cancelling Open Sell Order for {pair}",
"value2": "limit {limit:8f}",
"value3": "profit: {profit_amount:8f} {stake_currency}"
"value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
},
"webhookstatus": {
"value1": "Status: {status}",
@ -87,7 +87,7 @@ Possible parameters are:
* `open_rate`
* `current_rate`
* `profit_amount`
* `profit_percent`
* `profit_ratio`
* `stake_currency`
* `fiat_currency`
* `sell_reason`
@ -108,7 +108,7 @@ Possible parameters are:
* `open_rate`
* `current_rate`
* `profit_amount`
* `profit_percent`
* `profit_ratio`
* `stake_currency`
* `fiat_currency`
* `sell_reason`

2
environment.yml
View File

@ -45,7 +45,7 @@ dependencies:
- pip:
# Required for app
- cython
- coinmarketcap
- pycoingecko
- ccxt
- TA-Lib
- py_find_1st

7
freqtrade/commands/arguments.py
View File

@ -69,7 +69,8 @@ ARGS_HYPEROPT_LIST = ["hyperopt_list_best", "hyperopt_list_profitable",
"hyperopt_list_min_avg_time", "hyperopt_list_max_avg_time",
"hyperopt_list_min_avg_profit", "hyperopt_list_max_avg_profit",
"hyperopt_list_min_total_profit", "hyperopt_list_max_total_profit",
"print_colorized", "print_json", "hyperopt_list_no_details"]
"print_colorized", "print_json", "hyperopt_list_no_details",
"export_csv"]
ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperopt_show_index",
"print_json", "hyperopt_show_no_header"]
@ -296,7 +297,7 @@ class Arguments:
# Add convert-data subcommand
convert_data_cmd = subparsers.add_parser(
'convert-data',
help='Convert OHLCV data from one format to another.',
help='Convert candle (OHLCV) data from one format to another.',
parents=[_common_parser],
)
convert_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=True))
@ -305,7 +306,7 @@ class Arguments:
# Add convert-trade-data subcommand
convert_trade_data_cmd = subparsers.add_parser(
'convert-trade-data',
help='Convert trade-data from one format to another.',
help='Convert trade data from one format to another.',
parents=[_common_parser],
)
convert_trade_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=False))

2
freqtrade/commands/build_config_commands.py
View File

@ -76,7 +76,7 @@ def ask_user_config() -> Dict[str, Any]:
{
"type": "text",
"name": "ticker_interval",
"message": "Please insert your ticker interval:",
"message": "Please insert your timeframe (ticker interval):",
"default": "5m",
},
{

12
freqtrade/commands/cli_options.py
View File

@ -221,6 +221,13 @@ AVAILABLE_CLI_OPTIONS = {
action='store_true',
default=False,
),
"export_csv": Arg(
'--export-csv',
help='Export to CSV-File.'
' This will disable table print.'
' Example: --export-csv hyperopt.csv',
metavar='FILE',
),
"hyperopt_jobs": Arg(
'-j', '--job-workers',
help='The number of concurrently running jobs for hyperoptimization '
@ -257,7 +264,8 @@ AVAILABLE_CLI_OPTIONS = {
help='Specify the class name of the hyperopt loss function class (IHyperOptLoss). '
'Different functions can generate completely different results, '
'since the target for optimization is different. Built-in Hyperopt-loss-functions are: '
'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss, SharpeHyperOptLossDaily.'
'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss, SharpeHyperOptLossDaily, '
'SortinoHyperOptLoss, SortinoHyperOptLossDaily.'
'(default: `%(default)s`).',
metavar='NAME',
default=constants.DEFAULT_HYPEROPT_LOSS,
@ -347,7 +355,7 @@ AVAILABLE_CLI_OPTIONS = {
),
"dataformat_ohlcv": Arg(
'--data-format-ohlcv',
help='Storage format for downloaded ohlcv data. (default: `%(default)s`).',
help='Storage format for downloaded candle (OHLCV) data. (default: `%(default)s`).',
choices=constants.AVAILABLE_DATAHANDLERS,
default='json'
),

30
freqtrade/commands/hyperopt_commands.py
View File

@ -21,6 +21,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
print_colorized = config.get('print_colorized', False)
print_json = config.get('print_json', False)
export_csv = config.get('export_csv', None)
no_details = config.get('hyperopt_list_no_details', False)
no_header = False
@ -46,18 +47,13 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
trials = _hyperopt_filter_trials(trials, filteroptions)
# TODO: fetch the interval for epochs to print from the cli option
epoch_start, epoch_stop = 0, None
if print_colorized:
colorama_init(autoreset=True)
if not export_csv:
try:
# Human-friendly indexes used here (starting from 1)
for val in trials[epoch_start:epoch_stop]:
Hyperopt.print_results_explanation(val, total_epochs,
not filteroptions['only_best'], print_colorized)
Hyperopt.print_result_table(config, trials, total_epochs,
not filteroptions['only_best'], print_colorized, 0)
except KeyboardInterrupt:
print('User interrupted..')
@ -66,6 +62,11 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
results = sorted_trials[0]
Hyperopt.print_epoch_details(results, total_epochs, print_json, no_header)
if trials and export_csv:
Hyperopt.export_csv_file(
config, trials, total_epochs, not filteroptions['only_best'], export_csv
)
def start_hyperopt_show(args: Dict[str, Any]) -> None:
"""
@ -75,6 +76,12 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
print_json = config.get('print_json', False)
no_header = config.get('hyperopt_show_no_header', False)
trials_file = (config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_results.pickle')
n = config.get('hyperopt_show_index', -1)
filteroptions = {
'only_best': config.get('hyperopt_list_best', False),
'only_profitable': config.get('hyperopt_list_profitable', False),
@ -87,10 +94,6 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None)
}
no_header = config.get('hyperopt_show_no_header', False)
trials_file = (config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_results.pickle')
# Previous evaluations
trials = Hyperopt.load_previous_results(trials_file)
@ -99,7 +102,6 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
trials = _hyperopt_filter_trials(trials, filteroptions)
trials_epochs = len(trials)
n = config.get('hyperopt_show_index', -1)
if n > trials_epochs:
raise OperationalException(
f"The index of the epoch to show should be less than {trials_epochs + 1}.")
@ -111,8 +113,6 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
if n > 0:
n -= 1
print_json = config.get('print_json', False)
if trials:
val = trials[n]
Hyperopt.print_epoch_details(val, total_epochs, print_json, no_header,

4
freqtrade/commands/list_commands.py
View File

@ -58,7 +58,7 @@ def _print_objs_tabular(objs: List, print_colorized: bool) -> None:
else yellow + "DUPLICATE NAME" + reset)
} for s in objs]
print(tabulate(objss_to_print, headers='keys', tablefmt='pipe'))
print(tabulate(objss_to_print, headers='keys', tablefmt='psql', stralign='right'))
def start_list_strategies(args: Dict[str, Any]) -> None:
@ -192,7 +192,7 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
else:
# print data as a table, with the human-readable summary
print(f"{summary_str}:")
print(tabulate(tabular_data, headers='keys', tablefmt='pipe'))
print(tabulate(tabular_data, headers='keys', tablefmt='psql', stralign='right'))
elif not (args.get('print_one_column', False) or
args.get('list_pairs_print_json', False) or
args.get('print_csv', False)):

13
freqtrade/commands/optimize_commands.py
View File

@ -17,10 +17,15 @@ def setup_optimize_configuration(args: Dict[str, Any], method: RunMode) -> Dict[
"""
config = setup_utils_configuration(args, method)
if method == RunMode.BACKTEST:
if config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT:
raise DependencyException('stake amount could not be "%s" for backtesting' %
constants.UNLIMITED_STAKE_AMOUNT)
no_unlimited_runmodes = {
RunMode.BACKTEST: 'backtesting',
RunMode.HYPEROPT: 'hyperoptimization',
}
if (method in no_unlimited_runmodes.keys() and
config['stake_amount'] == constants.UNLIMITED_STAKE_AMOUNT):
raise DependencyException(
f'The value of `stake_amount` cannot be set as "{constants.UNLIMITED_STAKE_AMOUNT}" '
f'for {no_unlimited_runmodes[method]}')
return config

12
freqtrade/configuration/config_validation.py
View File

@ -150,15 +150,3 @@ def _validate_whitelist(conf: Dict[str, Any]) -> None:
if (pl.get('method') == 'StaticPairList'
and not conf.get('exchange', {}).get('pair_whitelist')):
raise OperationalException("StaticPairList requires pair_whitelist to be set.")
if pl.get('method') == 'StaticPairList':
stake = conf['stake_currency']
invalid_pairs = []
for pair in conf['exchange'].get('pair_whitelist'):
if not pair.endswith(f'/{stake}'):
invalid_pairs.append(pair)
if invalid_pairs:
raise OperationalException(
f"Stake-currency '{stake}' not compatible with pair-whitelist. "
f"Please remove the following pairs: {invalid_pairs}")

18
freqtrade/configuration/configuration.py
View File

@ -96,6 +96,8 @@ class Configuration:
# Keep a copy of the original configuration file
config['original_config'] = deepcopy(config)
self._process_logging_options(config)
self._process_runmode(config)
self._process_common_options(config)
@ -146,8 +148,6 @@ class Configuration:
def _process_common_options(self, config: Dict[str, Any]) -> None:
self._process_logging_options(config)
# 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")})
@ -167,10 +167,6 @@ class Configuration:
if 'sd_notify' in self.args and self.args["sd_notify"]:
config['internals'].update({'sd_notify': True})
self._args_to_config(config, argname='dry_run',
logstring='Parameter --dry-run detected, '
'overriding dry_run to: {} ...')
def _process_datadir_options(self, config: Dict[str, Any]) -> None:
"""
Extract information for sys.argv and load directory configurations
@ -200,6 +196,7 @@ class Configuration:
if self.args.get('exportfilename'):
self._args_to_config(config, argname='exportfilename',
logstring='Storing backtest results to {} ...')
config['exportfilename'] = Path(config['exportfilename'])
else:
config['exportfilename'] = (config['user_data_dir']
/ 'backtest_results/backtest-result.json')
@ -286,6 +283,9 @@ class Configuration:
self._args_to_config(config, argname='print_json',
logstring='Parameter --print-json detected ...')
self._args_to_config(config, argname='export_csv',
logstring='Parameter --export-csv detected: {}')
self._args_to_config(config, argname='hyperopt_jobs',
logstring='Parameter -j/--job-workers detected: {}')
@ -376,10 +376,14 @@ class Configuration:
def _process_runmode(self, config: Dict[str, Any]) -> None:
self._args_to_config(config, argname='dry_run',
logstring='Parameter --dry-run detected, '
'overriding dry_run to: {} ...')
if not self.runmode:
# Handle real mode, infer dry/live from config
self.runmode = RunMode.DRY_RUN if config.get('dry_run', True) else RunMode.LIVE
logger.info(f"Runmode set to {self.runmode}.")
logger.info(f"Runmode set to {self.runmode.value}.")
config.update({'runmode': self.runmode})

2
freqtrade/configuration/timerange.py
View File

@ -45,7 +45,7 @@ class TimeRange:
"""
Adjust startts by <startup_candles> candles.
Applies only if no startup-candles have been available.
:param timeframe_secs: Ticker timeframe in seconds e.g. `timeframe_to_seconds('5m')`
:param timeframe_secs: Timeframe in seconds e.g. `timeframe_to_seconds('5m')`
:param startup_candles: Number of candles to move start-date forward
:param min_date: Minimum data date loaded. Key kriterium to decide if start-time
has to be moved

10
freqtrade/constants.py
View File

@ -15,6 +15,7 @@ UNLIMITED_STAKE_AMOUNT = 'unlimited'
DEFAULT_AMOUNT_RESERVE_PERCENT = 0.05
REQUIRED_ORDERTIF = ['buy', 'sell']
REQUIRED_ORDERTYPES = ['buy', 'sell', 'stoploss', 'stoploss_on_exchange']
ORDERBOOK_SIDES = ['ask', 'bid']
ORDERTYPE_POSSIBILITIES = ['limit', 'market']
ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
@ -42,7 +43,7 @@ SUPPORTED_FIAT = [
"EUR", "GBP", "HKD", "HUF", "IDR", "ILS", "INR", "JPY",
"KRW", "MXN", "MYR", "NOK", "NZD", "PHP", "PKR", "PLN",
"RUB", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR", "USD",
"BTC", "XBT", "ETH", "XRP", "LTC", "BCH", "USDT"
"BTC", "ETH", "XRP", "LTC", "BCH"
]
MINIMAL_CONFIG = {
@ -113,6 +114,8 @@ CONF_SCHEMA = {
'minimum': 0,
'maximum': 1,
'exclusiveMaximum': False,
},
'price_side': {'type': 'string', 'enum': ORDERBOOK_SIDES, 'default': 'bid'},
'use_order_book': {'type': 'boolean'},
'order_book_top': {'type': 'integer', 'maximum': 20, 'minimum': 1},
'check_depth_of_market': {
@ -123,12 +126,12 @@ CONF_SCHEMA = {
}
},
},
},
'required': ['ask_last_balance']
},
'ask_strategy': {
'type': 'object',
'properties': {
'price_side': {'type': 'string', 'enum': ORDERBOOK_SIDES, 'default': 'ask'},
'use_order_book': {'type': 'boolean'},
'order_book_min': {'type': 'integer', 'minimum': 1},
'order_book_max': {'type': 'integer', 'minimum': 1, 'maximum': 50},
@ -251,7 +254,6 @@ CONF_SCHEMA = {
'type': 'array',
'items': {
'type': 'string',
'pattern': '^[0-9A-Z]+/[0-9A-Z]+$'
},
'uniqueItems': True
},
@ -259,7 +261,6 @@ CONF_SCHEMA = {
'type': 'array',
'items': {
'type': 'string',
'pattern': '^[0-9A-Z]+/[0-9A-Z]+$'
},
'uniqueItems': True
},
@ -301,6 +302,7 @@ SCHEMA_TRADE_REQUIRED = [
'last_stake_amount_min_ratio',
'dry_run',
'dry_run_wallet',
'ask_strategy',
'bid_strategy',
'unfilledtimeout',
'stoploss',

43
freqtrade/data/btanalysis.py
View File

@ -3,7 +3,7 @@ Helpers when analyzing backtest data
"""
import logging
from pathlib import Path
from typing import Dict, Union
from typing import Dict, Union, Tuple
import numpy as np
import pandas as pd
@ -129,16 +129,20 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
return trades
def load_trades(source: str, db_url: str, exportfilename: str) -> pd.DataFrame:
def load_trades(source: str, db_url: str, exportfilename: Path) -> pd.DataFrame:
"""
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
:param db_url: sqlalchemy formatted url to a database
:param exportfilename: Json file generated by backtesting
:return: DataFrame containing trades
"""
if source == "DB":
return load_trades_from_db(db_url)
elif source == "file":
return load_backtest_data(Path(exportfilename))
return load_backtest_data(exportfilename)
def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame) -> pd.DataFrame:
@ -151,17 +155,17 @@ def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame) -> p
return trades
def combine_tickers_with_mean(tickers: Dict[str, pd.DataFrame],
def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
column: str = "close") -> pd.DataFrame:
"""
Combine multiple dataframes "column"
:param tickers: Dict of Dataframes, dict key should be pair.
:param data: Dict of Dataframes, dict key should be pair.
:param column: Column in the original dataframes to use
:return: DataFrame with the column renamed to the dict key, and a column
named mean, containing the mean of all pairs.
"""
df_comb = pd.concat([tickers[pair].set_index('date').rename(
{column: pair}, axis=1)[pair] for pair in tickers], axis=1)
df_comb = pd.concat([data[pair].set_index('date').rename(
{column: pair}, axis=1)[pair] for pair in data], axis=1)
df_comb['mean'] = df_comb.mean(axis=1)
@ -188,3 +192,28 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
# FFill to get continuous
df[col_name] = df[col_name].ffill()
return df
def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_time',
value_col: str = 'profitperc'
) -> Tuple[float, pd.Timestamp, pd.Timestamp]:
"""
Calculate max drawdown and the corresponding close dates
:param trades: DataFrame containing trades (requires columns close_time and profitperc)
:param date_col: Column in DataFrame to use for dates (defaults to 'close_time')
:param value_col: Column in DataFrame to use for values (defaults to 'profitperc')
:return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time
:raise: ValueError if trade-dataframe was found empty.
"""
if len(trades) == 0:
raise ValueError("Trade dataframe empty.")
profit_results = trades.sort_values(date_col)
max_drawdown_df = pd.DataFrame()
max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
high_date = profit_results.loc[max_drawdown_df['high_value'].idxmax(), date_col]
low_date = profit_results.loc[max_drawdown_df['drawdown'].idxmin(), date_col]
return abs(min(max_drawdown_df['drawdown'])), high_date, low_date

53
freqtrade/data/converter.py
View File

@ -13,12 +13,12 @@ from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
logger = logging.getLogger(__name__)
def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
fill_missing: bool = True,
drop_incomplete: bool = True) -> DataFrame:
def ohlcv_to_dataframe(ohlcv: list, timeframe: str, pair: str, *,
fill_missing: bool = True, drop_incomplete: bool = True) -> DataFrame:
"""
Converts a ticker-list (format ccxt.fetch_ohlcv) to a Dataframe
:param ticker: ticker list, as returned by exchange.async_get_candle_history
Converts a list with candle (OHLCV) data (in format returned by ccxt.fetch_ohlcv)
to a Dataframe
:param ohlcv: list with candle (OHLCV) data, as returned by exchange.async_get_candle_history
:param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
:param pair: Pair this data is for (used to warn if fillup was necessary)
:param fill_missing: fill up missing candles with 0 candles
@ -26,21 +26,18 @@ def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
:param drop_incomplete: Drop the last candle of the dataframe, assuming it's incomplete
:return: DataFrame
"""
logger.debug("Parsing tickerlist to dataframe")
logger.debug(f"Converting candle (OHLCV) data to dataframe for pair {pair}.")
cols = DEFAULT_DATAFRAME_COLUMNS
frame = DataFrame(ticker, columns=cols)
df = DataFrame(ohlcv, columns=cols)
frame['date'] = to_datetime(frame['date'],
unit='ms',
utc=True,
infer_datetime_format=True)
df['date'] = to_datetime(df['date'], unit='ms', utc=True, infer_datetime_format=True)
# Some exchanges return int values for volume and even for ohlc.
# Some exchanges return int values for Volume and even for OHLC.
# Convert them since TA-LIB indicators used in the strategy assume floats
# and fail with exception...
frame = frame.astype(dtype={'open': 'float', 'high': 'float', 'low': 'float', 'close': 'float',
df = df.astype(dtype={'open': 'float', 'high': 'float', 'low': 'float', 'close': 'float',
'volume': 'float'})
return clean_ohlcv_dataframe(frame, timeframe, pair,
return clean_ohlcv_dataframe(df, timeframe, pair,
fill_missing=fill_missing,
drop_incomplete=drop_incomplete)
@ -49,11 +46,11 @@ def clean_ohlcv_dataframe(data: DataFrame, timeframe: str, pair: str, *,
fill_missing: bool = True,
drop_incomplete: bool = True) -> DataFrame:
"""
Clense a ohlcv dataframe by
Clense a OHLCV dataframe by
* Grouping it by date (removes duplicate tics)
* dropping last candles if requested
* Filling up missing data (if requested)
:param data: DataFrame containing ohlcv data.
:param data: DataFrame containing candle (OHLCV) data.
:param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
:param pair: Pair this data is for (used to warn if fillup was necessary)
:param fill_missing: fill up missing candles with 0 candles
@ -88,16 +85,16 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
"""
from freqtrade.exchange import timeframe_to_minutes
ohlc_dict = {
ohlcv_dict = {
'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last',
'volume': 'sum'
}
ticker_minutes = timeframe_to_minutes(timeframe)
timeframe_minutes = timeframe_to_minutes(timeframe)
# Resample to create "NAN" values
df = dataframe.resample(f'{ticker_minutes}min', on='date').agg(ohlc_dict)
df = dataframe.resample(f'{timeframe_minutes}min', on='date').agg(ohlcv_dict)
# Forwardfill close for missing columns
df['close'] = df['close'].fillna(method='ffill')
@ -159,20 +156,20 @@ def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
def trades_to_ohlcv(trades: list, timeframe: str) -> DataFrame:
"""
Converts trades list to ohlcv list
Converts trades list to OHLCV list
TODO: This should get a dedicated test
:param trades: List of trades, as returned by ccxt.fetch_trades.
:param timeframe: Ticker timeframe to resample data to
:return: ohlcv Dataframe.
:param timeframe: Timeframe to resample data to
:return: OHLCV Dataframe.
"""
from freqtrade.exchange import timeframe_to_minutes
ticker_minutes = timeframe_to_minutes(timeframe)
timeframe_minutes = timeframe_to_minutes(timeframe)
df = pd.DataFrame(trades)
df['datetime'] = pd.to_datetime(df['datetime'])
df = df.set_index('datetime')
df_new = df['price'].resample(f'{ticker_minutes}min').ohlc()
df_new['volume'] = df['amount'].resample(f'{ticker_minutes}min').sum()
df_new = df['price'].resample(f'{timeframe_minutes}min').ohlc()
df_new['volume'] = df['amount'].resample(f'{timeframe_minutes}min').sum()
df_new['date'] = df_new.index
# Drop 0 volume rows
df_new = df_new.dropna()
@ -206,7 +203,7 @@ def convert_trades_format(config: Dict[str, Any], convert_from: str, convert_to:
def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
"""
Convert ohlcv from one format to another format.
Convert OHLCV from one format to another
:param config: Config dictionary
:param convert_from: Source format
:param convert_to: Target format
@ -216,7 +213,7 @@ def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to:
src = get_datahandler(config['datadir'], convert_from)
trg = get_datahandler(config['datadir'], convert_to)
timeframes = config.get('timeframes', [config.get('ticker_interval')])
logger.info(f"Converting OHLCV for timeframe {timeframes}")
logger.info(f"Converting candle (OHLCV) for timeframe {timeframes}")
if 'pairs' not in config:
config['pairs'] = []
@ -224,7 +221,7 @@ def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to:
for timeframe in timeframes:
config['pairs'].extend(src.ohlcv_get_pairs(config['datadir'],
timeframe))
logger.info(f"Converting OHLCV for {config['pairs']}")
logger.info(f"Converting candle (OHLCV) data for {config['pairs']}")
for timeframe in timeframes:
for pair in config['pairs']:

14
freqtrade/data/dataprovider.py
View File

@ -1,7 +1,7 @@
"""
Dataprovider
Responsible to provide data to the bot
including Klines, tickers, historic data
including ticker and orderbook data, live and historical candle (OHLCV) data
Common Interface for bot and strategy to access data.
"""
import logging
@ -43,10 +43,10 @@ class DataProvider:
def ohlcv(self, pair: str, timeframe: str = None, copy: bool = True) -> DataFrame:
"""
Get ohlcv data for the given pair as DataFrame
Get candle (OHLCV) data for the given pair as DataFrame
Please use the `available_pairs` method to verify which pairs are currently cached.
:param pair: pair to get the data for
:param timeframe: Ticker timeframe to get data for
:param timeframe: Timeframe to get data for
:param copy: copy dataframe before returning if True.
Use False only for read-only operations (where the dataframe is not modified)
"""
@ -58,7 +58,7 @@ class DataProvider:
def historic_ohlcv(self, pair: str, timeframe: str = None) -> DataFrame:
"""
Get stored historic ohlcv data
Get stored historical candle (OHLCV) data
:param pair: pair to get the data for
:param timeframe: timeframe to get data for
"""
@ -69,17 +69,17 @@ class DataProvider:
def get_pair_dataframe(self, pair: str, timeframe: str = None) -> DataFrame:
"""
Return pair ohlcv data, either live or cached historical -- depending
Return pair candle (OHLCV) data, either live or cached historical -- depending
on the runmode.
:param pair: pair to get the data for
:param timeframe: timeframe to get data for
:return: Dataframe for this pair
"""
if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
# Get live ohlcv data.
# Get live OHLCV data.
data = self.ohlcv(pair=pair, timeframe=timeframe)
else:
# Get historic ohlcv data (cached on disk).
# Get historical OHLCV data (cached on disk).
data = self.historic_ohlcv(pair=pair, timeframe=timeframe)
if len(data) == 0:
logger.warning(f"No data found for ({pair}, {timeframe}).")

20
freqtrade/data/history/history_utils.py
View File

@ -9,7 +9,7 @@ from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
from freqtrade.data.converter import parse_ticker_dataframe, trades_to_ohlcv
from freqtrade.data.converter import ohlcv_to_dataframe, trades_to_ohlcv
from freqtrade.data.history.idatahandler import IDataHandler, get_datahandler
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import Exchange
@ -28,10 +28,10 @@ def load_pair_history(pair: str,
data_handler: IDataHandler = None,
) -> DataFrame:
"""
Load cached ticker history for the given pair.
Load cached ohlcv history for the given pair.
:param pair: Pair to load data for
:param timeframe: Ticker timeframe (e.g. "5m")
:param timeframe: Timeframe (e.g. "5m")
:param datadir: Path to the data storage location.
:param data_format: Format of the data. Ignored if data_handler is set.
:param timerange: Limit data to be loaded to this timerange
@ -63,10 +63,10 @@ def load_data(datadir: Path,
data_format: str = 'json',
) -> Dict[str, DataFrame]:
"""
Load ticker history data for a list of pairs.
Load ohlcv history data for a list of pairs.
:param datadir: Path to the data storage location.
:param timeframe: Ticker Timeframe (e.g. "5m")
:param timeframe: Timeframe (e.g. "5m")
:param pairs: List of pairs to load
:param timerange: Limit data to be loaded to this timerange
:param fill_up_missing: Fill missing values with "No action"-candles
@ -104,10 +104,10 @@ def refresh_data(datadir: Path,
timerange: Optional[TimeRange] = None,
) -> None:
"""
Refresh ticker history data for a list of pairs.
Refresh ohlcv history data for a list of pairs.
:param datadir: Path to the data storage location.
:param timeframe: Ticker Timeframe (e.g. "5m")
:param timeframe: Timeframe (e.g. "5m")
:param pairs: List of pairs to load
:param exchange: Exchange object
:param timerange: Limit data to be loaded to this timerange
@ -165,7 +165,7 @@ def _download_pair_history(datadir: Path,
Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
:param pair: pair to download
:param timeframe: Ticker Timeframe (e.g 5m)
:param timeframe: Timeframe (e.g "5m")
:param timerange: range of time to download
:return: bool with success state
"""
@ -194,7 +194,7 @@ def _download_pair_history(datadir: Path,
days=-30).float_timestamp) * 1000
)
# TODO: Maybe move parsing to exchange class (?)
new_dataframe = parse_ticker_dataframe(new_data, timeframe, pair,
new_dataframe = ohlcv_to_dataframe(new_data, timeframe, pair,
fill_missing=False, drop_incomplete=True)
if data.empty:
data = new_dataframe
@ -362,7 +362,7 @@ def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,
:param pair: pair used for log output.
:param min_date: start-date of the data
:param max_date: end-date of the data
:param timeframe_min: ticker Timeframe in minutes
:param timeframe_min: Timeframe in minutes
"""
# total difference in minutes / timeframe-minutes
expected_frames = int((max_date - min_date).total_seconds() // 60 // timeframe_min)

34
freqtrade/data/history/idatahandler.py
View File

@ -55,7 +55,7 @@ class IDataHandler(ABC):
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: Ticker timeframe (e.g. "5m")
: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.
@ -67,7 +67,7 @@ class IDataHandler(ABC):
"""
Remove data for this pair
:param pair: Delete data for this pair.
:param timeframe: Ticker timeframe (e.g. "5m")
:param timeframe: Timeframe (e.g. "5m")
:return: True when deleted, false if file did not exist.
"""
@ -129,10 +129,10 @@ class IDataHandler(ABC):
warn_no_data: bool = True
) -> DataFrame:
"""
Load cached ticker history for the given pair.
Load cached candle (OHLCV) data for the given pair.
:param pair: Pair to load data for
:param timeframe: Ticker timeframe (e.g. "5m")
:param timeframe: Timeframe (e.g. "5m")
:param timerange: Limit data to be loaded to this timerange
:param fill_missing: Fill missing values with "No action"-candles
:param drop_incomplete: Drop last candle assuming it may be incomplete.
@ -147,12 +147,7 @@ class IDataHandler(ABC):
pairdf = self._ohlcv_load(pair, timeframe,
timerange=timerange_startup)
if pairdf.empty:
if warn_no_data:
logger.warning(
f'No history data for pair: "{pair}", timeframe: {timeframe}. '
'Use `freqtrade download-data` to download the data'
)
if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
return pairdf
else:
enddate = pairdf.iloc[-1]['date']
@ -160,13 +155,30 @@ class IDataHandler(ABC):
if timerange_startup:
self._validate_pairdata(pair, pairdf, timerange_startup)
pairdf = trim_dataframe(pairdf, timerange_startup)
if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
return pairdf
# incomplete candles should only be dropped if we didn't trim the end beforehand.
return clean_ohlcv_dataframe(pairdf, timeframe,
pairdf = clean_ohlcv_dataframe(pairdf, timeframe,
pair=pair,
fill_missing=fill_missing,
drop_incomplete=(drop_incomplete and
enddate == pairdf.iloc[-1]['date']))
self._check_empty_df(pairdf, pair, timeframe, warn_no_data)
return pairdf
def _check_empty_df(self, pairdf: DataFrame, pair: str, timeframe: str, warn_no_data: bool):
"""
Warn on empty dataframe
"""
if pairdf.empty:
if warn_no_data:
logger.warning(
f'No history data for pair: "{pair}", timeframe: {timeframe}. '
'Use `freqtrade download-data` to download the data'
)
return True
return False
def _validate_pairdata(self, pair, pairdata: DataFrame, timerange: TimeRange):
"""

6
freqtrade/data/history/jsondatahandler.py
View File

@ -60,7 +60,7 @@ class JsonDataHandler(IDataHandler):
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: Ticker timeframe (e.g. "5m")
: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.
@ -71,6 +71,8 @@ class JsonDataHandler(IDataHandler):
return DataFrame(columns=self._columns)
pairdata = read_json(filename, orient='values')
pairdata.columns = self._columns
pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
'low': 'float', 'close': 'float', 'volume': 'float'})
pairdata['date'] = to_datetime(pairdata['date'],
unit='ms',
utc=True,
@ -81,7 +83,7 @@ class JsonDataHandler(IDataHandler):
"""
Remove data for this pair
:param pair: Delete data for this pair.
:param timeframe: Ticker timeframe (e.g. "5m")
: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)

30
freqtrade/edge/edge_positioning.py
View File

@ -119,7 +119,7 @@ class Edge:
logger.critical("No data found. Edge is stopped ...")
return False
preprocessed = self.strategy.tickerdata_to_dataframe(data)
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
# Print timeframe
min_date, max_date = history.get_timerange(preprocessed)
@ -137,10 +137,10 @@ class Edge:
pair_data = pair_data.sort_values(by=['date'])
pair_data = pair_data.reset_index(drop=True)
ticker_data = self.strategy.advise_sell(
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
trades += self._find_trades_for_stoploss_range(ticker_data, pair, self._stoploss_range)
trades += self._find_trades_for_stoploss_range(df_analyzed, pair, self._stoploss_range)
# If no trade found then exit
if len(trades) == 0:
@ -246,7 +246,8 @@ class Edge:
# we set stake amount to an arbitrary amount.
# as it doesn't change the calculation.
# all returned values are relative. they are percentages.
# all returned values are relative.
# they are defined as ratios.
stake = 0.015
fee = self.fee
open_fee = fee / 2
@ -269,8 +270,8 @@ class Edge:
result['sell_fee'] = result['sell_sum'] * close_fee
result['sell_take'] = result['sell_sum'] - result['sell_fee']
# profit_percent
result['profit_percent'] = (result['sell_take'] - result['buy_spend']) / result['buy_spend']
# profit_ratio
result['profit_ratio'] = (result['sell_take'] - result['buy_spend']) / result['buy_spend']
# Absolute profit
result['profit_abs'] = result['sell_take'] - result['buy_spend']
@ -316,7 +317,7 @@ class Edge:
}
# Group by (pair and stoploss) by applying above aggregator
df = results.groupby(['pair', 'stoploss'])['profit_abs', 'trade_duration'].agg(
df = results.groupby(['pair', 'stoploss'])[['profit_abs', 'trade_duration']].agg(
groupby_aggregator).reset_index(col_level=1)
# Dropping level 0 as we don't need it
@ -358,11 +359,11 @@ class Edge:
# Returning a list of pairs in order of "expectancy"
return final
def _find_trades_for_stoploss_range(self, ticker_data, pair, stoploss_range):
buy_column = ticker_data['buy'].values
sell_column = ticker_data['sell'].values
date_column = ticker_data['date'].values
ohlc_columns = ticker_data[['open', 'high', 'low', 'close']].values
def _find_trades_for_stoploss_range(self, df, pair, stoploss_range):
buy_column = df['buy'].values
sell_column = df['sell'].values
date_column = df['date'].values
ohlc_columns = df[['open', 'high', 'low', 'close']].values
result: list = []
for stoploss in stoploss_range:
@ -399,9 +400,8 @@ class Edge:
# trade opens in reality on the next candle
open_trade_index += 1
stop_price_percentage = stoploss + 1
open_price = ohlc_columns[open_trade_index, 0]
stop_price = (open_price * stop_price_percentage)
stop_price = (open_price * (stoploss + 1))
# Searching for the index where stoploss is hit
stop_index = utf1st.find_1st(
@ -441,7 +441,7 @@ class Edge:
trade = {'pair': pair,
'stoploss': stoploss,
'profit_percent': '',
'profit_ratio': '',
'profit_abs': '',
'open_time': date_column[open_trade_index],
'close_time': date_column[exit_index],

117
freqtrade/exchange/exchange.py
View File

@ -18,7 +18,7 @@ from ccxt.base.decimal_to_precision import (ROUND_DOWN, ROUND_UP, TICK_SIZE,
TRUNCATE, decimal_to_precision)
from pandas import DataFrame
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.data.converter import ohlcv_to_dataframe
from freqtrade.exceptions import (DependencyException, InvalidOrderException,
OperationalException, TemporaryError)
from freqtrade.exchange.common import BAD_EXCHANGES, retrier, retrier_async
@ -66,8 +66,6 @@ class Exchange:
self._config.update(config)
self._cached_ticker: Dict[str, Any] = {}
# Holds last candle refreshed time of each pair
self._pairs_last_refresh_time: Dict[Tuple[str, str], int] = {}
# Timestamp of last markets refresh
@ -228,6 +226,18 @@ class Exchange:
markets = self.markets
return sorted(set([x['quote'] for _, x in markets.items()]))
def get_pair_quote_currency(self, pair: str) -> str:
"""
Return a pair's quote currency
"""
return self.markets.get(pair, {}).get('quote', '')
def get_pair_base_currency(self, pair: str) -> str:
"""
Return a pair's quote currency
"""
return self.markets.get(pair, {}).get('base', '')
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]
@ -300,7 +310,7 @@ class Exchange:
if not self.markets:
logger.warning('Unable to validate pairs (assuming they are correct).')
return
invalid_pairs = []
for pair in pairs:
# Note: ccxt has BaseCurrency/QuoteCurrency format for pairs
# TODO: add a support for having coins in BTC/USDT format
@ -322,6 +332,13 @@ class Exchange:
logger.warning(f"Pair {pair} is restricted for some users on this exchange."
f"Please check if you are impacted by this restriction "
f"on the exchange and eventually remove {pair} from your whitelist.")
if (self._config['stake_currency'] and
self.get_pair_quote_currency(pair) != self._config['stake_currency']):
invalid_pairs.append(pair)
if invalid_pairs:
raise OperationalException(
f"Stake-currency '{self._config['stake_currency']}' not compatible with "
f"pair-whitelist. Please remove the following pairs: {invalid_pairs}")
def get_valid_pair_combination(self, curr_1: str, curr_2: str) -> str:
"""
@ -334,7 +351,7 @@ class Exchange:
def validate_timeframes(self, timeframe: Optional[str]) -> None:
"""
Checks if ticker interval from config is a supported timeframe on the exchange
Check if timeframe from config is a supported timeframe on the exchange
"""
if not hasattr(self._api, "timeframes") or self._api.timeframes is None:
# If timeframes attribute is missing (or is None), the exchange probably
@ -347,7 +364,7 @@ class Exchange:
if timeframe and (timeframe not in self.timeframes):
raise OperationalException(
f"Invalid ticker interval '{timeframe}'. This exchange supports: {self.timeframes}")
f"Invalid timeframe '{timeframe}'. This exchange supports: {self.timeframes}")
if timeframe and timeframe_to_minutes(timeframe) < 1:
raise OperationalException(
@ -582,7 +599,7 @@ class Exchange:
return self._api.fetch_tickers()
except ccxt.NotSupported as e:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching tickers in batch.'
f'Exchange {self._api.name} does not support fetching tickers in batch. '
f'Message: {e}') from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
@ -591,39 +608,28 @@ class Exchange:
raise OperationalException(e) from e
@retrier
def fetch_ticker(self, pair: str, refresh: Optional[bool] = True) -> dict:
if refresh or pair not in self._cached_ticker.keys():
def fetch_ticker(self, pair: str) -> dict:
try:
if pair not in self._api.markets or not self._api.markets[pair].get('active'):
raise DependencyException(f"Pair {pair} not available")
data = self._api.fetch_ticker(pair)
try:
self._cached_ticker[pair] = {
'bid': float(data['bid']),
'ask': float(data['ask']),
}
except KeyError:
logger.debug("Could not cache ticker data for %s", pair)
return data
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not load ticker due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
else:
logger.info("returning cached ticker-data for %s", pair)
return self._cached_ticker[pair]
def get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int) -> List:
"""
Gets candle history using asyncio and returns the list of candles.
Handles all async doing.
Async over one pair, assuming we get `_ohlcv_candle_limit` candles per call.
Get candle history using asyncio and returns the list of candles.
Handles all async work for this.
Async over one pair, assuming we get `self._ohlcv_candle_limit` candles per call.
:param pair: Pair to download
:param timeframe: Ticker Timeframe to get
:param timeframe: Timeframe to get data for
:param since_ms: Timestamp in milliseconds to get history from
:returns List of tickers
:returns List with candle (OHLCV) data
"""
return asyncio.get_event_loop().run_until_complete(
self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
@ -643,26 +649,27 @@ class Exchange:
pair, timeframe, since) for since in
range(since_ms, arrow.utcnow().timestamp * 1000, one_call)]
tickers = await asyncio.gather(*input_coroutines, return_exceptions=True)
results = await asyncio.gather(*input_coroutines, return_exceptions=True)
# Combine tickers
# Combine gathered results
data: List = []
for p, timeframe, ticker in tickers:
for p, timeframe, res in results:
if p == pair:
data.extend(ticker)
data.extend(res)
# Sort data again after extending the result - above calls return in "async order"
data = sorted(data, key=lambda x: x[0])
logger.info("downloaded %s with length %s.", pair, len(data))
logger.info("Downloaded data for %s with length %s.", pair, len(data))
return data
def refresh_latest_ohlcv(self, pair_list: List[Tuple[str, str]]) -> List[Tuple[str, List]]:
"""
Refresh in-memory ohlcv asynchronously and set `_klines` with the result
Refresh in-memory OHLCV asynchronously and set `_klines` with the result
Loops asynchronously over pair_list and downloads all pairs async (semi-parallel).
Only used in the dataprovider.refresh() method.
:param pair_list: List of 2 element tuples containing pair, interval to refresh
:return: Returns a List of ticker-dataframes.
:return: TODO: return value is only used in the tests, get rid of it
"""
logger.debug("Refreshing ohlcv data for %d pairs", len(pair_list))
logger.debug("Refreshing candle (OHLCV) data for %d pairs", len(pair_list))
input_coroutines = []
@ -673,15 +680,15 @@ class Exchange:
input_coroutines.append(self._async_get_candle_history(pair, timeframe))
else:
logger.debug(
"Using cached ohlcv data for pair %s, timeframe %s ...",
"Using cached candle (OHLCV) data for pair %s, timeframe %s ...",
pair, timeframe
)
tickers = asyncio.get_event_loop().run_until_complete(
results = asyncio.get_event_loop().run_until_complete(
asyncio.gather(*input_coroutines, return_exceptions=True))
# handle caching
for res in tickers:
for res in results:
if isinstance(res, Exception):
logger.warning("Async code raised an exception: %s", res.__class__.__name__)
continue
@ -692,13 +699,14 @@ class Exchange:
if ticks:
self._pairs_last_refresh_time[(pair, timeframe)] = ticks[-1][0] // 1000
# keeping parsed dataframe in cache
self._klines[(pair, timeframe)] = parse_ticker_dataframe(
self._klines[(pair, timeframe)] = ohlcv_to_dataframe(
ticks, timeframe, pair=pair, fill_missing=True,
drop_incomplete=self._ohlcv_partial_candle)
return tickers
return results
def _now_is_time_to_refresh(self, pair: str, timeframe: str) -> bool:
# Calculating ticker interval in seconds
# Timeframe in seconds
interval_in_sec = timeframe_to_seconds(timeframe)
return not ((self._pairs_last_refresh_time.get((pair, timeframe), 0)
@ -708,11 +716,11 @@ class Exchange:
async def _async_get_candle_history(self, pair: str, timeframe: str,
since_ms: Optional[int] = None) -> Tuple[str, str, List]:
"""
Asynchronously gets candle histories using fetch_ohlcv
Asynchronously get candle history data using fetch_ohlcv
returns tuple: (pair, timeframe, ohlcv_list)
"""
try:
# fetch ohlcv asynchronously
# Fetch OHLCV asynchronously
s = '(' + arrow.get(since_ms // 1000).isoformat() + ') ' if since_ms is not None else ''
logger.debug(
"Fetching pair %s, interval %s, since %s %s...",
@ -722,9 +730,9 @@ class Exchange:
data = await self._api_async.fetch_ohlcv(pair, timeframe=timeframe,
since=since_ms)
# Because some exchange sort Tickers ASC and other DESC.
# Ex: Bittrex returns a list of tickers ASC (oldest first, newest last)
# when GDAX returns a list of tickers DESC (newest first, oldest last)
# Some exchanges sort OHLCV in ASC order and others in DESC.
# Ex: Bittrex returns the list of OHLCV in ASC order (oldest first, newest last)
# while GDAX returns the list of OHLCV in DESC order (newest first, oldest last)
# Only sort if necessary to save computing time
try:
if data and data[0][0] > data[-1][0]:
@ -737,14 +745,15 @@ class Exchange:
except ccxt.NotSupported as e:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching historical candlestick data.'
f'Message: {e}') from e
f'Exchange {self._api.name} does not support fetching historical '
f'candle (OHLCV) data. Message: {e}') from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(f'Could not load ticker history for pair {pair} due to '
f'{e.__class__.__name__}. Message: {e}') from e
raise TemporaryError(f'Could not fetch historical candle (OHLCV) data '
f'for pair {pair} due to {e.__class__.__name__}. '
f'Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(f'Could not fetch ticker data for pair {pair}. '
f'Msg: {e}') from e
raise OperationalException(f'Could not fetch historical candle (OHLCV) data '
f'for pair {pair}. Message: {e}') from e
@retrier_async
async def _async_fetch_trades(self, pair: str,
@ -877,14 +886,14 @@ class Exchange:
until: Optional[int] = None,
from_id: Optional[str] = None) -> Tuple[str, List]:
"""
Gets candle history using asyncio and returns the list of candles.
Handles all async doing.
Async over one pair, assuming we get `_ohlcv_candle_limit` candles per call.
Get trade history data using asyncio.
Handles all async work and returns the list of candles.
Async over one pair, assuming we get `self._ohlcv_candle_limit` candles per call.
:param pair: Pair to download
:param since: Timestamp in milliseconds to get history from
:param until: Timestamp in milliseconds. Defaults to current timestamp if not defined.
:param from_id: Download data starting with ID (if id is known)
:returns List of tickers
:returns List of trade data
"""
if not self.exchange_has("fetchTrades"):
raise OperationalException("This exchange does not suport downloading Trades.")
@ -1018,7 +1027,7 @@ def is_exchange_known_ccxt(exchange_name: str, ccxt_module: CcxtModuleType = Non
def is_exchange_officially_supported(exchange_name: str) -> bool:
return exchange_name in ['bittrex', 'binance']
return exchange_name in ['bittrex', 'binance', 'kraken']
def ccxt_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:

132
freqtrade/freqtradebot.py
View File

@ -6,11 +6,11 @@ import logging
import traceback
from datetime import datetime
from math import isclose
from os import getpid
from threading import Lock
from typing import Any, Dict, List, Optional, Tuple
import arrow
from cachetools import TTLCache
from requests.exceptions import RequestException
from freqtrade import __version__, constants, persistence
@ -53,9 +53,8 @@ class FreqtradeBot:
# Init objects
self.config = config
self._heartbeat_msg = 0
self.heartbeat_interval = self.config.get('internals', {}).get('heartbeat_interval', 60)
self._sell_rate_cache = TTLCache(maxsize=100, ttl=5)
self._buy_rate_cache = TTLCache(maxsize=100, ttl=5)
self.strategy: IStrategy = StrategyResolver.load_strategy(self.config)
@ -160,11 +159,6 @@ class FreqtradeBot:
self.check_handle_timedout()
Trade.session.flush()
if (self.heartbeat_interval
and (arrow.utcnow().timestamp - self._heartbeat_msg > self.heartbeat_interval)):
logger.info(f"Bot heartbeat. PID={getpid()}")
self._heartbeat_msg = arrow.utcnow().timestamp
def _refresh_whitelist(self, trades: List[Trade] = []) -> List[str]:
"""
Refresh whitelist from pairlist or edge and extend it with trades.
@ -179,8 +173,8 @@ class FreqtradeBot:
_whitelist = self.edge.adjust(_whitelist)
if trades:
# Extend active-pair whitelist with pairs from open trades
# It ensures that tickers are downloaded for open trades
# Extend active-pair whitelist with pairs of open trades
# It ensures that candle (OHLCV) data are downloaded for open trades as well
_whitelist.extend([trade.pair for trade in trades if trade.pair not in _whitelist])
return _whitelist
@ -235,35 +229,43 @@ class FreqtradeBot:
return trades_created
def get_buy_rate(self, pair: str, refresh: bool, tick: Dict = None) -> float:
def get_buy_rate(self, pair: str, refresh: bool) -> float:
"""
Calculates bid target between current ask price and last price
:param pair: Pair to get rate for
:param refresh: allow cached data
:return: float: Price
"""
config_bid_strategy = self.config.get('bid_strategy', {})
if 'use_order_book' in config_bid_strategy and\
config_bid_strategy.get('use_order_book', False):
logger.info('Getting price from order book')
order_book_top = config_bid_strategy.get('order_book_top', 1)
if not refresh:
rate = self._buy_rate_cache.get(pair)
# Check if cache has been invalidated
if rate:
logger.info(f"Using cached buy rate for {pair}.")
return rate
bid_strategy = self.config.get('bid_strategy', {})
if 'use_order_book' in bid_strategy and bid_strategy.get('use_order_book', False):
logger.info(
f"Getting price from order book {bid_strategy['price_side'].capitalize()} side."
)
order_book_top = bid_strategy.get('order_book_top', 1)
order_book = self.exchange.get_order_book(pair, order_book_top)
logger.debug('order_book %s', order_book)
# top 1 = index 0
order_book_rate = order_book['bids'][order_book_top - 1][0]
logger.info('...top %s order book buy rate %0.8f', order_book_top, order_book_rate)
order_book_rate = order_book[f"{bid_strategy['price_side']}s"][order_book_top - 1][0]
logger.info(f'...top {order_book_top} order book buy rate {order_book_rate:.8f}')
used_rate = order_book_rate
else:
if not tick:
logger.info('Using Last Ask / Last Price')
ticker = self.exchange.fetch_ticker(pair, refresh)
else:
ticker = tick
if ticker['ask'] < ticker['last']:
ticker_rate = ticker['ask']
else:
logger.info(f"Using Last {bid_strategy['price_side'].capitalize()} / Last Price")
ticker = self.exchange.fetch_ticker(pair)
ticker_rate = ticker[bid_strategy['price_side']]
if ticker['last'] and ticker_rate > ticker['last']:
balance = self.config['bid_strategy']['ask_last_balance']
ticker_rate = ticker['ask'] + balance * (ticker['last'] - ticker['ask'])
ticker_rate = ticker_rate + balance * (ticker['last'] - ticker_rate)
used_rate = ticker_rate
self._buy_rate_cache[pair] = used_rate
return used_rate
def get_trade_stake_amount(self, pair: str) -> float:
@ -567,7 +569,7 @@ class FreqtradeBot:
"""
Sends rpc notification when a buy cancel occured.
"""
current_rate = self.get_buy_rate(trade.pair, True)
current_rate = self.get_buy_rate(trade.pair, False)
msg = {
'type': RPCMessageType.BUY_CANCEL_NOTIFICATION,
@ -616,23 +618,43 @@ class FreqtradeBot:
return trades_closed
def _order_book_gen(self, pair: str, side: str, order_book_max: int = 1,
order_book_min: int = 1):
"""
Helper generator to query orderbook in loop (used for early sell-order placing)
"""
order_book = self.exchange.get_order_book(pair, order_book_max)
for i in range(order_book_min, order_book_max + 1):
yield order_book[side][i - 1][0]
def get_sell_rate(self, pair: str, refresh: bool) -> float:
"""
Get sell rate - either using get-ticker bid or first bid based on orderbook
Get sell rate - either using ticker bid or first bid based on orderbook
The orderbook portion is only used for rpc messaging, which would otherwise fail
for BitMex (has no bid/ask in fetch_ticker)
or remain static in any other case since it's not updating.
:param pair: Pair to get rate for
:param refresh: allow cached data
:return: Bid rate
"""
config_ask_strategy = self.config.get('ask_strategy', {})
if config_ask_strategy.get('use_order_book', False):
logger.debug('Using order book to get sell rate')
if not refresh:
rate = self._sell_rate_cache.get(pair)
# Check if cache has been invalidated
if rate:
logger.info(f"Using cached sell rate for {pair}.")
return rate
order_book = self.exchange.get_order_book(pair, 1)
rate = order_book['bids'][0][0]
ask_strategy = self.config.get('ask_strategy', {})
if ask_strategy.get('use_order_book', False):
# This code is only used for notifications, selling uses the generator directly
logger.info(
f"Getting price from order book {ask_strategy['price_side'].capitalize()} side."
)
rate = next(self._order_book_gen(pair, f"{ask_strategy['price_side']}s"))
else:
rate = self.exchange.fetch_ticker(pair, refresh)['bid']
rate = self.exchange.fetch_ticker(pair)[ask_strategy['price_side']]
self._sell_rate_cache[pair] = rate
return rate
def handle_trade(self, trade: Trade) -> bool:
@ -650,7 +672,7 @@ class FreqtradeBot:
config_ask_strategy = self.config.get('ask_strategy', {})
if (config_ask_strategy.get('use_sell_signal', True) or
config_ask_strategy.get('ignore_roi_if_buy_signal')):
config_ask_strategy.get('ignore_roi_if_buy_signal', False)):
(buy, sell) = self.strategy.get_signal(
trade.pair, self.strategy.ticker_interval,
self.dataprovider.ohlcv(trade.pair, self.strategy.ticker_interval))
@ -661,12 +683,13 @@ class FreqtradeBot:
order_book_min = config_ask_strategy.get('order_book_min', 1)
order_book_max = config_ask_strategy.get('order_book_max', 1)
order_book = self.exchange.get_order_book(trade.pair, order_book_max)
order_book = self._order_book_gen(trade.pair, f"{config_ask_strategy['price_side']}s",
order_book_min=order_book_min,
order_book_max=order_book_max)
for i in range(order_book_min, order_book_max + 1):
order_book_rate = order_book['asks'][i - 1][0]
logger.debug(' order book asks top %s: %0.8f', i, order_book_rate)
sell_rate = order_book_rate
sell_rate = next(order_book)
logger.debug(f" order book {config_ask_strategy['price_side']} top {i}: "
f"{sell_rate:0.8f}")
if self._check_and_execute_sell(trade, sell_rate, buy, sell):
return True
@ -960,8 +983,8 @@ class FreqtradeBot:
"""
# Update wallets to ensure amounts tied up in a stoploss is now free!
self.wallets.update()
wallet_amount = self.wallets.get_free(pair.split('/')[0])
trade_base_currency = self.exchange.get_pair_base_currency(pair)
wallet_amount = self.wallets.get_free(trade_base_currency)
logger.debug(f"{pair} - Wallet: {wallet_amount} - Trade-amount: {amount}")
if wallet_amount >= amount:
return amount
@ -1032,10 +1055,10 @@ class FreqtradeBot:
"""
profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit_trade = trade.calc_profit(rate=profit_rate)
# Use cached ticker here - it was updated seconds ago.
# Use cached rates here - it was updated seconds ago.
current_rate = self.get_sell_rate(trade.pair, False)
profit_percent = trade.calc_profit_ratio(profit_rate)
gain = "profit" if profit_percent > 0 else "loss"
profit_ratio = trade.calc_profit_ratio(profit_rate)
gain = "profit" if profit_ratio > 0 else "loss"
msg = {
'type': RPCMessageType.SELL_NOTIFICATION,
@ -1048,7 +1071,7 @@ class FreqtradeBot:
'open_rate': trade.open_rate,
'current_rate': current_rate,
'profit_amount': profit_trade,
'profit_percent': profit_percent,
'profit_ratio': profit_ratio,
'sell_reason': trade.sell_reason,
'open_date': trade.open_date,
'close_date': trade.close_date or datetime.utcnow(),
@ -1070,9 +1093,9 @@ class FreqtradeBot:
"""
profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit_trade = trade.calc_profit(rate=profit_rate)
current_rate = self.get_sell_rate(trade.pair, True)
profit_percent = trade.calc_profit_ratio(profit_rate)
gain = "profit" if profit_percent > 0 else "loss"
current_rate = self.get_sell_rate(trade.pair, False)
profit_ratio = trade.calc_profit_ratio(profit_rate)
gain = "profit" if profit_ratio > 0 else "loss"
msg = {
'type': RPCMessageType.SELL_CANCEL_NOTIFICATION,
@ -1085,7 +1108,7 @@ class FreqtradeBot:
'open_rate': trade.open_rate,
'current_rate': current_rate,
'profit_amount': profit_trade,
'profit_percent': profit_percent,
'profit_ratio': profit_ratio,
'sell_reason': trade.sell_reason,
'open_date': trade.open_date,
'close_date': trade.close_date,
@ -1147,12 +1170,13 @@ class FreqtradeBot:
if trade.fee_open == 0 or order['status'] == 'open':
return order_amount
trade_base_currency = self.exchange.get_pair_base_currency(trade.pair)
# use fee from order-dict if possible
if ('fee' in order and order['fee'] is not None and
(order['fee'].keys() >= {'currency', 'cost'})):
if (order['fee']['currency'] is not None and
order['fee']['cost'] is not None and
trade.pair.startswith(order['fee']['currency'])):
trade_base_currency == order['fee']['currency']):
new_amount = order_amount - order['fee']['cost']
logger.info("Applying fee on amount for %s (from %s to %s) from Order",
trade, order['amount'], new_amount)
@ -1174,7 +1198,7 @@ class FreqtradeBot:
# only applies if fee is in quote currency!
if (exectrade['fee']['currency'] is not None and
exectrade['fee']['cost'] is not None and
trade.pair.startswith(exectrade['fee']['currency'])):
trade_base_currency == exectrade['fee']['currency']):
fee_abs += exectrade['fee']['cost']
if not isclose(amount, order_amount, abs_tol=constants.MATH_CLOSE_PREC):

12
freqtrade/misc.py
View File

@ -81,13 +81,13 @@ def file_load_json(file):
gzipfile = file
# Try gzip file first, otherwise regular json file.
if gzipfile.is_file():
logger.debug('Loading ticker data from file %s', gzipfile)
with gzip.open(gzipfile) as tickerdata:
pairdata = json_load(tickerdata)
logger.debug(f"Loading historical data from file {gzipfile}")
with gzip.open(gzipfile) as datafile:
pairdata = json_load(datafile)
elif file.is_file():
logger.debug('Loading ticker data from file %s', file)
with open(file) as tickerdata:
pairdata = json_load(tickerdata)
logger.debug(f"Loading historical data from file {file}")
with open(file) as datafile:
pairdata = json_load(datafile)
else:
return None
return pairdata

85
freqtrade/optimize/backtesting.py
View File

@ -88,8 +88,8 @@ class Backtesting:
validate_config_consistency(self.config)
if "ticker_interval" not in self.config:
raise OperationalException("Ticker-interval needs to be set in either configuration "
"or as cli argument `--ticker-interval 5m`")
raise OperationalException("Timeframe (ticker interval) needs to be set in either "
"configuration or as cli argument `--ticker-interval 5m`")
self.timeframe = str(self.config.get('ticker_interval'))
self.timeframe_min = timeframe_to_minutes(self.timeframe)
@ -151,32 +151,33 @@ class Backtesting:
logger.info(f'Dumping backtest results to {recordfilename}')
file_dump_json(recordfilename, records)
def _get_ticker_list(self, processed: Dict) -> Dict[str, DataFrame]:
def _get_ohlcv_as_lists(self, processed: Dict) -> Dict[str, DataFrame]:
"""
Helper function to convert a processed tickerlist into a list for performance reasons.
Helper function to convert a processed dataframes into lists for performance reasons.
Used by backtest() - so keep this optimized for performance.
"""
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
ticker: Dict = {}
# Create ticker dict
data: Dict = {}
# Create dict with data
for pair, pair_data in processed.items():
pair_data.loc[:, 'buy'] = 0 # cleanup from previous run
pair_data.loc[:, 'sell'] = 0 # cleanup from previous run
ticker_data = self.strategy.advise_sell(
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
# to avoid using data from future, we buy/sell with signal from previous candle
ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
# To avoid using data from future, we use buy/sell signals shifted
# from the previous candle
df_analyzed.loc[:, 'buy'] = df_analyzed['buy'].shift(1)
df_analyzed.loc[:, 'sell'] = df_analyzed['sell'].shift(1)
ticker_data.drop(ticker_data.head(1).index, inplace=True)
df_analyzed.drop(df_analyzed.head(1).index, inplace=True)
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
ticker[pair] = [x for x in ticker_data.itertuples()]
return ticker
data[pair] = [x for x in df_analyzed.itertuples()]
return data
def _get_close_rate(self, sell_row, trade: Trade, sell: SellCheckTuple,
trade_dur: int) -> float:
@ -220,7 +221,7 @@ class Backtesting:
def _get_sell_trade_entry(
self, pair: str, buy_row: DataFrame,
partial_ticker: List, trade_count_lock: Dict,
partial_ohlcv: List, trade_count_lock: Dict,
stake_amount: float, max_open_trades: int) -> Optional[BacktestResult]:
trade = Trade(
@ -235,7 +236,7 @@ class Backtesting:
)
logger.debug(f"{pair} - Backtesting emulates creation of new trade: {trade}.")
# calculate win/lose forwards from buy point
for sell_row in partial_ticker:
for sell_row in partial_ohlcv:
if max_open_trades > 0:
# Increase trade_count_lock for every iteration
trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
@ -259,9 +260,9 @@ class Backtesting:
close_rate=closerate,
sell_reason=sell.sell_type
)
if partial_ticker:
if partial_ohlcv:
# no sell condition found - trade stil open at end of backtest period
sell_row = partial_ticker[-1]
sell_row = partial_ohlcv[-1]
bt_res = BacktestResult(pair=pair,
profit_percent=trade.calc_profit_ratio(rate=sell_row.open),
profit_abs=trade.calc_profit(rate=sell_row.open),
@ -308,8 +309,9 @@ class Backtesting:
trades = []
trade_count_lock: Dict = {}
# Dict of ticker-lists for performance (looping lists is a lot faster than dataframes)
ticker: Dict = self._get_ticker_list(processed)
# Use dict of lists with data for performance
# (looping lists is a lot faster than pandas DataFrames)
data: Dict = self._get_ohlcv_as_lists(processed)
lock_pair_until: Dict = {}
# Indexes per pair, so some pairs are allowed to have a missing start.
@ -319,12 +321,12 @@ class Backtesting:
# Loop timerange and get candle for each pair at that point in time
while tmp < end_date:
for i, pair in enumerate(ticker):
for i, pair in enumerate(data):
if pair not in indexes:
indexes[pair] = 0
try:
row = ticker[pair][indexes[pair]]
row = data[pair][indexes[pair]]
except IndexError:
# missing Data for one pair at the end.
# Warnings for this are shown during data loading
@ -352,7 +354,7 @@ class Backtesting:
# since indexes has been incremented before, we need to go one step back to
# also check the buying candle for sell conditions.
trade_entry = self._get_sell_trade_entry(pair, row, ticker[pair][indexes[pair]-1:],
trade_entry = self._get_sell_trade_entry(pair, row, data[pair][indexes[pair]-1:],
trade_count_lock, stake_amount,
max_open_trades)
@ -395,7 +397,7 @@ class Backtesting:
self._set_strategy(strat)
# need to reprocess data every time to populate signals
preprocessed = self.strategy.tickerdata_to_dataframe(data)
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
@ -419,32 +421,41 @@ class Backtesting:
for strategy, results in all_results.items():
if self.config.get('export', False):
self._store_backtest_result(Path(self.config['exportfilename']), results,
self._store_backtest_result(self.config['exportfilename'], results,
strategy if len(self.strategylist) > 1 else None)
print(f"Result for strategy {strategy}")
print(' BACKTESTING REPORT '.center(133, '='))
print(generate_text_table(data,
stake_currency=self.config['stake_currency'],
table = generate_text_table(data, stake_currency=self.config['stake_currency'],
max_open_trades=self.config['max_open_trades'],
results=results))
results=results)
if isinstance(table, str):
print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
print(' SELL REASON STATS '.center(133, '='))
print(generate_text_table_sell_reason(data,
table = generate_text_table_sell_reason(data,
stake_currency=self.config['stake_currency'],
max_open_trades=self.config['max_open_trades'],
results=results))
results=results)
if isinstance(table, str):
print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
print(table)
print(' LEFT OPEN TRADES REPORT '.center(133, '='))
print(generate_text_table(data,
table = generate_text_table(data,
stake_currency=self.config['stake_currency'],
max_open_trades=self.config['max_open_trades'],
results=results.loc[results.open_at_end], skip_nan=True))
results=results.loc[results.open_at_end], skip_nan=True)
if isinstance(table, str):
print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
if isinstance(table, str):
print('=' * len(table.splitlines()[0]))
print()
if len(all_results) > 1:
# Print Strategy summary table
print(' STRATEGY SUMMARY '.center(133, '='))
print(generate_text_table_strategy(self.config['stake_currency'],
table = generate_text_table_strategy(self.config['stake_currency'],
self.config['max_open_trades'],
all_results=all_results))
all_results=all_results)
print(' STRATEGY SUMMARY '.center(len(table.splitlines()[0]), '='))
print(table)
print('=' * len(table.splitlines()[0]))
print('\nFor more details, please look at the detail tables above')

176
freqtrade/optimize/hyperopt.py
View File

@ -9,6 +9,7 @@ import logging
import random
import sys
import warnings
from math import ceil
from collections import OrderedDict
from operator import itemgetter
from pathlib import Path
@ -20,7 +21,10 @@ 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
from pandas import DataFrame, json_normalize, isna
import tabulate
from os import path
import io
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.history import get_timerange
@ -73,7 +77,7 @@ class Hyperopt:
self.trials_file = (self.config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_results.pickle')
self.tickerdata_pickle = (self.config['user_data_dir'] /
self.data_pickle_file = (self.config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_tickerdata.pkl')
self.total_epochs = config.get('epochs', 0)
@ -115,6 +119,7 @@ class Hyperopt:
self.config['ask_strategy']['use_sell_signal'] = True
self.print_all = self.config.get('print_all', False)
self.hyperopt_table_header = 0
self.print_colorized = self.config.get('print_colorized', False)
self.print_json = self.config.get('print_json', False)
@ -127,7 +132,7 @@ class Hyperopt:
"""
Remove hyperopt pickle files to restart hyperopt.
"""
for f in [self.tickerdata_pickle, self.trials_file]:
for f in [self.data_pickle_file, self.trials_file]:
p = Path(f)
if p.is_file():
logger.info(f"Removing `{p}`.")
@ -152,7 +157,7 @@ class Hyperopt:
"""
num_trials = len(self.trials)
if num_trials > self.num_trials_saved:
logger.info(f"Saving {num_trials} {plural(num_trials, 'epoch')}.")
logger.debug(f"Saving {num_trials} {plural(num_trials, 'epoch')}.")
dump(self.trials, self.trials_file)
self.num_trials_saved = num_trials
if final:
@ -271,8 +276,10 @@ class Hyperopt:
if not self.print_all:
# Separate the results explanation string from dots
print("\n")
self.print_results_explanation(results, self.total_epochs, self.print_all,
self.print_colorized)
self.print_result_table(self.config, results, self.total_epochs,
self.print_all, self.print_colorized,
self.hyperopt_table_header)
self.hyperopt_table_header = 2
@staticmethod
def print_results_explanation(results, total_epochs, highlight_best: bool,
@ -296,6 +303,142 @@ class Hyperopt:
f"{results['results_explanation']} " +
f"Objective: {results['loss']:.5f}")
@staticmethod
def print_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
print_colorized: bool, remove_header: int) -> None:
"""
Log result table
"""
if not results:
return
tabulate.PRESERVE_WHITESPACE = True
trials = json_normalize(results, max_level=1)
trials['Best'] = ''
trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
'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',
'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '*'
trials.loc[trials['is_best'], 'Best'] = 'Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Trades'] = trials['Trades'].astype(str)
trials['Epoch'] = trials['Epoch'].apply(
lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: '{:,.2f}%'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: '{:,.1f} m'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
)
trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
)
trials['Profit'] = trials.apply(
lambda x: '{:,.8f} {} {}'.format(
x['Total profit'], config['stake_currency'],
'({:,.2f}%)'.format(x['Profit']).rjust(10, ' ')
).rjust(25+len(config['stake_currency']))
if x['Total profit'] != 0.0 else '--'.rjust(25+len(config['stake_currency'])),
axis=1
)
trials = trials.drop(columns=['Total profit'])
if print_colorized:
for i in range(len(trials)):
if trials.loc[i]['is_profit']:
for j in range(len(trials.loc[i])-3):
trials.iat[i, j] = "{}{}{}".format(Fore.GREEN,
str(trials.loc[i][j]), Fore.RESET)
if trials.loc[i]['is_best'] and highlight_best:
for j in range(len(trials.loc[i])-3):
trials.iat[i, j] = "{}{}{}".format(Style.BRIGHT,
str(trials.loc[i][j]), Style.RESET_ALL)
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
if remove_header > 0:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='orgtbl',
headers='keys', stralign="right"
)
table = table.split("\n", remove_header)[remove_header]
elif remove_header < 0:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='psql',
headers='keys', stralign="right"
)
table = "\n".join(table.split("\n")[0:remove_header])
else:
table = tabulate.tabulate(
trials.to_dict(orient='list'), tablefmt='psql',
headers='keys', stralign="right"
)
print(table)
@staticmethod
def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
csv_file: str) -> None:
"""
Log result to csv-file
"""
if not results:
return
# Verification for overwrite
if path.isfile(csv_file):
logger.error("CSV-File already exists!")
return
try:
io.open(csv_file, 'w+').close()
except IOError:
logger.error("Filed to create CSV-File!")
return
trials = json_normalize(results, max_level=1)
trials['Best'] = ''
trials['Stake currency'] = config['stake_currency']
trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.avg_profit', 'results_metrics.total_profit',
'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']]
trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit', 'Stake currency',
'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '*'
trials.loc[trials['is_best'], 'Best'] = 'Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Epoch'] = trials['Epoch'].astype(str)
trials['Trades'] = trials['Trades'].astype(str)
trials['Total profit'] = trials['Total profit'].apply(
lambda x: '{:,.8f}'.format(x) if x != 0.0 else ""
)
trials['Profit'] = trials['Profit'].apply(
lambda x: '{:,.2f}'.format(x) if not isna(x) else ""
)
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: '{:,.2f}%'.format(x) if not isna(x) else ""
)
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: '{:,.1f} m'.format(x) if not isna(x) else ""
)
trials['Objective'] = trials['Objective'].apply(
lambda x: '{:,.5f}'.format(x) if x != 100000 else ""
)
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
trials.to_csv(csv_file, index=False, header=True, mode='w', encoding='UTF-8')
print("CSV-File created!")
def has_space(self, space: str) -> bool:
"""
Tell if the space value is contained in the configuration
@ -369,7 +512,7 @@ class Hyperopt:
self.backtesting.strategy.trailing_only_offset_is_reached = \
d['trailing_only_offset_is_reached']
processed = load(self.tickerdata_pickle)
processed = load(self.data_pickle_file)
min_date, max_date = get_timerange(processed)
@ -482,10 +625,10 @@ class Hyperopt:
def start(self) -> None:
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
logger.info(f"Using optimizer random state: {self.random_state}")
self.hyperopt_table_header = -1
data, timerange = self.backtesting.load_bt_data()
preprocessed = self.backtesting.strategy.tickerdata_to_dataframe(data)
preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
@ -496,7 +639,7 @@ class Hyperopt:
'Hyperopting with data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
)
dump(preprocessed, self.tickerdata_pickle)
dump(preprocessed, self.data_pickle_file)
# We don't need exchange instance anymore while running hyperopt
self.backtesting.exchange = None # type: ignore
@ -518,16 +661,21 @@ class Hyperopt:
with Parallel(n_jobs=config_jobs) as parallel:
jobs = parallel._effective_n_jobs()
logger.info(f'Effective number of parallel workers used: {jobs}')
EVALS = max(self.total_epochs // jobs, 1)
EVALS = ceil(self.total_epochs / jobs)
for i in range(EVALS):
asked = self.opt.ask(n_points=jobs)
# Correct the number of epochs to be processed for the last
# iteration (should not exceed self.total_epochs in total)
n_rest = (i + 1) * jobs - self.total_epochs
current_jobs = jobs - n_rest if n_rest > 0 else jobs
asked = self.opt.ask(n_points=current_jobs)
f_val = self.run_optimizer_parallel(parallel, asked, i)
self.opt.tell(asked, [v['loss'] for v in f_val])
self.fix_optimizer_models_list()
for j in range(jobs):
for j, val in enumerate(f_val):
# Use human-friendly indexes here (starting from 1)
current = i * jobs + j + 1
val = f_val[j]
val['current_epoch'] = current
val['is_initial_point'] = current <= INITIAL_POINTS
logger.debug(f"Optimizer epoch evaluated: {val}")

2
freqtrade/optimize/hyperopt_loss_sharpe.py
View File

@ -36,7 +36,7 @@ class SharpeHyperOptLoss(IHyperOptLoss):
expected_returns_mean = total_profit.sum() / days_period
up_stdev = np.std(total_profit)
if (np.std(total_profit) != 0.):
if up_stdev != 0:
sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
else:
# Define high (negative) sharpe ratio to be clear that this is NOT optimal.

2
freqtrade/optimize/hyperopt_loss_sharpe_daily.py
View File

@ -51,7 +51,7 @@ class SharpeHyperOptLossDaily(IHyperOptLoss):
expected_returns_mean = total_profit.mean()
up_stdev = total_profit.std()
if (up_stdev != 0.):
if up_stdev != 0:
sharp_ratio = expected_returns_mean / up_stdev * math.sqrt(days_in_year)
else:
# Define high (negative) sharpe ratio to be clear that this is NOT optimal.

49
freqtrade/optimize/hyperopt_loss_sortino.py Normal file
View File

@ -0,0 +1,49 @@
"""
SortinoHyperOptLoss
This module defines the alternative HyperOptLoss class which can be used for
Hyperoptimization.
"""
from datetime import datetime
from pandas import DataFrame
import numpy as np
from freqtrade.optimize.hyperopt import IHyperOptLoss
class SortinoHyperOptLoss(IHyperOptLoss):
"""
Defines the loss function for hyperopt.
This implementation uses the Sortino Ratio calculation.
"""
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for more optimal results.
Uses Sortino Ratio calculation.
"""
total_profit = results["profit_percent"]
days_period = (max_date - min_date).days
# adding slippage of 0.1% per trade
total_profit = total_profit - 0.0005
expected_returns_mean = total_profit.sum() / days_period
results['downside_returns'] = 0
results.loc[total_profit < 0, 'downside_returns'] = results['profit_percent']
down_stdev = np.std(results['downside_returns'])
if down_stdev != 0:
sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
else:
# Define high (negative) sortino ratio to be clear that this is NOT optimal.
sortino_ratio = -20.
# print(expected_returns_mean, down_stdev, sortino_ratio)
return -sortino_ratio

70
freqtrade/optimize/hyperopt_loss_sortino_daily.py Normal file
View File

@ -0,0 +1,70 @@
"""
SortinoHyperOptLossDaily
This module defines the alternative HyperOptLoss class which can be used for
Hyperoptimization.
"""
import math
from datetime import datetime
from pandas import DataFrame, date_range
from freqtrade.optimize.hyperopt import IHyperOptLoss
class SortinoHyperOptLossDaily(IHyperOptLoss):
"""
Defines the loss function for hyperopt.
This implementation uses the Sortino Ratio calculation.
"""
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for more optimal results.
Uses Sortino Ratio calculation.
Sortino Ratio calculated as described in
http://www.redrockcapital.com/Sortino__A__Sharper__Ratio_Red_Rock_Capital.pdf
"""
resample_freq = '1D'
slippage_per_trade_ratio = 0.0005
days_in_year = 365
minimum_acceptable_return = 0.0
# apply slippage per trade to profit_percent
results.loc[:, 'profit_percent_after_slippage'] = \
results['profit_percent'] - slippage_per_trade_ratio
# create the index within the min_date and end max_date
t_index = date_range(start=min_date, end=max_date, freq=resample_freq,
normalize=True)
sum_daily = (
results.resample(resample_freq, on='close_time').agg(
{"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0)
)
total_profit = sum_daily["profit_percent_after_slippage"] - minimum_acceptable_return
expected_returns_mean = total_profit.mean()
sum_daily['downside_returns'] = 0
sum_daily.loc[total_profit < 0, 'downside_returns'] = total_profit
total_downside = sum_daily['downside_returns']
# Here total_downside contains min(0, P - MAR) values,
# where P = sum_daily["profit_percent_after_slippage"]
down_stdev = math.sqrt((total_downside**2).sum() / len(total_downside))
if down_stdev != 0:
sortino_ratio = expected_returns_mean / down_stdev * math.sqrt(days_in_year)
else:
# Define high (negative) sortino ratio to be clear that this is NOT optimal.
sortino_ratio = -20.
# print(t_index, sum_daily, total_profit)
# print(minimum_acceptable_return, expected_returns_mean, down_stdev, sortino_ratio)
return -sortino_ratio

8
freqtrade/optimize/optimize_reports.py
View File

@ -66,7 +66,7 @@ def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_tra
])
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(tabular_data, headers=headers,
floatfmt=floatfmt, tablefmt="pipe") # type: ignore
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
def generate_text_table_sell_reason(
@ -112,7 +112,7 @@ def generate_text_table_sell_reason(
profit_percent_tot,
]
)
return tabulate(tabular_data, headers=headers, tablefmt="pipe")
return tabulate(tabular_data, headers=headers, tablefmt="orgtbl", stralign="right")
def generate_text_table_strategy(stake_currency: str, max_open_trades: str,
@ -146,7 +146,7 @@ def generate_text_table_strategy(stake_currency: str, max_open_trades: str,
])
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(tabular_data, headers=headers,
floatfmt=floatfmt, tablefmt="pipe") # type: ignore
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
def generate_edge_table(results: dict) -> str:
@ -172,4 +172,4 @@ def generate_edge_table(results: dict) -> str:
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(tabular_data, headers=headers,
floatfmt=floatfmt, tablefmt="pipe") # type: ignore
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore

26
freqtrade/pairlist/IPairList.py
View File

@ -67,21 +67,37 @@ class IPairList(ABC):
"""
@staticmethod
def verify_blacklist(pairlist: List[str], blacklist: List[str]) -> List[str]:
def verify_blacklist(pairlist: List[str], blacklist: List[str],
aswarning: bool) -> List[str]:
"""
Verify and remove items from pairlist - returning a filtered pairlist.
Logs a warning or info depending on `aswarning`.
Pairlists explicitly using this method shall use `aswarning=False`!
:param pairlist: Pairlist to validate
:param blacklist: Blacklist to validate pairlist against
:param aswarning: Log message as Warning or info
:return: pairlist - blacklisted pairs
"""
for pair in deepcopy(pairlist):
if pair in blacklist:
if aswarning:
logger.warning(f"Pair {pair} in your blacklist. Removing it from whitelist...")
else:
logger.info(f"Pair {pair} in your blacklist. Removing it from whitelist...")
pairlist.remove(pair)
return pairlist
def _verify_blacklist(self, pairlist: List[str]) -> List[str]:
def _verify_blacklist(self, pairlist: List[str], aswarning: bool = True) -> List[str]:
"""
Proxy method to verify_blacklist for easy access for child classes.
Logs a warning or info depending on `aswarning`.
Pairlists explicitly using this method shall use aswarning=False!
:param pairlist: Pairlist to validate
:param aswarning: Log message as Warning or info.
:return: pairlist - blacklisted pairs
"""
return IPairList.verify_blacklist(pairlist, self._pairlistmanager.blacklist)
return IPairList.verify_blacklist(pairlist, self._pairlistmanager.blacklist,
aswarning=aswarning)
def _whitelist_for_active_markets(self, pairlist: List[str]) -> List[str]:
"""
@ -99,7 +115,8 @@ class IPairList(ABC):
logger.warning(f"Pair {pair} is not compatible with exchange "
f"{self._exchange.name}. Removing it from whitelist..")
continue
if not pair.endswith(self._config['stake_currency']):
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..")
continue
@ -112,6 +129,5 @@ class IPairList(ABC):
if pair not in sanitized_whitelist:
sanitized_whitelist.append(pair)
sanitized_whitelist = self._verify_blacklist(sanitized_whitelist)
# We need to remove pairs that are unknown
return sanitized_whitelist

6
freqtrade/pairlist/VolumePairList.py
View File

@ -91,9 +91,9 @@ class VolumePairList(IPairList):
if self._pairlist_pos == 0:
# If VolumePairList is the first in the list, use fresh pairlist
# check length so that we make sure that '/' is actually in the string
# Check if pair quote currency equals to the stake currency.
filtered_tickers = [v for k, v in tickers.items()
if (len(k.split('/')) == 2 and k.split('/')[1] == base_currency
if (self._exchange.get_pair_quote_currency(k) == base_currency
and v[key] is not None)]
else:
# If other pairlist is in front, use the incomming pairlist.
@ -106,7 +106,7 @@ class VolumePairList(IPairList):
# Validate whitelist to only have active market pairs
pairs = self._whitelist_for_active_markets([s['symbol'] for s in sorted_tickers])
pairs = self._verify_blacklist(pairs)
pairs = self._verify_blacklist(pairs, aswarning=False)
# Limit to X number of pairs
pairs = pairs[:self._number_pairs]
logger.info(f"Searching {self._number_pairs} pairs: {pairs}")

2
freqtrade/pairlist/pairlistmanager.py
View File

@ -91,6 +91,6 @@ class PairListManager():
pairlist = pl.filter_pairlist(pairlist, tickers)
# Validation against blacklist happens after the pairlists to ensure blacklist is respected.
pairlist = IPairList.verify_blacklist(pairlist, self.blacklist)
pairlist = IPairList.verify_blacklist(pairlist, self.blacklist, True)
self._whitelist = pairlist

4
freqtrade/persistence.py
View File

@ -405,8 +405,8 @@ class Trade(_DECL_BASE):
rate=(rate or self.close_rate),
fee=(fee or self.fee_close)
)
profit_percent = (close_trade_price / self.open_trade_price) - 1
return float(f"{profit_percent:.8f}")
profit_ratio = (close_trade_price / self.open_trade_price) - 1
return float(f"{profit_ratio:.8f}")
@staticmethod
def get_trades(trade_filter=None) -> Query:

58
freqtrade/plot/plotting.py
View File

@ -5,7 +5,8 @@ from typing import Any, Dict, List
import pandas as pd
from freqtrade.configuration import TimeRange
from freqtrade.data.btanalysis import (combine_tickers_with_mean,
from freqtrade.data.btanalysis import (calculate_max_drawdown,
combine_dataframes_with_mean,
create_cum_profit,
extract_trades_of_period, load_trades)
from freqtrade.data.converter import trim_dataframe
@ -28,7 +29,7 @@ except ImportError:
def init_plotscript(config):
"""
Initialize objects needed for plotting
:return: Dict with tickers, trades and pairs
:return: Dict with candle (OHLCV) data, trades and pairs
"""
if "pairs" in config:
@ -39,7 +40,7 @@ def init_plotscript(config):
# Set timerange to use
timerange = TimeRange.parse_timerange(config.get("timerange"))
tickers = load_data(
data = load_data(
datadir=config.get("datadir"),
pairs=pairs,
timeframe=config.get('ticker_interval', '5m'),
@ -52,7 +53,7 @@ def init_plotscript(config):
exportfilename=config.get('exportfilename'),
)
trades = trim_dataframe(trades, timerange, 'open_time')
return {"tickers": tickers,
return {"ohlcv": data,
"trades": trades,
"pairs": pairs,
}
@ -111,6 +112,36 @@ def add_profit(fig, row, data: pd.DataFrame, column: str, name: str) -> make_sub
return fig
def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame) -> make_subplots:
"""
Add scatter points indicating max drawdown
"""
try:
max_drawdown, highdate, lowdate = calculate_max_drawdown(trades)
drawdown = go.Scatter(
x=[highdate, lowdate],
y=[
df_comb.loc[highdate, 'cum_profit'],
df_comb.loc[lowdate, 'cum_profit'],
],
mode='markers',
name=f"Max drawdown {max_drawdown:.2f}%",
text=f"Max drawdown {max_drawdown:.2f}%",
marker=dict(
symbol='square-open',
size=9,
line=dict(width=2),
color='green'
)
)
fig.add_trace(drawdown, row, 1)
except ValueError:
logger.warning("No trades found - not plotting max drawdown.")
return fig
def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
"""
Add trades to "fig"
@ -337,10 +368,10 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
return fig
def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
trades: pd.DataFrame, timeframe: str) -> go.Figure:
# Combine close-values for all pairs, rename columns to "pair"
df_comb = combine_tickers_with_mean(tickers, "close")
df_comb = combine_dataframes_with_mean(data, "close")
# Add combined cumulative profit
df_comb = create_cum_profit(df_comb, trades, 'cum_profit', timeframe)
@ -364,6 +395,7 @@ def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
fig.add_trace(avgclose, 1, 1)
fig = add_profit(fig, 2, df_comb, 'cum_profit', 'Profit')
fig = add_max_drawdown(fig, 2, trades, df_comb)
for pair in pairs:
profit_col = f'cum_profit_{pair}'
@ -407,7 +439,7 @@ def load_and_plot_trades(config: Dict[str, Any]):
"""
From configuration provided
- Initializes plot-script
- Get tickers data
- Get candle (OHLCV) data
- Generate Dafaframes populated with indicators and signals based on configured strategy
- Load trades excecuted during the selected period
- Generate Plotly plot objects
@ -419,19 +451,17 @@ def load_and_plot_trades(config: Dict[str, Any]):
plot_elements = init_plotscript(config)
trades = plot_elements['trades']
pair_counter = 0
for pair, data in plot_elements["tickers"].items():
for pair, data in plot_elements["ohlcv"].items():
pair_counter += 1
logger.info("analyse pair %s", pair)
tickers = {}
tickers[pair] = data
dataframe = strategy.analyze_ticker(tickers[pair], {'pair': pair})
df_analyzed = strategy.analyze_ticker(data, {'pair': pair})
trades_pair = trades.loc[trades['pair'] == pair]
trades_pair = extract_trades_of_period(dataframe, trades_pair)
trades_pair = extract_trades_of_period(df_analyzed, trades_pair)
fig = generate_candlestick_graph(
pair=pair,
data=dataframe,
data=df_analyzed,
trades=trades_pair,
indicators1=config.get("indicators1", []),
indicators2=config.get("indicators2", []),
@ -462,7 +492,7 @@ 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["tickers"],
fig = generate_profit_graph(plot_elements["pairs"], plot_elements["ohlcv"],
trades, config.get('ticker_interval', '5m'))
store_plot_file(fig, filename='freqtrade-profit-plot.html',
directory=config['user_data_dir'] / "plot", auto_open=True)

52
freqtrade/rpc/fiat_convert.py
View File

@ -7,7 +7,7 @@ import logging
import time
from typing import Dict, List
from coinmarketcap import Market
from pycoingecko import CoinGeckoAPI
from freqtrade.constants import SUPPORTED_FIAT
@ -38,8 +38,8 @@ class CryptoFiat:
# Private attributes
self._expiration = 0.0
self.crypto_symbol = crypto_symbol.upper()
self.fiat_symbol = fiat_symbol.upper()
self.crypto_symbol = crypto_symbol.lower()
self.fiat_symbol = fiat_symbol.lower()
self.set_price(price=price)
def set_price(self, price: float) -> None:
@ -67,17 +67,20 @@ class CryptoToFiatConverter:
This object is also a Singleton
"""
__instance = None
_coinmarketcap: Market = None
_coingekko: CoinGeckoAPI = None
_cryptomap: Dict = {}
def __new__(cls):
"""
This class is a singleton - cannot be instantiated twice.
"""
if CryptoToFiatConverter.__instance is None:
CryptoToFiatConverter.__instance = object.__new__(cls)
try:
CryptoToFiatConverter._coinmarketcap = Market()
CryptoToFiatConverter._coingekko = CoinGeckoAPI()
except BaseException:
CryptoToFiatConverter._coinmarketcap = None
CryptoToFiatConverter._coingekko = None
return CryptoToFiatConverter.__instance
def __init__(self) -> None:
@ -86,14 +89,12 @@ class CryptoToFiatConverter:
def _load_cryptomap(self) -> None:
try:
coinlistings = self._coinmarketcap.listings()
self._cryptomap = dict(map(lambda coin: (coin["symbol"], str(coin["id"])),
coinlistings["data"]))
except (BaseException) as exception:
coinlistings = self._coingekko.get_coins_list()
# Create mapping table from synbol to coingekko_id
self._cryptomap = {x['symbol']: x['id'] for x in coinlistings}
except (Exception) as exception:
logger.error(
"Could not load FIAT Cryptocurrency map for the following problem: %s",
type(exception).__name__
)
f"Could not load FIAT Cryptocurrency map for the following problem: {exception}")
def convert_amount(self, crypto_amount: float, crypto_symbol: str, fiat_symbol: str) -> float:
"""
@ -115,8 +116,8 @@ class CryptoToFiatConverter:
:param fiat_symbol: FIAT currency you want to convert to (e.g USD)
:return: Price in FIAT
"""
crypto_symbol = crypto_symbol.upper()
fiat_symbol = fiat_symbol.upper()
crypto_symbol = crypto_symbol.lower()
fiat_symbol = fiat_symbol.lower()
# Check if the fiat convertion you want is supported
if not self._is_supported_fiat(fiat=fiat_symbol):
@ -170,15 +171,13 @@ class CryptoToFiatConverter:
:return: bool, True supported, False not supported
"""
fiat = fiat.upper()
return fiat in SUPPORTED_FIAT
return fiat.upper() in SUPPORTED_FIAT
def _find_price(self, crypto_symbol: str, fiat_symbol: str) -> float:
"""
Call CoinMarketCap API to retrieve the price in the FIAT
:param crypto_symbol: Crypto-currency you want to convert (e.g BTC)
:param fiat_symbol: FIAT currency you want to convert to (e.g USD)
Call CoinGekko API to retrieve the price in the FIAT
:param crypto_symbol: Crypto-currency you want to convert (e.g btc)
:param fiat_symbol: FIAT currency you want to convert to (e.g usd)
:return: float, price of the crypto-currency in Fiat
"""
# Check if the fiat convertion you want is supported
@ -195,12 +194,13 @@ class CryptoToFiatConverter:
return 0.0
try:
_gekko_id = self._cryptomap[crypto_symbol]
return float(
self._coinmarketcap.ticker(
currency=self._cryptomap[crypto_symbol],
convert=fiat_symbol
)['data']['quotes'][fiat_symbol.upper()]['price']
self._coingekko.get_price(
ids=_gekko_id,
vs_currencies=fiat_symbol
)[_gekko_id][fiat_symbol]
)
except BaseException as exception:
except Exception as exception:
logger.error("Error in _find_price: %s", exception)
return 0.0

26
freqtrade/rpc/rpc.py
View File

@ -155,9 +155,9 @@ class RPC:
current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
except DependencyException:
current_rate = NAN
trade_perc = (100 * trade.calc_profit_ratio(current_rate))
trade_percent = (100 * trade.calc_profit_ratio(current_rate))
trade_profit = trade.calc_profit(current_rate)
profit_str = f'{trade_perc:.2f}%'
profit_str = f'{trade_percent:.2f}%'
if self._fiat_converter:
fiat_profit = self._fiat_converter.convert_amount(
trade_profit,
@ -232,9 +232,9 @@ class RPC:
trades = Trade.get_trades().order_by(Trade.id).all()
profit_all_coin = []
profit_all_perc = []
profit_all_ratio = []
profit_closed_coin = []
profit_closed_perc = []
profit_closed_ratio = []
durations = []
for trade in trades:
@ -246,21 +246,21 @@ class RPC:
durations.append((trade.close_date - trade.open_date).total_seconds())
if not trade.is_open:
profit_percent = trade.calc_profit_ratio()
profit_ratio = trade.calc_profit_ratio()
profit_closed_coin.append(trade.calc_profit())
profit_closed_perc.append(profit_percent)
profit_closed_ratio.append(profit_ratio)
else:
# Get current rate
try:
current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
except DependencyException:
current_rate = NAN
profit_percent = trade.calc_profit_ratio(rate=current_rate)
profit_ratio = trade.calc_profit_ratio(rate=current_rate)
profit_all_coin.append(
trade.calc_profit(rate=trade.close_rate or current_rate)
)
profit_all_perc.append(profit_percent)
profit_all_ratio.append(profit_ratio)
best_pair = Trade.get_best_pair()
@ -271,7 +271,7 @@ class RPC:
# Prepare data to display
profit_closed_coin_sum = round(sum(profit_closed_coin), 8)
profit_closed_percent = (round(mean(profit_closed_perc) * 100, 2) if profit_closed_perc
profit_closed_percent = (round(mean(profit_closed_ratio) * 100, 2) if profit_closed_ratio
else 0.0)
profit_closed_fiat = self._fiat_converter.convert_amount(
profit_closed_coin_sum,
@ -280,7 +280,7 @@ class RPC:
) if self._fiat_converter else 0
profit_all_coin_sum = round(sum(profit_all_coin), 8)
profit_all_percent = round(mean(profit_all_perc) * 100, 2) if profit_all_perc else 0.0
profit_all_percent = round(mean(profit_all_ratio) * 100, 2) if profit_all_ratio else 0.0
profit_all_fiat = self._fiat_converter.convert_amount(
profit_all_coin_sum,
stake_currency,
@ -460,9 +460,9 @@ class RPC:
if self._freqtrade.state != State.RUNNING:
raise RPCException('trader is not running')
# Check pair is in stake currency
# Check if pair quote currency equals to the stake currency.
stake_currency = self._freqtrade.config.get('stake_currency')
if not pair.endswith(stake_currency):
if not self._freqtrade.exchange.get_pair_quote_currency(pair) == stake_currency:
raise RPCException(
f'Wrong pair selected. Please pairs with stake {stake_currency} pairs only')
# check if valid pair
@ -517,7 +517,7 @@ class RPC:
if add:
stake_currency = self._freqtrade.config.get('stake_currency')
for pair in add:
if (pair.endswith(stake_currency)
if (self._freqtrade.exchange.get_pair_quote_currency(pair) == stake_currency
and pair not in self._freqtrade.pairlists.blacklist):
self._freqtrade.pairlists.blacklist.append(pair)

2
freqtrade/rpc/telegram.py
View File

@ -148,7 +148,7 @@ class Telegram(RPC):
elif msg['type'] == RPCMessageType.SELL_NOTIFICATION:
msg['amount'] = round(msg['amount'], 8)
msg['profit_percent'] = round(msg['profit_percent'] * 100, 2)
msg['profit_percent'] = round(msg['profit_ratio'] * 100, 2)
msg['duration'] = msg['close_date'].replace(
microsecond=0) - msg['open_date'].replace(microsecond=0)
msg['duration_min'] = msg['duration'].total_seconds() / 60

35
freqtrade/strategy/interface.py
View File

@ -60,7 +60,7 @@ class IStrategy(ABC):
Attributes you can use:
minimal_roi -> Dict: Minimal ROI designed for the strategy
stoploss -> float: optimal stoploss designed for the strategy
ticker_interval -> str: value of the ticker interval to use for the strategy
ticker_interval -> str: value of the timeframe (ticker interval) to use with the strategy
"""
# Strategy interface version
# Default to version 2
@ -126,7 +126,7 @@ class IStrategy(ABC):
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Populate indicators that will be used in the Buy and Sell strategy
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param dataframe: DataFrame with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
@ -237,11 +237,11 @@ class IStrategy(ABC):
def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Parses the given ticker history and returns a populated DataFrame
Parses the given candle (OHLCV) data and returns a populated DataFrame
add several TA indicators and buy signal to it
:param dataframe: Dataframe containing ticker data
:param dataframe: Dataframe containing data from exchange
:param metadata: Metadata dictionary with additional data (e.g. 'pair')
:return: DataFrame with ticker data and indicator data
:return: DataFrame of candle (OHLCV) data with indicator data and signals added
"""
logger.debug("TA Analysis Launched")
dataframe = self.advise_indicators(dataframe, metadata)
@ -251,12 +251,12 @@ class IStrategy(ABC):
def _analyze_ticker_internal(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Parses the given ticker history and returns a populated DataFrame
Parses the given candle (OHLCV) data and returns a populated DataFrame
add several TA indicators and buy signal to it
WARNING: Used internally only, may skip analysis if `process_only_new_candles` is set.
:param dataframe: Dataframe containing ticker data
:param dataframe: Dataframe containing data from exchange
:param metadata: Metadata dictionary with additional data (e.g. 'pair')
:return: DataFrame with ticker data and indicator data
:return: DataFrame of candle (OHLCV) data with indicator data and signals added
"""
pair = str(metadata.get('pair'))
@ -288,7 +288,7 @@ class IStrategy(ABC):
:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
"""
if not isinstance(dataframe, DataFrame) or dataframe.empty:
logger.warning('Empty ticker history for pair %s', pair)
logger.warning('Empty candle (OHLCV) data for pair %s', pair)
return False, False
try:
@ -296,7 +296,7 @@ class IStrategy(ABC):
self._analyze_ticker_internal, message=""
)(dataframe, {'pair': pair})
except StrategyError as error:
logger.warning(f"Unable to analyze ticker for pair {pair}: {error}")
logger.warning(f"Unable to analyze candle (OHLCV) data for pair {pair}: {error}")
return False, False
@ -393,7 +393,7 @@ class IStrategy(ABC):
"""
Based on current profit of the trade and configured (trailing) stoploss,
decides to sell or not
:param current_profit: current profit in percent
:param current_profit: current profit as ratio
"""
stop_loss_value = force_stoploss if force_stoploss else self.stoploss
@ -456,8 +456,9 @@ class IStrategy(ABC):
def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
"""
Based on trade duration, current price and ROI configuration, decides whether bot should
sell. Requires current_profit to be in percent!!
Based on trade duration, current profit of the trade and ROI configuration,
decides whether bot should sell.
:param current_profit: current profit as ratio
:return: True if bot should sell at current rate
"""
# Check if time matches and current rate is above threshold
@ -468,19 +469,19 @@ class IStrategy(ABC):
else:
return current_profit > roi
def tickerdata_to_dataframe(self, tickerdata: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
def ohlcvdata_to_dataframe(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
"""
Creates a dataframe and populates indicators for given ticker data
Creates a dataframe and populates indicators for given candle (OHLCV) data
Used by optimize operations only, not during dry / live runs.
"""
return {pair: self.advise_indicators(pair_data, {'pair': pair})
for pair, pair_data in tickerdata.items()}
for pair, pair_data in data.items()}
def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Populate indicators that will be used in the Buy and Sell strategy
This method should not be overridden.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""

4
freqtrade/templates/base_config.json.j2
View File

@ -11,6 +11,7 @@
"sell": 30
},
"bid_strategy": {
"price_side": "bid",
"ask_last_balance": 0.0,
"use_order_book": false,
"order_book_top": 1,
@ -20,9 +21,10 @@
}
},
"ask_strategy": {
"price_side": "ask",
"use_order_book": false,
"order_book_min": 1,
"order_book_max": 9,
"order_book_max": 1,
"use_sell_signal": true,
"sell_profit_only": false,
"ignore_roi_if_buy_signal": false

21
freqtrade/templates/base_hyperopt.py.j2
View File

@ -21,7 +21,7 @@ class {{ hyperopt }}(IHyperOpt):
"""
This is a Hyperopt template to get you started.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
More information in the documentation: https://www.freqtrade.io/en/latest/hyperopt/
You should:
- Add any lib you need to build your hyperopt.
@ -29,11 +29,14 @@ class {{ hyperopt }}(IHyperOpt):
You must keep:
- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
The roi_space, generate_roi_table, stoploss_space methods are no longer required to be
copied in every custom hyperopt. However, you may override them if you need the
'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
Sample implementation of these methods can be found in
https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py
The methods roi_space, generate_roi_table and stoploss_space are not required
and are provided by default.
However, you may override them if you need 'roi' and 'stoploss' spaces that
differ from the defaults offered by Freqtrade.
Sample implementation of these methods will be copied to `user_data/hyperopts` when
creating the user-data directory using `freqtrade create-userdir --userdir user_data`,
or is available online under the following URL:
https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py.
"""
@staticmethod
@ -63,6 +66,9 @@ class {{ hyperopt }}(IHyperOpt):
dataframe['close'], dataframe['sar']
))
# Check that the candle had volume
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
@ -108,6 +114,9 @@ class {{ hyperopt }}(IHyperOpt):
dataframe['sar'], dataframe['close']
))
# Check that the candle had volume
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),

2
freqtrade/templates/base_strategy.py.j2
View File

@ -99,7 +99,7 @@ class {{ strategy }}(IStrategy):
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""

25
freqtrade/templates/sample_hyperopt.py
View File

@ -20,23 +20,28 @@ import freqtrade.vendor.qtpylib.indicators as qtpylib
class SampleHyperOpt(IHyperOpt):
"""
This is a sample Hyperopt to inspire you.
Feel free to customize it.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
More information in the documentation: https://www.freqtrade.io/en/latest/hyperopt/
You should:
- Rename the class name to some unique name.
- Add any methods you want to build your hyperopt.
- Add any lib you need to build your hyperopt.
An easier way to get a new hyperopt file is by using
`freqtrade new-hyperopt --hyperopt MyCoolHyperopt`.
You must keep:
- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
The roi_space, generate_roi_table, stoploss_space methods are no longer required to be
copied in every custom hyperopt. However, you may override them if you need the
'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
Sample implementation of these methods can be found in
https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py
The methods roi_space, generate_roi_table and stoploss_space are not required
and are provided by default.
However, you may override them if you need 'roi' and 'stoploss' spaces that
differ from the defaults offered by Freqtrade.
Sample implementation of these methods will be copied to `user_data/hyperopts` when
creating the user-data directory using `freqtrade create-userdir --userdir user_data`,
or is available online under the following URL:
https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py.
"""
@staticmethod
@ -73,6 +78,9 @@ class SampleHyperOpt(IHyperOpt):
dataframe['close'], dataframe['sar']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
@ -133,6 +141,9 @@ class SampleHyperOpt(IHyperOpt):
dataframe['sar'], dataframe['close']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),

13
freqtrade/templates/sample_hyperopt_advanced.py
View File

@ -22,7 +22,7 @@ class AdvancedSampleHyperOpt(IHyperOpt):
This is a sample hyperopt to inspire you.
Feel free to customize it.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
More information in the documentation: https://www.freqtrade.io/en/latest/hyperopt/
You should:
- Rename the class name to some unique name.
@ -32,8 +32,9 @@ class AdvancedSampleHyperOpt(IHyperOpt):
You must keep:
- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
The roi_space, generate_roi_table, stoploss_space methods are no longer required to be
copied in every custom hyperopt. However, you may override them if you need the
The methods roi_space, generate_roi_table and stoploss_space are not required
and are provided by default.
However, you may override them if you need the
'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
This sample illustrates how to override these methods.
@ -92,6 +93,9 @@ class AdvancedSampleHyperOpt(IHyperOpt):
dataframe['close'], dataframe['sar']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
@ -152,6 +156,9 @@ class AdvancedSampleHyperOpt(IHyperOpt):
dataframe['sar'], dataframe['close']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),

126
freqtrade/templates/sample_strategy.py
View File

@ -116,7 +116,7 @@ class SampleStrategy(IStrategy):
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
@ -124,24 +124,70 @@ class SampleStrategy(IStrategy):
# Momentum Indicators
# ------------------------------------
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# # Plus Directional Indicator / Movement
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# # Minus Directional Indicator / Movement
# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # Aroon, Aroon Oscillator
# aroon = ta.AROON(dataframe)
# dataframe['aroonup'] = aroon['aroonup']
# dataframe['aroondown'] = aroon['aroondown']
# dataframe['aroonosc'] = ta.AROONOSC(dataframe)
# # Awesome oscillator
# # Awesome Oscillator
# dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
# # Commodity Channel Index: values Oversold:<-100, Overbought:>100
# # Keltner Channel
# keltner = qtpylib.keltner_channel(dataframe)
# dataframe["kc_upperband"] = keltner["upper"]
# dataframe["kc_lowerband"] = keltner["lower"]
# dataframe["kc_middleband"] = keltner["mid"]
# dataframe["kc_percent"] = (
# (dataframe["close"] - dataframe["kc_lowerband"]) /
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
# )
# dataframe["kc_width"] = (
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
# )
# # Ultimate Oscillator
# dataframe['uo'] = ta.ULTOSC(dataframe)
# # Commodity Channel Index: values [Oversold:-100, Overbought:100]
# dataframe['cci'] = ta.CCI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
# rsi = 0.1 * (dataframe['rsi'] - 50)
# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# # Stochastic Slow
# stoch = ta.STOCH(dataframe)
# dataframe['slowd'] = stoch['slowd']
# dataframe['slowk'] = stoch['slowk']
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# # Stochastic RSI
# stoch_rsi = ta.STOCHRSI(dataframe)
# dataframe['fastd_rsi'] = stoch_rsi['fastd']
# dataframe['fastk_rsi'] = stoch_rsi['fastk']
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
@ -151,60 +197,58 @@ class SampleStrategy(IStrategy):
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# # Minus Directional Indicator / Movement
# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # Plus Directional Indicator / Movement
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # ROC
# dataframe['roc'] = ta.ROC(dataframe)
# # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
# rsi = 0.1 * (dataframe['rsi'] - 50)
# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# # Stoch
# stoch = ta.STOCH(dataframe)
# dataframe['slowd'] = stoch['slowd']
# dataframe['slowk'] = stoch['slowk']
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# # Stoch RSI
# stoch_rsi = ta.STOCHRSI(dataframe)
# dataframe['fastd_rsi'] = stoch_rsi['fastd']
# dataframe['fastk_rsi'] = stoch_rsi['fastk']
# Overlap Studies
# ------------------------------------
# Bollinger bands
# Bollinger Bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe["bb_percent"] = (
(dataframe["close"] - dataframe["bb_lowerband"]) /
(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
)
dataframe["bb_width"] = (
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
)
# Bollinger Bands - Weighted (EMA based instead of SMA)
# weighted_bollinger = qtpylib.weighted_bollinger_bands(
# qtpylib.typical_price(dataframe), window=20, stds=2
# )
# dataframe["wbb_upperband"] = weighted_bollinger["upper"]
# dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
# dataframe["wbb_middleband"] = weighted_bollinger["mid"]
# dataframe["wbb_percent"] = (
# (dataframe["close"] - dataframe["wbb_lowerband"]) /
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
# )
# dataframe["wbb_width"] = (
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) /
# dataframe["wbb_middleband"]
# )
# # EMA - Exponential Moving Average
# dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
# dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
# dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
# dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
# dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
# dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# # SMA - Simple Moving Average
# dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
# dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
# dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
# dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
# dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
# dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
# dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
# SAR Parabol
# Parabolic SAR
dataframe['sar'] = ta.SAR(dataframe)
# TEMA - Triple Exponential Moving Average
@ -264,7 +308,7 @@ class SampleStrategy(IStrategy):
# # Chart type
# # ------------------------------------
# # Heikinashi stategy
# # Heikin Ashi Strategy
# heikinashi = qtpylib.heikinashi(dataframe)
# dataframe['ha_open'] = heikinashi['open']
# dataframe['ha_close'] = heikinashi['close']

6
freqtrade/templates/strategy_analysis_example.ipynb
View File

@ -190,7 +190,6 @@
"# Analyze the above\n",
"parallel_trades = analyze_trade_parallelism(trades, '5m')\n",
"\n",
"\n",
"parallel_trades.plot()"
]
},
@ -212,11 +211,14 @@
"from freqtrade.plot.plotting import generate_candlestick_graph\n",
"# Limit graph period to keep plotly quick and reactive\n",
"\n",
"# Filter trades to one pair\n",
"trades_red = trades.loc[trades['pair'] == pair]\n",
"\n",
"data_red = data['2019-06-01':'2019-06-10']\n",
"# Generate candlestick graph\n",
"graph = generate_candlestick_graph(pair=pair,\n",
" data=data_red,\n",
" trades=trades,\n",
" trades=trades_red,\n",
" indicators1=['sma20', 'ema50', 'ema55'],\n",
" indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']\n",
" )\n",

123
freqtrade/templates/subtemplates/indicators_full.j2
View File

@ -2,24 +2,70 @@
# Momentum Indicators
# ------------------------------------
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# # Plus Directional Indicator / Movement
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# # Minus Directional Indicator / Movement
# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # Aroon, Aroon Oscillator
# aroon = ta.AROON(dataframe)
# dataframe['aroonup'] = aroon['aroonup']
# dataframe['aroondown'] = aroon['aroondown']
# dataframe['aroonosc'] = ta.AROONOSC(dataframe)
# # Awesome oscillator
# # Awesome Oscillator
# dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
# # Commodity Channel Index: values Oversold:<-100, Overbought:>100
# # Keltner Channel
# keltner = qtpylib.keltner_channel(dataframe)
# dataframe["kc_upperband"] = keltner["upper"]
# dataframe["kc_lowerband"] = keltner["lower"]
# dataframe["kc_middleband"] = keltner["mid"]
# dataframe["kc_percent"] = (
# (dataframe["close"] - dataframe["kc_lowerband"]) /
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
# )
# dataframe["kc_width"] = (
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
# )
# # Ultimate Oscillator
# dataframe['uo'] = ta.ULTOSC(dataframe)
# # Commodity Channel Index: values [Oversold:-100, Overbought:100]
# dataframe['cci'] = ta.CCI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
# rsi = 0.1 * (dataframe['rsi'] - 50)
# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# # Stochastic Slow
# stoch = ta.STOCH(dataframe)
# dataframe['slowd'] = stoch['slowd']
# dataframe['slowk'] = stoch['slowk']
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# # Stochastic RSI
# stoch_rsi = ta.STOCHRSI(dataframe)
# dataframe['fastd_rsi'] = stoch_rsi['fastd']
# dataframe['fastk_rsi'] = stoch_rsi['fastk']
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
@ -29,60 +75,57 @@ dataframe['macdhist'] = macd['macdhist']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# # Minus Directional Indicator / Movement
# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # Plus Directional Indicator / Movement
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # ROC
# dataframe['roc'] = ta.ROC(dataframe)
# # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
# rsi = 0.1 * (dataframe['rsi'] - 50)
# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# # Stoch
# stoch = ta.STOCH(dataframe)
# dataframe['slowd'] = stoch['slowd']
# dataframe['slowk'] = stoch['slowk']
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# # Stoch RSI
# stoch_rsi = ta.STOCHRSI(dataframe)
# dataframe['fastd_rsi'] = stoch_rsi['fastd']
# dataframe['fastk_rsi'] = stoch_rsi['fastk']
# Overlap Studies
# ------------------------------------
# Bollinger bands
# Bollinger Bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe["bb_percent"] = (
(dataframe["close"] - dataframe["bb_lowerband"]) /
(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
)
dataframe["bb_width"] = (
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
)
# Bollinger Bands - Weighted (EMA based instead of SMA)
# weighted_bollinger = qtpylib.weighted_bollinger_bands(
# qtpylib.typical_price(dataframe), window=20, stds=2
# )
# dataframe["wbb_upperband"] = weighted_bollinger["upper"]
# dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
# dataframe["wbb_middleband"] = weighted_bollinger["mid"]
# dataframe["wbb_percent"] = (
# (dataframe["close"] - dataframe["wbb_lowerband"]) /
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
# )
# dataframe["wbb_width"] = (
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / dataframe["wbb_middleband"]
# )
# # EMA - Exponential Moving Average
# dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
# dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
# dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
# dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
# dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
# dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# # SMA - Simple Moving Average
# dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
# dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
# dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
# dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
# dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
# dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
# dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
# SAR Parabol
# Parabolic SAR
dataframe['sar'] = ta.SAR(dataframe)
# TEMA - Triple Exponential Moving Average
@ -142,7 +185,7 @@ dataframe['htleadsine'] = hilbert['leadsine']
# # Chart type
# # ------------------------------------
# # Heikinashi stategy
# # Heikin Ashi Strategy
# heikinashi = qtpylib.heikinashi(dataframe)
# dataframe['ha_open'] = heikinashi['open']
# dataframe['ha_close'] = heikinashi['close']

2
freqtrade/wallets.py
View File

@ -74,7 +74,7 @@ class Wallets:
)
for trade in open_trades:
curr = trade.pair.split('/')[0]
curr = self._exchange.get_pair_base_currency(trade.pair)
_wallets[curr] = Wallet(
curr,
trade.amount,

62
freqtrade/worker.py
View File

@ -4,6 +4,7 @@ Main Freqtrade worker class.
import logging
import time
import traceback
from os import getpid
from typing import Any, Callable, Dict, Optional
import sdnotify
@ -26,12 +27,15 @@ class Worker:
"""
Init all variables and objects the bot needs to work
"""
logger.info('Starting worker %s', __version__)
logger.info(f"Starting worker {__version__}")
self._args = args
self._config = config
self._init(False)
self.last_throttle_start_time: float = 0
self._heartbeat_msg: float = 0
# Tell systemd that we completed initialization phase
if self._sd_notify:
logger.debug("sd_notify: READY=1")
@ -48,10 +52,10 @@ class Worker:
# Init the instance of the bot
self.freqtrade = FreqtradeBot(self._config)
self._throttle_secs = self._config.get('internals', {}).get(
'process_throttle_secs',
constants.PROCESS_THROTTLE_SECS
)
internals_config = self._config.get('internals', {})
self._throttle_secs = internals_config.get('process_throttle_secs',
constants.PROCESS_THROTTLE_SECS)
self._heartbeat_interval = internals_config.get('heartbeat_interval', 60)
self._sd_notify = sdnotify.SystemdNotifier() if \
self._config.get('internals', {}).get('sd_notify', False) else None
@ -63,31 +67,33 @@ class Worker:
if state == State.RELOAD_CONF:
self._reconfigure()
def _worker(self, old_state: Optional[State], throttle_secs: Optional[float] = None) -> State:
def _worker(self, old_state: Optional[State]) -> State:
"""
Trading routine that must be run at each loop
The main routine that runs each throttling iteration and handles the states.
:param old_state: the previous service state from the previous call
:return: current service state
"""
state = self.freqtrade.state
if throttle_secs is None:
throttle_secs = self._throttle_secs
# Log state transition
if state != old_state:
self.freqtrade.notify_status(f'{state.name.lower()}')
logger.info('Changing state to: %s', state.name)
logger.info(f"Changing state to: {state.name}")
if state == State.RUNNING:
self.freqtrade.startup()
# Reset heartbeat timestamp to log the heartbeat message at
# first throttling iteration when the state changes
self._heartbeat_msg = 0
if state == State.STOPPED:
# Ping systemd watchdog before sleeping in the stopped state
if self._sd_notify:
logger.debug("sd_notify: WATCHDOG=1\\nSTATUS=State: STOPPED.")
self._sd_notify.notify("WATCHDOG=1\nSTATUS=State: STOPPED.")
time.sleep(throttle_secs)
self._throttle(func=self._process_stopped, throttle_secs=self._throttle_secs)
elif state == State.RUNNING:
# Ping systemd watchdog before throttling
@ -95,28 +101,40 @@ class Worker:
logger.debug("sd_notify: WATCHDOG=1\\nSTATUS=State: RUNNING.")
self._sd_notify.notify("WATCHDOG=1\nSTATUS=State: RUNNING.")
self._throttle(func=self._process, min_secs=throttle_secs)
self._throttle(func=self._process_running, throttle_secs=self._throttle_secs)
if self._heartbeat_interval:
now = time.time()
if (now - self._heartbeat_msg) > self._heartbeat_interval:
logger.info(f"Bot heartbeat. PID={getpid()}, "
f"version='{__version__}', state='{state.name}'")
self._heartbeat_msg = now
return state
def _throttle(self, func: Callable[..., Any], min_secs: float, *args, **kwargs) -> Any:
def _throttle(self, func: Callable[..., Any], throttle_secs: float, *args, **kwargs) -> Any:
"""
Throttles the given callable that it
takes at least `min_secs` to finish execution.
:param func: Any callable
:param min_secs: minimum execution time in seconds
:return: Any
:param throttle_secs: throttling interation execution time limit in seconds
:return: Any (result of execution of func)
"""
start = time.time()
self.last_throttle_start_time = time.time()
logger.debug("========================================")
result = func(*args, **kwargs)
end = time.time()
duration = max(min_secs - (end - start), 0.0)
logger.debug('Throttling %s for %.2f seconds', func.__name__, duration)
time.sleep(duration)
time_passed = time.time() - self.last_throttle_start_time
sleep_duration = max(throttle_secs - time_passed, 0.0)
logger.debug(f"Throttling with '{func.__name__}()': sleep for {sleep_duration:.2f} s, "
f"last iteration took {time_passed:.2f} s.")
time.sleep(sleep_duration)
return result
def _process(self) -> None:
logger.debug("========================================")
def _process_stopped(self) -> None:
# Maybe do here something in the future...
pass
def _process_running(self) -> None:
try:
self.freqtrade.process()
except TemporaryError as error:

4
mkdocs.yml
View File

@ -1,8 +1,8 @@
site_name: Freqtrade
nav:
- About: index.md
- Installation: installation.md
- Home: index.md
- Installation Docker: docker.md
- Installation: installation.md
- Configuration: configuration.md
- Strategy Customization: strategy-customization.md
- Stoploss: stoploss.md

10
requirements-common.txt
View File

@ -1,17 +1,17 @@
# requirements without requirements installable via conda
# mainly used for Raspberry pi installs
ccxt==1.22.61
ccxt==1.23.81
SQLAlchemy==1.3.13
python-telegram-bot==12.4.2
arrow==0.15.5
cachetools==4.0.0
requests==2.22.0
requests==2.23.0
urllib3==1.25.8
wrapt==1.12.0
wrapt==1.12.1
jsonschema==3.2.0
TA-Lib==0.4.17
tabulate==0.8.6
coinmarketcap==5.0.3
pycoingecko==1.2.0
jinja2==2.11.1
# find first, C search in arrays
@ -30,4 +30,4 @@ flask==1.1.1
colorama==0.4.3
# Building config files interactively
questionary==1.5.1
prompt-toolkit==3.0.3
prompt-toolkit==3.0.4

4
requirements-hyperopt.txt
View File

@ -3,7 +3,7 @@
# Required for hyperopt
scipy==1.4.1
scikit-learn==0.22.1
scikit-optimize==0.7.2
scikit-learn==0.22.2.post1
scikit-optimize==0.7.4
filelock==3.0.12
joblib==0.14.1

2
requirements-plot.txt
View File

@ -1,5 +1,5 @@
# Include all requirements to run the bot.
-r requirements.txt
plotly==4.5.0
plotly==4.5.3

2
setup.py
View File

@ -73,7 +73,7 @@ setup(name='freqtrade',
'jsonschema',
'TA-Lib',
'tabulate',
'coinmarketcap',
'pycoingecko',
'py_find_1st',
'python-rapidjson',
'sdnotify',

66
tests/commands/test_commands.py
View File

@ -217,8 +217,9 @@ def test_list_markets(mocker, markets, capsys):
]
start_list_markets(get_args(args), False)
captured = capsys.readouterr()
assert ("Exchange Bittrex has 9 active markets: "
"BLK/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/USD, NEO/BTC, TKN/BTC, XLTCUSDT, XRP/BTC.\n"
assert ("Exchange Bittrex has 10 active markets: "
"BLK/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/ETH, LTC/USD, NEO/BTC, "
"TKN/BTC, XLTCUSDT, XRP/BTC.\n"
in captured.out)
patch_exchange(mocker, api_mock=api_mock, id="binance")
@ -231,7 +232,7 @@ def test_list_markets(mocker, markets, capsys):
pargs['config'] = None
start_list_markets(pargs, False)
captured = capsys.readouterr()
assert re.match("\nExchange Binance has 9 active markets:\n",
assert re.match("\nExchange Binance has 10 active markets:\n",
captured.out)
patch_exchange(mocker, api_mock=api_mock, id="bittrex")
@ -243,8 +244,8 @@ def test_list_markets(mocker, markets, capsys):
]
start_list_markets(get_args(args), False)
captured = capsys.readouterr()
assert ("Exchange Bittrex has 11 markets: "
"BLK/BTC, BTT/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/USD, LTC/USDT, NEO/BTC, "
assert ("Exchange Bittrex has 12 markets: "
"BLK/BTC, BTT/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/ETH, LTC/USD, LTC/USDT, NEO/BTC, "
"TKN/BTC, XLTCUSDT, XRP/BTC.\n"
in captured.out)
@ -256,8 +257,8 @@ def test_list_markets(mocker, markets, capsys):
]
start_list_markets(get_args(args), True)
captured = capsys.readouterr()
assert ("Exchange Bittrex has 8 active pairs: "
"BLK/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/USD, NEO/BTC, TKN/BTC, XRP/BTC.\n"
assert ("Exchange Bittrex has 9 active pairs: "
"BLK/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/ETH, LTC/USD, NEO/BTC, TKN/BTC, XRP/BTC.\n"
in captured.out)
# Test list-pairs subcommand with --all: all pairs
@ -268,8 +269,8 @@ def test_list_markets(mocker, markets, capsys):
]
start_list_markets(get_args(args), True)
captured = capsys.readouterr()
assert ("Exchange Bittrex has 10 pairs: "
"BLK/BTC, BTT/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/USD, LTC/USDT, NEO/BTC, "
assert ("Exchange Bittrex has 11 pairs: "
"BLK/BTC, BTT/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/ETH, LTC/USD, LTC/USDT, NEO/BTC, "
"TKN/BTC, XRP/BTC.\n"
in captured.out)
@ -282,8 +283,8 @@ def test_list_markets(mocker, markets, capsys):
]
start_list_markets(get_args(args), False)
captured = capsys.readouterr()
assert ("Exchange Bittrex has 5 active markets with ETH, LTC as base currencies: "
"ETH/BTC, ETH/USDT, LTC/BTC, LTC/USD, XLTCUSDT.\n"
assert ("Exchange Bittrex has 6 active markets with ETH, LTC as base currencies: "
"ETH/BTC, ETH/USDT, LTC/BTC, LTC/ETH, LTC/USD, XLTCUSDT.\n"
in captured.out)
# active markets, base=LTC
@ -295,8 +296,8 @@ def test_list_markets(mocker, markets, capsys):
]
start_list_markets(get_args(args), False)
captured = capsys.readouterr()
assert ("Exchange Bittrex has 3 active markets with LTC as base currency: "
"LTC/BTC, LTC/USD, XLTCUSDT.\n"
assert ("Exchange Bittrex has 4 active markets with LTC as base currency: "
"LTC/BTC, LTC/ETH, LTC/USD, XLTCUSDT.\n"
in captured.out)
# active markets, quote=USDT, USD
@ -384,7 +385,7 @@ def test_list_markets(mocker, markets, capsys):
]
start_list_markets(get_args(args), False)
captured = capsys.readouterr()
assert ("Exchange Bittrex has 9 active markets:\n"
assert ("Exchange Bittrex has 10 active markets:\n"
in captured.out)
# Test tabular output, no markets found
@ -407,7 +408,7 @@ def test_list_markets(mocker, markets, capsys):
]
start_list_markets(get_args(args), False)
captured = capsys.readouterr()
assert ('["BLK/BTC","ETH/BTC","ETH/USDT","LTC/BTC","LTC/USD","NEO/BTC",'
assert ('["BLK/BTC","ETH/BTC","ETH/USDT","LTC/BTC","LTC/ETH","LTC/USD","NEO/BTC",'
'"TKN/BTC","XLTCUSDT","XRP/BTC"]'
in captured.out)
@ -446,11 +447,6 @@ def test_create_datadir_failed(caplog):
def test_create_datadir(caplog, mocker):
# Ensure that caplog is empty before starting ...
# Should prevent random failures.
caplog.clear()
# Added assert here to analyze random test-failures ...
assert len(caplog.record_tuples) == 0
cud = mocker.patch("freqtrade.commands.deploy_commands.create_userdata_dir", MagicMock())
csf = mocker.patch("freqtrade.commands.deploy_commands.copy_sample_files", MagicMock())
@ -463,7 +459,6 @@ def test_create_datadir(caplog, mocker):
assert cud.call_count == 1
assert csf.call_count == 1
assert len(caplog.record_tuples) == 0
def test_start_new_strategy(mocker, caplog):
@ -778,6 +773,20 @@ def test_hyperopt_list(mocker, capsys, hyperopt_results):
assert all(x not in captured.out
for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
" 11/12", " 12/12"])
args = [
"hyperopt-list",
"--profitable"
]
pargs = get_args(args)
pargs['config'] = None
start_hyperopt_list(pargs)
captured = capsys.readouterr()
assert all(x in captured.out
for x in [" 2/12", " 10/12", "Best result:", "Buy hyperspace params",
"Sell hyperspace params", "ROI table", "Stoploss"])
assert all(x not in captured.out
for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
" 11/12", " 12/12"])
args = [
"hyperopt-list",
"--no-details",
@ -893,6 +902,21 @@ def test_hyperopt_list(mocker, capsys, hyperopt_results):
assert all(x not in captured.out
for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 7/12", " 8/12"
" 9/12", " 10/12", " 11/12", " 12/12"])
args = [
"hyperopt-list",
"--no-details",
"--export-csv", "test_file.csv"
]
pargs = get_args(args)
pargs['config'] = None
start_hyperopt_list(pargs)
captured = capsys.readouterr()
assert all(x in captured.out
for x in ["CSV-File created!"])
f = Path("test_file.csv")
assert 'Best,1,2,-1.25%,-0.00125625,,-2.51,"3,930.0 m",0.43662' in f.read_text()
assert f.is_file()
f.unlink()
def test_hyperopt_show(mocker, capsys, hyperopt_results):

58
tests/conftest.py
View File

@ -15,7 +15,7 @@ from telegram import Chat, Message, Update
from freqtrade import constants, persistence
from freqtrade.commands import Arguments
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.data.converter import ohlcv_to_dataframe
from freqtrade.edge import Edge, PairInfo
from freqtrade.exchange import Exchange
from freqtrade.freqtradebot import FreqtradeBot
@ -167,23 +167,23 @@ def patch_get_signal(freqtrade: FreqtradeBot, value=(True, False)) -> None:
@pytest.fixture(autouse=True)
def patch_coinmarketcap(mocker) -> None:
def patch_coingekko(mocker) -> None:
"""
Mocker to coinmarketcap to speed up tests
:param mocker: mocker to patch coinmarketcap class
Mocker to coingekko to speed up tests
:param mocker: mocker to patch coingekko class
:return: None
"""
tickermock = MagicMock(return_value={'price_usd': 12345.0})
listmock = MagicMock(return_value={'data': [{'id': 1, 'name': 'Bitcoin', 'symbol': 'BTC',
tickermock = MagicMock(return_value={'bitcoin': {'usd': 12345.0}, 'ethereum': {'usd': 12345.0}})
listmock = MagicMock(return_value=[{'id': 'bitcoin', 'name': 'Bitcoin', 'symbol': 'btc',
'website_slug': 'bitcoin'},
{'id': 1027, 'name': 'Ethereum', 'symbol': 'ETH',
{'id': 'ethereum', 'name': 'Ethereum', 'symbol': 'eth',
'website_slug': 'ethereum'}
]})
])
mocker.patch.multiple(
'freqtrade.rpc.fiat_convert.Market',
ticker=tickermock,
listings=listmock,
'freqtrade.rpc.fiat_convert.CoinGeckoAPI',
get_price=tickermock,
get_coins_list=listmock,
)
@ -575,7 +575,34 @@ def get_markets():
}
},
'info': {},
},
'LTC/ETH': {
'id': 'LTCETH',
'symbol': 'LTC/ETH',
'base': 'LTC',
'quote': 'ETH',
'active': True,
'precision': {
'base': 8,
'quote': 8,
'amount': 3,
'price': 5
},
'limits': {
'amount': {
'min': 0.001,
'max': 10000000.0
},
'price': {
'min': 1e-05,
'max': 1000.0
},
'cost': {
'min': 0.01,
'max': None
}
},
},
}
@ -822,7 +849,7 @@ def order_book_l2():
@pytest.fixture
def ticker_history_list():
def ohlcv_history_list():
return [
[
1511686200000, # unix timestamp ms
@ -852,8 +879,9 @@ def ticker_history_list():
@pytest.fixture
def ticker_history(ticker_history_list):
return parse_ticker_dataframe(ticker_history_list, "5m", pair="UNITTEST/BTC", fill_missing=True)
def ohlcv_history(ohlcv_history_list):
return ohlcv_to_dataframe(ohlcv_history_list, "5m", pair="UNITTEST/BTC",
fill_missing=True)
@pytest.fixture
@ -1168,7 +1196,7 @@ def tickers():
@pytest.fixture
def result(testdatadir):
with (testdatadir / 'UNITTEST_BTC-1m.json').open('r') as data_file:
return parse_ticker_dataframe(json.load(data_file), '1m', pair="UNITTEST/BTC",
return ohlcv_to_dataframe(json.load(data_file), '1m', pair="UNITTEST/BTC",
fill_missing=True)

34
tests/data/test_btanalysis.py
View File

@ -1,16 +1,19 @@
from pathlib import Path
from unittest.mock import MagicMock
import pytest
from arrow import Arrow
from pandas import DataFrame, DateOffset, to_datetime
from pandas import DataFrame, DateOffset, Timestamp, to_datetime
from freqtrade.configuration import TimeRange
from freqtrade.data.btanalysis import (BT_DATA_COLUMNS,
combine_tickers_with_mean,
analyze_trade_parallelism,
calculate_max_drawdown,
combine_dataframes_with_mean,
create_cum_profit,
extract_trades_of_period,
load_backtest_data, load_trades,
load_trades_from_db, analyze_trade_parallelism)
load_trades_from_db)
from freqtrade.data.history import load_data, load_pair_history
from tests.test_persistence import create_mock_trades
@ -109,7 +112,7 @@ def test_load_trades(default_conf, mocker):
db_mock.reset_mock()
bt_mock.reset_mock()
default_conf['exportfilename'] = "testfile.json"
default_conf['exportfilename'] = Path("testfile.json")
load_trades("file",
db_url=default_conf.get('db_url'),
exportfilename=default_conf.get('exportfilename'),)
@ -118,13 +121,10 @@ def test_load_trades(default_conf, mocker):
assert bt_mock.call_count == 1
def test_combine_tickers_with_mean(testdatadir):
def test_combine_dataframes_with_mean(testdatadir):
pairs = ["ETH/BTC", "ADA/BTC"]
tickers = load_data(datadir=testdatadir,
pairs=pairs,
timeframe='5m'
)
df = combine_tickers_with_mean(tickers)
data = load_data(datadir=testdatadir, pairs=pairs, timeframe='5m')
df = combine_dataframes_with_mean(data)
assert isinstance(df, DataFrame)
assert "ETH/BTC" in df.columns
assert "ADA/BTC" in df.columns
@ -163,3 +163,17 @@ def test_create_cum_profit1(testdatadir):
assert "cum_profits" in cum_profits.columns
assert cum_profits.iloc[0]['cum_profits'] == 0
assert cum_profits.iloc[-1]['cum_profits'] == 0.0798005
def test_calculate_max_drawdown(testdatadir):
filename = testdatadir / "backtest-result_test.json"
bt_data = load_backtest_data(filename)
drawdown, h, low = calculate_max_drawdown(bt_data)
assert isinstance(drawdown, float)
assert pytest.approx(drawdown) == 0.21142322
assert isinstance(h, Timestamp)
assert isinstance(low, Timestamp)
assert h == Timestamp('2018-01-24 14:25:00', tz='UTC')
assert low == Timestamp('2018-01-30 04:45:00', tz='UTC')
with pytest.raises(ValueError, match='Trade dataframe empty.'):
drawdown, h, low = calculate_max_drawdown(DataFrame())

24
tests/data/test_converter.py
View File

@ -5,9 +5,12 @@ from freqtrade.configuration.timerange import TimeRange
from freqtrade.data.converter import (convert_ohlcv_format,
convert_trades_format,
ohlcv_fill_up_missing_data,
parse_ticker_dataframe, trim_dataframe)
from freqtrade.data.history import (get_timerange, load_data,
load_pair_history, validate_backtest_data)
ohlcv_to_dataframe,
trim_dataframe)
from freqtrade.data.history import (get_timerange,
load_data,
load_pair_history,
validate_backtest_data)
from tests.conftest import log_has
from tests.data.test_history import _backup_file, _clean_test_file
@ -16,15 +19,15 @@ def test_dataframe_correct_columns(result):
assert result.columns.tolist() == ['date', 'open', 'high', 'low', 'close', 'volume']
def test_parse_ticker_dataframe(ticker_history_list, caplog):
def test_ohlcv_to_dataframe(ohlcv_history_list, caplog):
columns = ['date', 'open', 'high', 'low', 'close', 'volume']
caplog.set_level(logging.DEBUG)
# Test file with BV data
dataframe = parse_ticker_dataframe(ticker_history_list, '5m',
pair="UNITTEST/BTC", fill_missing=True)
dataframe = ohlcv_to_dataframe(ohlcv_history_list, '5m', pair="UNITTEST/BTC",
fill_missing=True)
assert dataframe.columns.tolist() == columns
assert log_has('Parsing tickerlist to dataframe', caplog)
assert log_has('Converting candle (OHLCV) data to dataframe for pair UNITTEST/BTC.', caplog)
def test_ohlcv_fill_up_missing_data(testdatadir, caplog):
@ -84,7 +87,8 @@ def test_ohlcv_fill_up_missing_data2(caplog):
]
# Generate test-data without filling missing
data = parse_ticker_dataframe(ticks, timeframe, pair="UNITTEST/BTC", fill_missing=False)
data = ohlcv_to_dataframe(ticks, timeframe, pair="UNITTEST/BTC",
fill_missing=False)
assert len(data) == 3
caplog.set_level(logging.DEBUG)
data2 = ohlcv_fill_up_missing_data(data, timeframe, "UNITTEST/BTC")
@ -140,13 +144,13 @@ def test_ohlcv_drop_incomplete(caplog):
]
]
caplog.set_level(logging.DEBUG)
data = parse_ticker_dataframe(ticks, timeframe, pair="UNITTEST/BTC",
data = ohlcv_to_dataframe(ticks, timeframe, pair="UNITTEST/BTC",
fill_missing=False, drop_incomplete=False)
assert len(data) == 4
assert not log_has("Dropping last candle", caplog)
# Drop last candle
data = parse_ticker_dataframe(ticks, timeframe, pair="UNITTEST/BTC",
data = ohlcv_to_dataframe(ticks, timeframe, pair="UNITTEST/BTC",
fill_missing=False, drop_incomplete=True)
assert len(data) == 3

36
tests/data/test_dataprovider.py
View File

@ -7,19 +7,19 @@ from freqtrade.state import RunMode
from tests.conftest import get_patched_exchange
def test_ohlcv(mocker, default_conf, ticker_history):
def test_ohlcv(mocker, default_conf, ohlcv_history):
default_conf["runmode"] = RunMode.DRY_RUN
timeframe = default_conf["ticker_interval"]
exchange = get_patched_exchange(mocker, default_conf)
exchange._klines[("XRP/BTC", timeframe)] = ticker_history
exchange._klines[("UNITTEST/BTC", timeframe)] = ticker_history
exchange._klines[("XRP/BTC", timeframe)] = ohlcv_history
exchange._klines[("UNITTEST/BTC", timeframe)] = ohlcv_history
dp = DataProvider(default_conf, exchange)
assert dp.runmode == RunMode.DRY_RUN
assert ticker_history.equals(dp.ohlcv("UNITTEST/BTC", timeframe))
assert ohlcv_history.equals(dp.ohlcv("UNITTEST/BTC", timeframe))
assert isinstance(dp.ohlcv("UNITTEST/BTC", timeframe), DataFrame)
assert dp.ohlcv("UNITTEST/BTC", timeframe) is not ticker_history
assert dp.ohlcv("UNITTEST/BTC", timeframe, copy=False) is ticker_history
assert dp.ohlcv("UNITTEST/BTC", timeframe) is not ohlcv_history
assert dp.ohlcv("UNITTEST/BTC", timeframe, copy=False) is ohlcv_history
assert not dp.ohlcv("UNITTEST/BTC", timeframe).empty
assert dp.ohlcv("NONESENSE/AAA", timeframe).empty
@ -37,8 +37,8 @@ def test_ohlcv(mocker, default_conf, ticker_history):
assert dp.ohlcv("UNITTEST/BTC", timeframe).empty
def test_historic_ohlcv(mocker, default_conf, ticker_history):
historymock = MagicMock(return_value=ticker_history)
def test_historic_ohlcv(mocker, default_conf, ohlcv_history):
historymock = MagicMock(return_value=ohlcv_history)
mocker.patch("freqtrade.data.dataprovider.load_pair_history", historymock)
dp = DataProvider(default_conf, None)
@ -48,18 +48,18 @@ def test_historic_ohlcv(mocker, default_conf, ticker_history):
assert historymock.call_args_list[0][1]["timeframe"] == "5m"
def test_get_pair_dataframe(mocker, default_conf, ticker_history):
def test_get_pair_dataframe(mocker, default_conf, ohlcv_history):
default_conf["runmode"] = RunMode.DRY_RUN
ticker_interval = default_conf["ticker_interval"]
exchange = get_patched_exchange(mocker, default_conf)
exchange._klines[("XRP/BTC", ticker_interval)] = ticker_history
exchange._klines[("UNITTEST/BTC", ticker_interval)] = ticker_history
exchange._klines[("XRP/BTC", ticker_interval)] = ohlcv_history
exchange._klines[("UNITTEST/BTC", ticker_interval)] = ohlcv_history
dp = DataProvider(default_conf, exchange)
assert dp.runmode == RunMode.DRY_RUN
assert ticker_history.equals(dp.get_pair_dataframe("UNITTEST/BTC", ticker_interval))
assert ohlcv_history.equals(dp.get_pair_dataframe("UNITTEST/BTC", ticker_interval))
assert isinstance(dp.get_pair_dataframe("UNITTEST/BTC", ticker_interval), DataFrame)
assert dp.get_pair_dataframe("UNITTEST/BTC", ticker_interval) is not ticker_history
assert dp.get_pair_dataframe("UNITTEST/BTC", ticker_interval) is not ohlcv_history
assert not dp.get_pair_dataframe("UNITTEST/BTC", ticker_interval).empty
assert dp.get_pair_dataframe("NONESENSE/AAA", ticker_interval).empty
@ -73,7 +73,7 @@ def test_get_pair_dataframe(mocker, default_conf, ticker_history):
assert isinstance(dp.get_pair_dataframe("UNITTEST/BTC", ticker_interval), DataFrame)
assert dp.get_pair_dataframe("NONESENSE/AAA", ticker_interval).empty
historymock = MagicMock(return_value=ticker_history)
historymock = MagicMock(return_value=ohlcv_history)
mocker.patch("freqtrade.data.dataprovider.load_pair_history", historymock)
default_conf["runmode"] = RunMode.BACKTEST
dp = DataProvider(default_conf, exchange)
@ -82,11 +82,11 @@ def test_get_pair_dataframe(mocker, default_conf, ticker_history):
# assert dp.get_pair_dataframe("NONESENSE/AAA", ticker_interval).empty
def test_available_pairs(mocker, default_conf, ticker_history):
def test_available_pairs(mocker, default_conf, ohlcv_history):
exchange = get_patched_exchange(mocker, default_conf)
ticker_interval = default_conf["ticker_interval"]
exchange._klines[("XRP/BTC", ticker_interval)] = ticker_history
exchange._klines[("UNITTEST/BTC", ticker_interval)] = ticker_history
exchange._klines[("XRP/BTC", ticker_interval)] = ohlcv_history
exchange._klines[("UNITTEST/BTC", ticker_interval)] = ohlcv_history
dp = DataProvider(default_conf, exchange)
assert len(dp.available_pairs) == 2
@ -96,7 +96,7 @@ def test_available_pairs(mocker, default_conf, ticker_history):
]
def test_refresh(mocker, default_conf, ticker_history):
def test_refresh(mocker, default_conf, ohlcv_history):
refresh_mock = MagicMock()
mocker.patch("freqtrade.exchange.Exchange.refresh_latest_ohlcv", refresh_mock)

42
tests/data/test_history.py
View File

@ -12,7 +12,7 @@ from pandas import DataFrame
from pandas.testing import assert_frame_equal
from freqtrade.configuration import TimeRange
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.data.converter import ohlcv_to_dataframe
from freqtrade.data.history.history_utils import (
_download_pair_history, _download_trades_history,
_load_cached_data_for_updating, convert_trades_to_ohlcv, get_timerange,
@ -63,7 +63,7 @@ def _clean_test_file(file: Path) -> None:
file_swp.rename(file)
def test_load_data_30min_ticker(mocker, caplog, default_conf, testdatadir) -> None:
def test_load_data_30min_timeframe(mocker, caplog, default_conf, testdatadir) -> None:
ld = load_pair_history(pair='UNITTEST/BTC', timeframe='30m', datadir=testdatadir)
assert isinstance(ld, DataFrame)
assert not log_has(
@ -72,7 +72,7 @@ def test_load_data_30min_ticker(mocker, caplog, default_conf, testdatadir) -> No
)
def test_load_data_7min_ticker(mocker, caplog, default_conf, testdatadir) -> None:
def test_load_data_7min_timeframe(mocker, caplog, default_conf, testdatadir) -> None:
ld = load_pair_history(pair='UNITTEST/BTC', timeframe='7m', datadir=testdatadir)
assert isinstance(ld, DataFrame)
assert ld.empty
@ -82,8 +82,8 @@ def test_load_data_7min_ticker(mocker, caplog, default_conf, testdatadir) -> Non
)
def test_load_data_1min_ticker(ticker_history, mocker, caplog, testdatadir) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_historic_ohlcv', return_value=ticker_history)
def test_load_data_1min_timeframe(ohlcv_history, mocker, caplog, testdatadir) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_historic_ohlcv', return_value=ohlcv_history)
file = testdatadir / 'UNITTEST_BTC-1m.json'
_backup_file(file, copy_file=True)
load_data(datadir=testdatadir, timeframe='1m', pairs=['UNITTEST/BTC'])
@ -110,12 +110,12 @@ def test_load_data_startup_candles(mocker, caplog, default_conf, testdatadir) ->
assert ltfmock.call_args_list[0][1]['timerange'].startts == timerange.startts - 20 * 60
def test_load_data_with_new_pair_1min(ticker_history_list, mocker, caplog,
def test_load_data_with_new_pair_1min(ohlcv_history_list, mocker, caplog,
default_conf, testdatadir) -> None:
"""
Test load_pair_history() with 1 min ticker
Test load_pair_history() with 1 min timeframe
"""
mocker.patch('freqtrade.exchange.Exchange.get_historic_ohlcv', return_value=ticker_history_list)
mocker.patch('freqtrade.exchange.Exchange.get_historic_ohlcv', return_value=ohlcv_history_list)
exchange = get_patched_exchange(mocker, default_conf)
file = testdatadir / 'MEME_BTC-1m.json'
@ -188,7 +188,7 @@ def test_load_cached_data_for_updating(mocker, testdatadir) -> None:
with open(test_filename, "rt") as file:
test_data = json.load(file)
test_data_df = parse_ticker_dataframe(test_data, '1m', 'UNITTEST/BTC',
test_data_df = ohlcv_to_dataframe(test_data, '1m', 'UNITTEST/BTC',
fill_missing=False, drop_incomplete=False)
# now = last cached item + 1 hour
now_ts = test_data[-1][0] / 1000 + 60 * 60
@ -230,8 +230,8 @@ def test_load_cached_data_for_updating(mocker, testdatadir) -> None:
assert start_ts is None
def test_download_pair_history(ticker_history_list, mocker, default_conf, testdatadir) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_historic_ohlcv', return_value=ticker_history_list)
def test_download_pair_history(ohlcv_history_list, mocker, default_conf, testdatadir) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_historic_ohlcv', return_value=ohlcv_history_list)
exchange = get_patched_exchange(mocker, default_conf)
file1_1 = testdatadir / 'MEME_BTC-1m.json'
file1_5 = testdatadir / 'MEME_BTC-5m.json'
@ -293,7 +293,7 @@ def test_download_pair_history2(mocker, default_conf, testdatadir) -> None:
assert json_dump_mock.call_count == 2
def test_download_backtesting_data_exception(ticker_history, mocker, caplog,
def test_download_backtesting_data_exception(ohlcv_history, mocker, caplog,
default_conf, testdatadir) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_historic_ohlcv',
side_effect=Exception('File Error'))
@ -321,15 +321,15 @@ def test_load_partial_missing(testdatadir, caplog) -> None:
# Make sure we start fresh - test missing data at start
start = arrow.get('2018-01-01T00:00:00')
end = arrow.get('2018-01-11T00:00:00')
tickerdata = load_data(testdatadir, '5m', ['UNITTEST/BTC'], startup_candles=20,
data = load_data(testdatadir, '5m', ['UNITTEST/BTC'], startup_candles=20,
timerange=TimeRange('date', 'date', start.timestamp, end.timestamp))
assert log_has(
'Using indicator startup period: 20 ...', caplog
)
# timedifference in 5 minutes
td = ((end - start).total_seconds() // 60 // 5) + 1
assert td != len(tickerdata['UNITTEST/BTC'])
start_real = tickerdata['UNITTEST/BTC'].iloc[0, 0]
assert td != len(data['UNITTEST/BTC'])
start_real = data['UNITTEST/BTC'].iloc[0, 0]
assert log_has(f'Missing data at start for pair '
f'UNITTEST/BTC, data starts at {start_real.strftime("%Y-%m-%d %H:%M:%S")}',
caplog)
@ -337,14 +337,14 @@ def test_load_partial_missing(testdatadir, caplog) -> None:
caplog.clear()
start = arrow.get('2018-01-10T00:00:00')
end = arrow.get('2018-02-20T00:00:00')
tickerdata = load_data(datadir=testdatadir, timeframe='5m', pairs=['UNITTEST/BTC'],
data = load_data(datadir=testdatadir, timeframe='5m', pairs=['UNITTEST/BTC'],
timerange=TimeRange('date', 'date', start.timestamp, end.timestamp))
# timedifference in 5 minutes
td = ((end - start).total_seconds() // 60 // 5) + 1
assert td != len(tickerdata['UNITTEST/BTC'])
assert td != len(data['UNITTEST/BTC'])
# Shift endtime with +5 - as last candle is dropped (partial candle)
end_real = arrow.get(tickerdata['UNITTEST/BTC'].iloc[-1, 0]).shift(minutes=5)
end_real = arrow.get(data['UNITTEST/BTC'].iloc[-1, 0]).shift(minutes=5)
assert log_has(f'Missing data at end for pair '
f'UNITTEST/BTC, data ends at {end_real.strftime("%Y-%m-%d %H:%M:%S")}',
caplog)
@ -403,7 +403,7 @@ def test_get_timerange(default_conf, mocker, testdatadir) -> None:
default_conf.update({'strategy': 'DefaultStrategy'})
strategy = StrategyResolver.load_strategy(default_conf)
data = strategy.tickerdata_to_dataframe(
data = strategy.ohlcvdata_to_dataframe(
load_data(
datadir=testdatadir,
timeframe='1m',
@ -421,7 +421,7 @@ def test_validate_backtest_data_warn(default_conf, mocker, caplog, testdatadir)
default_conf.update({'strategy': 'DefaultStrategy'})
strategy = StrategyResolver.load_strategy(default_conf)
data = strategy.tickerdata_to_dataframe(
data = strategy.ohlcvdata_to_dataframe(
load_data(
datadir=testdatadir,
timeframe='1m',
@ -446,7 +446,7 @@ def test_validate_backtest_data(default_conf, mocker, caplog, testdatadir) -> No
strategy = StrategyResolver.load_strategy(default_conf)
timerange = TimeRange('index', 'index', 200, 250)
data = strategy.tickerdata_to_dataframe(
data = strategy.ohlcvdata_to_dataframe(
load_data(
datadir=testdatadir,
timeframe='5m',

32
tests/edge/test_edge.py
View File

@ -11,7 +11,7 @@ import pytest
from pandas import DataFrame, to_datetime
from freqtrade.exceptions import OperationalException
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.data.converter import ohlcv_to_dataframe
from freqtrade.edge import Edge, PairInfo
from freqtrade.strategy.interface import SellType
from tests.conftest import get_patched_freqtradebot, log_has
@ -26,7 +26,7 @@ from tests.optimize import (BTContainer, BTrade, _build_backtest_dataframe,
# 5) Stoploss and sell are hit. should sell on stoploss
####################################################################
ticker_start_time = arrow.get(2018, 10, 3)
tests_start_time = arrow.get(2018, 10, 3)
ticker_interval_in_minute = 60
_ohlc = {'date': 0, 'buy': 1, 'open': 2, 'high': 3, 'low': 4, 'close': 5, 'sell': 6, 'volume': 7}
@ -43,10 +43,10 @@ def _validate_ohlc(buy_ohlc_sell_matrice):
def _build_dataframe(buy_ohlc_sell_matrice):
_validate_ohlc(buy_ohlc_sell_matrice)
tickers = []
data = []
for ohlc in buy_ohlc_sell_matrice:
ticker = {
'date': ticker_start_time.shift(
d = {
'date': tests_start_time.shift(
minutes=(
ohlc[0] *
ticker_interval_in_minute)).timestamp *
@ -57,9 +57,9 @@ def _build_dataframe(buy_ohlc_sell_matrice):
'low': ohlc[4],
'close': ohlc[5],
'sell': ohlc[6]}
tickers.append(ticker)
data.append(d)
frame = DataFrame(tickers)
frame = DataFrame(data)
frame['date'] = to_datetime(frame['date'],
unit='ms',
utc=True,
@ -69,7 +69,7 @@ def _build_dataframe(buy_ohlc_sell_matrice):
def _time_on_candle(number):
return np.datetime64(ticker_start_time.shift(
return np.datetime64(tests_start_time.shift(
minutes=(number * ticker_interval_in_minute)).timestamp * 1000, 'ms')
@ -158,13 +158,13 @@ def test_edge_results(edge_conf, mocker, caplog, data) -> None:
assert len(trades) == len(data.trades)
if not results.empty:
assert round(results["profit_percent"].sum(), 3) == round(data.profit_perc, 3)
assert round(results["profit_ratio"].sum(), 3) == round(data.profit_perc, 3)
for c, trade in enumerate(data.trades):
res = results.iloc[c]
assert res.exit_type == trade.sell_reason
assert res.open_time == np.datetime64(_get_frame_time_from_offset(trade.open_tick))
assert res.close_time == np.datetime64(_get_frame_time_from_offset(trade.close_tick))
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)
def test_adjust(mocker, edge_conf):
@ -262,7 +262,7 @@ def mocked_load_data(datadir, pairs=[], timeframe='0m',
NEOBTC = [
[
ticker_start_time.shift(minutes=(x * ticker_interval_in_minute)).timestamp * 1000,
tests_start_time.shift(minutes=(x * ticker_interval_in_minute)).timestamp * 1000,
math.sin(x * hz) / 1000 + base,
math.sin(x * hz) / 1000 + base + 0.0001,
math.sin(x * hz) / 1000 + base - 0.0001,
@ -274,7 +274,7 @@ def mocked_load_data(datadir, pairs=[], timeframe='0m',
base = 0.002
LTCBTC = [
[
ticker_start_time.shift(minutes=(x * ticker_interval_in_minute)).timestamp * 1000,
tests_start_time.shift(minutes=(x * ticker_interval_in_minute)).timestamp * 1000,
math.sin(x * hz) / 1000 + base,
math.sin(x * hz) / 1000 + base + 0.0001,
math.sin(x * hz) / 1000 + base - 0.0001,
@ -282,8 +282,10 @@ def mocked_load_data(datadir, pairs=[], timeframe='0m',
123.45
] for x in range(0, 500)]
pairdata = {'NEO/BTC': parse_ticker_dataframe(NEOBTC, '1h', pair="NEO/BTC", fill_missing=True),
'LTC/BTC': parse_ticker_dataframe(LTCBTC, '1h', pair="LTC/BTC", fill_missing=True)}
pairdata = {'NEO/BTC': ohlcv_to_dataframe(NEOBTC, '1h', pair="NEO/BTC",
fill_missing=True),
'LTC/BTC': ohlcv_to_dataframe(LTCBTC, '1h', pair="LTC/BTC",
fill_missing=True)}
return pairdata

215
tests/exchange/test_exchange.py
View File

@ -400,13 +400,40 @@ def test_validate_stake_currency_error(default_conf, mocker, caplog):
def test_get_quote_currencies(default_conf, mocker):
ex = get_patched_exchange(mocker, default_conf)
assert set(ex.get_quote_currencies()) == set(['USD', 'BTC', 'USDT'])
assert set(ex.get_quote_currencies()) == set(['USD', 'ETH', 'BTC', 'USDT'])
@pytest.mark.parametrize('pair,expected', [
('XRP/BTC', 'BTC'),
('LTC/USD', 'USD'),
('ETH/USDT', 'USDT'),
('XLTCUSDT', 'USDT'),
('XRP/NOCURRENCY', ''),
])
def test_get_pair_quote_currency(default_conf, mocker, pair, expected):
ex = get_patched_exchange(mocker, default_conf)
assert ex.get_pair_quote_currency(pair) == expected
@pytest.mark.parametrize('pair,expected', [
('XRP/BTC', 'XRP'),
('LTC/USD', 'LTC'),
('ETH/USDT', 'ETH'),
('XLTCUSDT', 'LTC'),
('XRP/NOCURRENCY', ''),
])
def test_get_pair_base_currency(default_conf, mocker, pair, expected):
ex = get_patched_exchange(mocker, default_conf)
assert ex.get_pair_base_currency(pair) == expected
def test_validate_pairs(default_conf, mocker): # test exchange.validate_pairs directly
api_mock = MagicMock()
type(api_mock).markets = PropertyMock(return_value={
'ETH/BTC': {}, 'LTC/BTC': {}, 'XRP/BTC': {}, 'NEO/BTC': {}
'ETH/BTC': {'quote': 'BTC'},
'LTC/BTC': {'quote': 'BTC'},
'XRP/BTC': {'quote': 'BTC'},
'NEO/BTC': {'quote': 'BTC'},
})
id_mock = PropertyMock(return_value='test_exchange')
type(api_mock).id = id_mock
@ -454,9 +481,9 @@ def test_validate_pairs_exception(default_conf, mocker, caplog):
def test_validate_pairs_restricted(default_conf, mocker, caplog):
api_mock = MagicMock()
type(api_mock).markets = PropertyMock(return_value={
'ETH/BTC': {}, 'LTC/BTC': {},
'XRP/BTC': {'info': {'IsRestricted': True}},
'NEO/BTC': {'info': 'TestString'}, # info can also be a string ...
'ETH/BTC': {'quote': 'BTC'}, 'LTC/BTC': {'quote': 'BTC'},
'XRP/BTC': {'quote': 'BTC', 'info': {'IsRestricted': True}},
'NEO/BTC': {'quote': 'BTC', 'info': 'TestString'}, # info can also be a string ...
})
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes')
@ -469,6 +496,54 @@ def test_validate_pairs_restricted(default_conf, mocker, caplog):
f"on the exchange and eventually remove XRP/BTC from your whitelist.", caplog)
def test_validate_pairs_stakecompatibility(default_conf, mocker, caplog):
api_mock = MagicMock()
type(api_mock).markets = PropertyMock(return_value={
'ETH/BTC': {'quote': 'BTC'}, 'LTC/BTC': {'quote': 'BTC'},
'XRP/BTC': {'quote': 'BTC'}, 'NEO/BTC': {'quote': 'BTC'},
'HELLO-WORLD': {'quote': 'BTC'},
})
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes')
mocker.patch('freqtrade.exchange.Exchange._load_async_markets')
mocker.patch('freqtrade.exchange.Exchange.validate_stakecurrency')
Exchange(default_conf)
def test_validate_pairs_stakecompatibility_downloaddata(default_conf, mocker, caplog):
api_mock = MagicMock()
default_conf['stake_currency'] = ''
type(api_mock).markets = PropertyMock(return_value={
'ETH/BTC': {'quote': 'BTC'}, 'LTC/BTC': {'quote': 'BTC'},
'XRP/BTC': {'quote': 'BTC'}, 'NEO/BTC': {'quote': 'BTC'},
'HELLO-WORLD': {'quote': 'BTC'},
})
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes')
mocker.patch('freqtrade.exchange.Exchange._load_async_markets')
mocker.patch('freqtrade.exchange.Exchange.validate_stakecurrency')
Exchange(default_conf)
def test_validate_pairs_stakecompatibility_fail(default_conf, mocker, caplog):
default_conf['exchange']['pair_whitelist'].append('HELLO-WORLD')
api_mock = MagicMock()
type(api_mock).markets = PropertyMock(return_value={
'ETH/BTC': {'quote': 'BTC'}, 'LTC/BTC': {'quote': 'BTC'},
'XRP/BTC': {'quote': 'BTC'}, 'NEO/BTC': {'quote': 'BTC'},
'HELLO-WORLD': {'quote': 'USDT'},
})
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes')
mocker.patch('freqtrade.exchange.Exchange._load_async_markets')
mocker.patch('freqtrade.exchange.Exchange.validate_stakecurrency')
with pytest.raises(OperationalException, match=r"Stake-currency 'BTC' not compatible with.*"):
Exchange(default_conf)
@pytest.mark.parametrize("timeframe", [
('5m'), ("1m"), ("15m"), ("1h")
])
@ -506,7 +581,7 @@ def test_validate_timeframes_failed(default_conf, mocker):
mocker.patch('freqtrade.exchange.Exchange._load_markets', MagicMock(return_value={}))
mocker.patch('freqtrade.exchange.Exchange.validate_pairs', MagicMock())
with pytest.raises(OperationalException,
match=r"Invalid ticker interval '3m'. This exchange supports.*"):
match=r"Invalid timeframe '3m'. This exchange supports.*"):
Exchange(default_conf)
default_conf["ticker_interval"] = "15s"
@ -1121,31 +1196,22 @@ def test_fetch_ticker(default_conf, mocker, exchange_name):
assert ticker['bid'] == 0.5
assert ticker['ask'] == 1
assert 'ETH/BTC' in exchange._cached_ticker
assert exchange._cached_ticker['ETH/BTC']['bid'] == 0.5
assert exchange._cached_ticker['ETH/BTC']['ask'] == 1
# Test caching
api_mock.fetch_ticker = MagicMock()
exchange.fetch_ticker(pair='ETH/BTC', refresh=False)
assert api_mock.fetch_ticker.call_count == 0
ccxt_exceptionhandlers(mocker, default_conf, api_mock, exchange_name,
"fetch_ticker", "fetch_ticker",
pair='ETH/BTC', refresh=True)
pair='ETH/BTC')
api_mock.fetch_ticker = MagicMock(return_value={})
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
exchange.fetch_ticker(pair='ETH/BTC', refresh=True)
exchange.fetch_ticker(pair='ETH/BTC')
with pytest.raises(DependencyException, match=r'Pair XRP/ETH not available'):
exchange.fetch_ticker(pair='XRP/ETH', refresh=True)
exchange.fetch_ticker(pair='XRP/ETH')
@pytest.mark.parametrize("exchange_name", EXCHANGES)
def test_get_historic_ohlcv(default_conf, mocker, caplog, exchange_name):
exchange = get_patched_exchange(mocker, default_conf, id=exchange_name)
tick = [
ohlcv = [
[
arrow.utcnow().timestamp * 1000, # unix timestamp ms
1, # open
@ -1158,7 +1224,7 @@ def test_get_historic_ohlcv(default_conf, mocker, caplog, exchange_name):
pair = 'ETH/BTC'
async def mock_candle_hist(pair, timeframe, since_ms):
return pair, timeframe, tick
return pair, timeframe, ohlcv
exchange._async_get_candle_history = Mock(wraps=mock_candle_hist)
# one_call calculation * 1.8 should do 2 calls
@ -1166,12 +1232,12 @@ def test_get_historic_ohlcv(default_conf, mocker, caplog, exchange_name):
ret = exchange.get_historic_ohlcv(pair, "5m", int((arrow.utcnow().timestamp - since) * 1000))
assert exchange._async_get_candle_history.call_count == 2
# Returns twice the above tick
# Returns twice the above OHLCV data
assert len(ret) == 2
def test_refresh_latest_ohlcv(mocker, default_conf, caplog) -> None:
tick = [
ohlcv = [
[
(arrow.utcnow().timestamp - 1) * 1000, # unix timestamp ms
1, # open
@ -1192,14 +1258,14 @@ def test_refresh_latest_ohlcv(mocker, default_conf, caplog) -> None:
caplog.set_level(logging.DEBUG)
exchange = get_patched_exchange(mocker, default_conf)
exchange._api_async.fetch_ohlcv = get_mock_coro(tick)
exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
pairs = [('IOTA/ETH', '5m'), ('XRP/ETH', '5m')]
# empty dicts
assert not exchange._klines
exchange.refresh_latest_ohlcv(pairs)
assert log_has(f'Refreshing ohlcv data for {len(pairs)} pairs', caplog)
assert log_has(f'Refreshing candle (OHLCV) data for {len(pairs)} pairs', caplog)
assert exchange._klines
assert exchange._api_async.fetch_ohlcv.call_count == 2
for pair in pairs:
@ -1217,14 +1283,15 @@ def test_refresh_latest_ohlcv(mocker, default_conf, caplog) -> None:
exchange.refresh_latest_ohlcv([('IOTA/ETH', '5m'), ('XRP/ETH', '5m')])
assert exchange._api_async.fetch_ohlcv.call_count == 2
assert log_has(f"Using cached ohlcv data for pair {pairs[0][0]}, timeframe {pairs[0][1]} ...",
assert log_has(f"Using cached candle (OHLCV) data for pair {pairs[0][0]}, "
f"timeframe {pairs[0][1]} ...",
caplog)
@pytest.mark.asyncio
@pytest.mark.parametrize("exchange_name", EXCHANGES)
async def test__async_get_candle_history(default_conf, mocker, caplog, exchange_name):
tick = [
ohlcv = [
[
arrow.utcnow().timestamp * 1000, # unix timestamp ms
1, # open
@ -1238,7 +1305,7 @@ async def test__async_get_candle_history(default_conf, mocker, caplog, exchange_
caplog.set_level(logging.DEBUG)
exchange = get_patched_exchange(mocker, default_conf, id=exchange_name)
# Monkey-patch async function
exchange._api_async.fetch_ohlcv = get_mock_coro(tick)
exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
pair = 'ETH/BTC'
res = await exchange._async_get_candle_history(pair, "5m")
@ -1246,9 +1313,9 @@ async def test__async_get_candle_history(default_conf, mocker, caplog, exchange_
assert len(res) == 3
assert res[0] == pair
assert res[1] == "5m"
assert res[2] == tick
assert res[2] == ohlcv
assert exchange._api_async.fetch_ohlcv.call_count == 1
assert not log_has(f"Using cached ohlcv data for {pair} ...", caplog)
assert not log_has(f"Using cached candle (OHLCV) data for {pair} ...", caplog)
# exchange = Exchange(default_conf)
await async_ccxt_exception(mocker, default_conf, MagicMock(),
@ -1256,14 +1323,15 @@ async def test__async_get_candle_history(default_conf, mocker, caplog, exchange_
pair='ABCD/BTC', timeframe=default_conf['ticker_interval'])
api_mock = MagicMock()
with pytest.raises(OperationalException, match=r'Could not fetch ticker data*'):
with pytest.raises(OperationalException,
match=r'Could not fetch historical candle \(OHLCV\) data.*'):
api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.BaseError("Unknown error"))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
await exchange._async_get_candle_history(pair, "5m",
(arrow.utcnow().timestamp - 2000) * 1000)
with pytest.raises(OperationalException, match=r'Exchange.* does not support fetching '
r'historical candlestick data\..*'):
r'historical candle \(OHLCV\) data\..*'):
api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.NotSupported("Not supported"))
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
await exchange._async_get_candle_history(pair, "5m",
@ -1273,7 +1341,7 @@ async def test__async_get_candle_history(default_conf, mocker, caplog, exchange_
@pytest.mark.asyncio
async def test__async_get_candle_history_empty(default_conf, mocker, caplog):
""" Test empty exchange result """
tick = []
ohlcv = []
caplog.set_level(logging.DEBUG)
exchange = get_patched_exchange(mocker, default_conf)
@ -1287,7 +1355,7 @@ async def test__async_get_candle_history_empty(default_conf, mocker, caplog):
assert len(res) == 3
assert res[0] == pair
assert res[1] == "5m"
assert res[2] == tick
assert res[2] == ohlcv
assert exchange._api_async.fetch_ohlcv.call_count == 1
@ -1365,8 +1433,8 @@ async def test___async_get_candle_history_sort(default_conf, mocker, exchange_na
return sorted(data, key=key)
# GDAX use-case (real data from GDAX)
# This ticker history is ordered DESC (newest first, oldest last)
tick = [
# This OHLCV data is ordered DESC (newest first, oldest last)
ohlcv = [
[1527833100000, 0.07666, 0.07671, 0.07666, 0.07668, 16.65244264],
[1527832800000, 0.07662, 0.07666, 0.07662, 0.07666, 1.30051526],
[1527832500000, 0.07656, 0.07661, 0.07656, 0.07661, 12.034778840000001],
@ -1379,31 +1447,31 @@ async def test___async_get_candle_history_sort(default_conf, mocker, exchange_na
[1527830400000, 0.07649, 0.07651, 0.07649, 0.07651, 2.5734867]
]
exchange = get_patched_exchange(mocker, default_conf, id=exchange_name)
exchange._api_async.fetch_ohlcv = get_mock_coro(tick)
exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
sort_mock = mocker.patch('freqtrade.exchange.exchange.sorted', MagicMock(side_effect=sort_data))
# Test the ticker history sort
# Test the OHLCV data sort
res = await exchange._async_get_candle_history('ETH/BTC', default_conf['ticker_interval'])
assert res[0] == 'ETH/BTC'
ticks = res[2]
res_ohlcv = res[2]
assert sort_mock.call_count == 1
assert ticks[0][0] == 1527830400000
assert ticks[0][1] == 0.07649
assert ticks[0][2] == 0.07651
assert ticks[0][3] == 0.07649
assert ticks[0][4] == 0.07651
assert ticks[0][5] == 2.5734867
assert res_ohlcv[0][0] == 1527830400000
assert res_ohlcv[0][1] == 0.07649
assert res_ohlcv[0][2] == 0.07651
assert res_ohlcv[0][3] == 0.07649
assert res_ohlcv[0][4] == 0.07651
assert res_ohlcv[0][5] == 2.5734867
assert ticks[9][0] == 1527833100000
assert ticks[9][1] == 0.07666
assert ticks[9][2] == 0.07671
assert ticks[9][3] == 0.07666
assert ticks[9][4] == 0.07668
assert ticks[9][5] == 16.65244264
assert res_ohlcv[9][0] == 1527833100000
assert res_ohlcv[9][1] == 0.07666
assert res_ohlcv[9][2] == 0.07671
assert res_ohlcv[9][3] == 0.07666
assert res_ohlcv[9][4] == 0.07668
assert res_ohlcv[9][5] == 16.65244264
# Bittrex use-case (real data from Bittrex)
# This ticker history is ordered ASC (oldest first, newest last)
tick = [
# This OHLCV data is ordered ASC (oldest first, newest last)
ohlcv = [
[1527827700000, 0.07659999, 0.0766, 0.07627, 0.07657998, 1.85216924],
[1527828000000, 0.07657995, 0.07657995, 0.0763, 0.0763, 26.04051037],
[1527828300000, 0.0763, 0.07659998, 0.0763, 0.0764, 10.36434124],
@ -1415,29 +1483,29 @@ async def test___async_get_candle_history_sort(default_conf, mocker, exchange_na
[1527830100000, 0.076695, 0.07671, 0.07624171, 0.07671, 1.80689244],
[1527830400000, 0.07671, 0.07674399, 0.07629216, 0.07655213, 2.31452783]
]
exchange._api_async.fetch_ohlcv = get_mock_coro(tick)
exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
# Reset sort mock
sort_mock = mocker.patch('freqtrade.exchange.sorted', MagicMock(side_effect=sort_data))
# Test the ticker history sort
# Test the OHLCV data sort
res = await exchange._async_get_candle_history('ETH/BTC', default_conf['ticker_interval'])
assert res[0] == 'ETH/BTC'
assert res[1] == default_conf['ticker_interval']
ticks = res[2]
res_ohlcv = res[2]
# Sorted not called again - data is already in order
assert sort_mock.call_count == 0
assert ticks[0][0] == 1527827700000
assert ticks[0][1] == 0.07659999
assert ticks[0][2] == 0.0766
assert ticks[0][3] == 0.07627
assert ticks[0][4] == 0.07657998
assert ticks[0][5] == 1.85216924
assert res_ohlcv[0][0] == 1527827700000
assert res_ohlcv[0][1] == 0.07659999
assert res_ohlcv[0][2] == 0.0766
assert res_ohlcv[0][3] == 0.07627
assert res_ohlcv[0][4] == 0.07657998
assert res_ohlcv[0][5] == 1.85216924
assert ticks[9][0] == 1527830400000
assert ticks[9][1] == 0.07671
assert ticks[9][2] == 0.07674399
assert ticks[9][3] == 0.07629216
assert ticks[9][4] == 0.07655213
assert ticks[9][5] == 2.31452783
assert res_ohlcv[9][0] == 1527830400000
assert res_ohlcv[9][1] == 0.07671
assert res_ohlcv[9][2] == 0.07674399
assert res_ohlcv[9][3] == 0.07629216
assert res_ohlcv[9][4] == 0.07655213
assert res_ohlcv[9][5] == 2.31452783
@pytest.mark.asyncio
@ -1828,6 +1896,7 @@ def test_get_valid_pair_combination(default_conf, mocker, markets):
# 'ETH/BTC': 'active': True
# 'ETH/USDT': 'active': True
# 'LTC/BTC': 'active': False
# 'LTC/ETH': 'active': True
# 'LTC/USD': 'active': True
# 'LTC/USDT': 'active': True
# 'NEO/BTC': 'active': False
@ -1836,26 +1905,26 @@ def test_get_valid_pair_combination(default_conf, mocker, markets):
# 'XRP/BTC': 'active': False
# all markets
([], [], False, False,
['BLK/BTC', 'BTT/BTC', 'ETH/BTC', 'ETH/USDT', 'LTC/BTC', 'LTC/USD',
['BLK/BTC', 'BTT/BTC', 'ETH/BTC', 'ETH/USDT', 'LTC/BTC', 'LTC/ETH', 'LTC/USD',
'LTC/USDT', 'NEO/BTC', 'TKN/BTC', 'XLTCUSDT', 'XRP/BTC']),
# active markets
([], [], False, True,
['BLK/BTC', 'ETH/BTC', 'ETH/USDT', 'LTC/BTC', 'LTC/USD', 'NEO/BTC',
['BLK/BTC', 'ETH/BTC', 'ETH/USDT', 'LTC/BTC', 'LTC/ETH', 'LTC/USD', 'NEO/BTC',
'TKN/BTC', 'XLTCUSDT', 'XRP/BTC']),
# all pairs
([], [], True, False,
['BLK/BTC', 'BTT/BTC', 'ETH/BTC', 'ETH/USDT', 'LTC/BTC', 'LTC/USD',
['BLK/BTC', 'BTT/BTC', 'ETH/BTC', 'ETH/USDT', 'LTC/BTC', 'LTC/ETH', 'LTC/USD',
'LTC/USDT', 'NEO/BTC', 'TKN/BTC', 'XRP/BTC']),
# active pairs
([], [], True, True,
['BLK/BTC', 'ETH/BTC', 'ETH/USDT', 'LTC/BTC', 'LTC/USD', 'NEO/BTC',
['BLK/BTC', 'ETH/BTC', 'ETH/USDT', 'LTC/BTC', 'LTC/ETH', 'LTC/USD', 'NEO/BTC',
'TKN/BTC', 'XRP/BTC']),
# all markets, base=ETH, LTC
(['ETH', 'LTC'], [], False, False,
['ETH/BTC', 'ETH/USDT', 'LTC/BTC', 'LTC/USD', 'LTC/USDT', 'XLTCUSDT']),
['ETH/BTC', 'ETH/USDT', 'LTC/BTC', 'LTC/ETH', 'LTC/USD', 'LTC/USDT', 'XLTCUSDT']),
# all markets, base=LTC
(['LTC'], [], False, False,
['LTC/BTC', 'LTC/USD', 'LTC/USDT', 'XLTCUSDT']),
['LTC/BTC', 'LTC/ETH', 'LTC/USD', 'LTC/USDT', 'XLTCUSDT']),
# all markets, quote=USDT
([], ['USDT'], False, False,
['ETH/USDT', 'LTC/USDT', 'XLTCUSDT']),

10
tests/optimize/__init__.py
View File

@ -6,7 +6,7 @@ from pandas import DataFrame
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.strategy.interface import SellType
ticker_start_time = arrow.get(2018, 10, 3)
tests_start_time = arrow.get(2018, 10, 3)
tests_timeframe = '1h'
@ -36,14 +36,14 @@ class BTContainer(NamedTuple):
def _get_frame_time_from_offset(offset):
return ticker_start_time.shift(minutes=(offset * timeframe_to_minutes(tests_timeframe))
).datetime
minutes = offset * timeframe_to_minutes(tests_timeframe)
return tests_start_time.shift(minutes=minutes).datetime
def _build_backtest_dataframe(ticker_with_signals):
def _build_backtest_dataframe(data):
columns = ['date', 'open', 'high', 'low', 'close', 'volume', 'buy', 'sell']
frame = DataFrame.from_records(ticker_with_signals, columns=columns)
frame = DataFrame.from_records(data, columns=columns)
frame['date'] = frame['date'].apply(_get_frame_time_from_offset)
# Ensure floats are in place
for column in ['open', 'high', 'low', 'close', 'volume']:

45
tests/optimize/test_backtesting.py
View File

@ -84,7 +84,7 @@ def simple_backtest(config, contour, num_results, mocker, testdatadir) -> None:
backtesting = Backtesting(config)
data = load_data_test(contour, testdatadir)
processed = backtesting.strategy.tickerdata_to_dataframe(data)
processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
min_date, max_date = get_timerange(processed)
assert isinstance(processed, dict)
results = backtesting.backtest(
@ -105,7 +105,7 @@ def _make_backtest_conf(mocker, datadir, conf=None, pair='UNITTEST/BTC'):
data = trim_dictlist(data, -201)
patch_exchange(mocker)
backtesting = Backtesting(conf)
processed = backtesting.strategy.tickerdata_to_dataframe(data)
processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
min_date, max_date = get_timerange(processed)
return {
'processed': processed,
@ -224,6 +224,7 @@ def test_setup_bt_configuration_with_arguments(mocker, default_conf, caplog) ->
assert 'export' in config
assert log_has('Parameter --export detected: {} ...'.format(config['export']), caplog)
assert 'exportfilename' in config
assert isinstance(config['exportfilename'], Path)
assert log_has('Storing backtest results to {} ...'.format(config['exportfilename']), caplog)
assert 'fee' in config
@ -241,7 +242,7 @@ def test_setup_optimize_configuration_unlimited_stake_amount(mocker, default_con
'--strategy', 'DefaultStrategy',
]
with pytest.raises(DependencyException, match=r'.*stake amount.*'):
with pytest.raises(DependencyException, match=r'.`stake_amount`.*'):
setup_optimize_configuration(get_args(args), RunMode.BACKTEST)
@ -275,7 +276,7 @@ def test_backtesting_init(mocker, default_conf, order_types) -> None:
backtesting = Backtesting(default_conf)
assert backtesting.config == default_conf
assert backtesting.timeframe == '5m'
assert callable(backtesting.strategy.tickerdata_to_dataframe)
assert callable(backtesting.strategy.ohlcvdata_to_dataframe)
assert callable(backtesting.strategy.advise_buy)
assert callable(backtesting.strategy.advise_sell)
assert isinstance(backtesting.strategy.dp, DataProvider)
@ -297,7 +298,7 @@ def test_backtesting_init_no_ticker_interval(mocker, default_conf, caplog) -> No
"or as cli argument `--ticker-interval 5m`", caplog)
def test_tickerdata_with_fee(default_conf, mocker, testdatadir) -> None:
def test_data_with_fee(default_conf, mocker, testdatadir) -> None:
patch_exchange(mocker)
default_conf['fee'] = 0.1234
@ -307,21 +308,21 @@ def test_tickerdata_with_fee(default_conf, mocker, testdatadir) -> None:
assert fee_mock.call_count == 0
def test_tickerdata_to_dataframe_bt(default_conf, mocker, testdatadir) -> None:
def test_data_to_dataframe_bt(default_conf, mocker, testdatadir) -> None:
patch_exchange(mocker)
timerange = TimeRange.parse_timerange('1510694220-1510700340')
tickerlist = history.load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange,
data = history.load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange,
fill_up_missing=True)
backtesting = Backtesting(default_conf)
data = backtesting.strategy.tickerdata_to_dataframe(tickerlist)
assert len(data['UNITTEST/BTC']) == 102
processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
assert len(processed['UNITTEST/BTC']) == 102
# Load strategy to compare the result between Backtesting function and strategy are the same
default_conf.update({'strategy': 'DefaultStrategy'})
strategy = StrategyResolver.load_strategy(default_conf)
data2 = strategy.tickerdata_to_dataframe(tickerlist)
assert data['UNITTEST/BTC'].equals(data2['UNITTEST/BTC'])
processed2 = strategy.ohlcvdata_to_dataframe(data)
assert processed['UNITTEST/BTC'].equals(processed2['UNITTEST/BTC'])
def test_backtesting_start(default_conf, mocker, testdatadir, caplog) -> None:
@ -329,7 +330,6 @@ def test_backtesting_start(default_conf, mocker, testdatadir, caplog) -> None:
return Arrow(2017, 11, 14, 21, 17), Arrow(2017, 11, 14, 22, 59)
mocker.patch('freqtrade.data.history.get_timerange', get_timerange)
mocker.patch('freqtrade.exchange.Exchange.refresh_latest_ohlcv', MagicMock())
patch_exchange(mocker)
mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', MagicMock())
mocker.patch('freqtrade.optimize.backtesting.generate_text_table', MagicMock(return_value=1))
@ -360,7 +360,6 @@ def test_backtesting_start_no_data(default_conf, mocker, caplog, testdatadir) ->
mocker.patch('freqtrade.data.history.history_utils.load_pair_history',
MagicMock(return_value=pd.DataFrame()))
mocker.patch('freqtrade.data.history.get_timerange', get_timerange)
mocker.patch('freqtrade.exchange.Exchange.refresh_latest_ohlcv', MagicMock())
patch_exchange(mocker)
mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', MagicMock())
mocker.patch('freqtrade.optimize.backtesting.generate_text_table', MagicMock(return_value=1))
@ -385,10 +384,10 @@ def test_backtest(default_conf, fee, mocker, testdatadir) -> None:
timerange = TimeRange('date', None, 1517227800, 0)
data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=['UNITTEST/BTC'],
timerange=timerange)
data_processed = backtesting.strategy.tickerdata_to_dataframe(data)
min_date, max_date = get_timerange(data_processed)
processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
min_date, max_date = get_timerange(processed)
results = backtesting.backtest(
processed=data_processed,
processed=processed,
stake_amount=default_conf['stake_amount'],
start_date=min_date,
end_date=max_date,
@ -416,7 +415,7 @@ def test_backtest(default_conf, fee, mocker, testdatadir) -> None:
'sell_reason': [SellType.ROI, SellType.ROI]
})
pd.testing.assert_frame_equal(results, expected)
data_pair = data_processed[pair]
data_pair = processed[pair]
for _, t in results.iterrows():
ln = data_pair.loc[data_pair["date"] == t["open_time"]]
# Check open trade rate alignes to open rate
@ -439,7 +438,7 @@ def test_backtest_1min_ticker_interval(default_conf, fee, mocker, testdatadir) -
timerange = TimeRange.parse_timerange('1510688220-1510700340')
data = history.load_data(datadir=testdatadir, timeframe='1m', pairs=['UNITTEST/BTC'],
timerange=timerange)
processed = backtesting.strategy.tickerdata_to_dataframe(data)
processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
min_date, max_date = get_timerange(processed)
results = backtesting.backtest(
processed=processed,
@ -458,7 +457,7 @@ def test_processed(default_conf, mocker, testdatadir) -> None:
backtesting = Backtesting(default_conf)
dict_of_tickerrows = load_data_test('raise', testdatadir)
dataframes = backtesting.strategy.tickerdata_to_dataframe(dict_of_tickerrows)
dataframes = backtesting.strategy.ohlcvdata_to_dataframe(dict_of_tickerrows)
dataframe = dataframes['UNITTEST/BTC']
cols = dataframe.columns
# assert the dataframe got some of the indicator columns
@ -557,10 +556,10 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
backtesting.strategy.advise_buy = _trend_alternate_hold # Override
backtesting.strategy.advise_sell = _trend_alternate_hold # Override
data_processed = backtesting.strategy.tickerdata_to_dataframe(data)
min_date, max_date = get_timerange(data_processed)
processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
min_date, max_date = get_timerange(processed)
backtest_conf = {
'processed': data_processed,
'processed': processed,
'stake_amount': default_conf['stake_amount'],
'start_date': min_date,
'end_date': max_date,
@ -576,7 +575,7 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
assert len(evaluate_result_multi(results, '5m', 3)) == 0
backtest_conf = {
'processed': data_processed,
'processed': processed,
'stake_amount': default_conf['stake_amount'],
'start_date': min_date,
'end_date': max_date,

223
tests/optimize/test_hyperopt.py
View File

@ -10,10 +10,11 @@ import pytest
from arrow import Arrow
from filelock import Timeout
from freqtrade import constants
from freqtrade.commands.optimize_commands import (setup_optimize_configuration,
start_hyperopt)
from freqtrade.data.history import load_data
from freqtrade.exceptions import OperationalException
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
@ -158,6 +159,21 @@ 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:
default_conf['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
patched_configuration_load_config_file(mocker, default_conf)
args = [
'hyperopt',
'--config', 'config.json',
'--hyperopt', 'DefaultHyperOpt',
]
with pytest.raises(DependencyException, match=r'.`stake_amount`.*'):
setup_optimize_configuration(get_args(args), RunMode.HYPEROPT)
def test_hyperoptresolver(mocker, default_conf, caplog) -> None:
patched_configuration_load_config_file(mocker, default_conf)
@ -369,6 +385,42 @@ def test_sharpe_loss_daily_prefers_higher_profits(default_conf, hyperopt_results
assert under > correct
def test_sortino_loss_prefers_higher_profits(default_conf, hyperopt_results) -> None:
results_over = hyperopt_results.copy()
results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2
results_under = hyperopt_results.copy()
results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2
default_conf.update({'hyperopt_loss': 'SortinoHyperOptLoss'})
hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
over = hl.hyperopt_loss_function(results_over, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
under = hl.hyperopt_loss_function(results_under, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
assert over < correct
assert under > correct
def test_sortino_loss_daily_prefers_higher_profits(default_conf, hyperopt_results) -> None:
results_over = hyperopt_results.copy()
results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2
results_under = hyperopt_results.copy()
results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2
default_conf.update({'hyperopt_loss': 'SortinoHyperOptLossDaily'})
hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
over = hl.hyperopt_loss_function(results_over, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
under = hl.hyperopt_loss_function(results_under, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
assert over < correct
assert under > correct
def test_onlyprofit_loss_prefers_higher_profits(default_conf, hyperopt_results) -> None:
results_over = hyperopt_results.copy()
results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2
@ -390,17 +442,27 @@ def test_onlyprofit_loss_prefers_higher_profits(default_conf, hyperopt_results)
def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
hyperopt.current_best_loss = 2
hyperopt.total_epochs = 2
hyperopt.print_results(
{
'is_best': True,
'loss': 1,
'results_metrics':
{
'trade_count': 1,
'avg_profit': 0.1,
'total_profit': 0.001,
'profit': 1.0,
'duration': 20.0
},
'total_profit': 0,
'current_epoch': 2, # This starts from 1 (in a human-friendly manner)
'results_explanation': 'foo.',
'is_initial_point': False
'is_initial_point': False,
'is_best': True
}
)
out, err = capsys.readouterr()
assert ' 2/2: foo. Objective: 1.00000' in out
assert all(x in out
for x in ["Best", "2/2", " 1", "0.10%", "0.00100000 BTC (1.00%)", "20.0 m"])
def test_no_log_if_loss_does_not_improve(hyperopt, caplog) -> None:
@ -422,13 +484,11 @@ def test_save_trials_saves_trials(mocker, hyperopt, testdatadir, caplog) -> None
hyperopt.trials = trials
hyperopt.save_trials(final=True)
assert log_has("Saving 1 epoch.", caplog)
assert log_has(f"1 epoch saved to '{trials_file}'.", caplog)
mock_dump.assert_called_once()
hyperopt.trials = trials + trials
hyperopt.save_trials(final=True)
assert log_has("Saving 2 epochs.", caplog)
assert log_has(f"2 epochs saved to '{trials_file}'.", caplog)
@ -466,11 +526,21 @@ def test_start_calls_optimizer(mocker, default_conf, caplog, capsys) -> None:
parallel = mocker.patch(
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result',
'params': {'buy': {}, 'sell': {}, 'roi': {}, 'stoploss': 0.0}}])
MagicMock(return_value=[{
'loss': 1, 'results_explanation': 'foo result',
'params': {'buy': {}, 'sell': {}, 'roi': {}, 'stoploss': 0.0},
'results_metrics':
{
'trade_count': 1,
'avg_profit': 0.1,
'total_profit': 0.001,
'profit': 1.0,
'duration': 20.0
},
}])
)
patch_exchange(mocker)
# Co-test loading ticker-interval from strategy
# Co-test loading timeframe from strategy
del default_conf['ticker_interval']
default_conf.update({'config': 'config.json.example',
'hyperopt': 'DefaultHyperOpt',
@ -480,7 +550,7 @@ def test_start_calls_optimizer(mocker, default_conf, caplog, capsys) -> None:
'hyperopt_jobs': 1, })
hyperopt = Hyperopt(default_conf)
hyperopt.backtesting.strategy.tickerdata_to_dataframe = MagicMock()
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
hyperopt.start()
@ -490,7 +560,7 @@ def test_start_calls_optimizer(mocker, default_conf, caplog, capsys) -> None:
out, err = capsys.readouterr()
assert 'Best result:\n\n* 1/1: foo result Objective: 1.00000\n' in out
assert dumper.called
# Should be called twice, once for tickerdata, once to save evaluations
# Should be called twice, once for historical candle data, once to save evaluations
assert dumper.call_count == 2
assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
@ -576,8 +646,8 @@ def test_has_space(hyperopt, spaces, expected_results):
def test_populate_indicators(hyperopt, testdatadir) -> None:
tickerlist = load_data(testdatadir, '1m', ['UNITTEST/BTC'], fill_up_missing=True)
dataframes = hyperopt.backtesting.strategy.tickerdata_to_dataframe(tickerlist)
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'})
@ -588,8 +658,8 @@ def test_populate_indicators(hyperopt, testdatadir) -> None:
def test_buy_strategy_generator(hyperopt, testdatadir) -> None:
tickerlist = load_data(testdatadir, '1m', ['UNITTEST/BTC'], fill_up_missing=True)
dataframes = hyperopt.backtesting.strategy.tickerdata_to_dataframe(tickerlist)
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'})
@ -729,7 +799,7 @@ def test_clean_hyperopt(mocker, default_conf, caplog):
h = Hyperopt(default_conf)
assert unlinkmock.call_count == 2
assert log_has(f"Removing `{h.tickerdata_pickle}`.", caplog)
assert log_has(f"Removing `{h.data_pickle_file}`.", caplog)
def test_continue_hyperopt(mocker, default_conf, caplog):
@ -761,11 +831,23 @@ def test_print_json_spaces_all(mocker, default_conf, caplog, capsys) -> None:
parallel = mocker.patch(
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result', 'params': {},
'params_details': {'buy': {'mfi-value': None},
MagicMock(return_value=[{
'loss': 1, 'results_explanation': 'foo result', 'params': {},
'params_details': {
'buy': {'mfi-value': None},
'sell': {'sell-mfi-value': None},
'roi': {}, 'stoploss': {'stoploss': None},
'trailing': {'trailing_stop': None}}}])
'trailing': {'trailing_stop': None}
},
'results_metrics':
{
'trade_count': 1,
'avg_profit': 0.1,
'total_profit': 0.001,
'profit': 1.0,
'duration': 20.0
}
}])
)
patch_exchange(mocker)
@ -779,7 +861,7 @@ def test_print_json_spaces_all(mocker, default_conf, caplog, capsys) -> None:
})
hyperopt = Hyperopt(default_conf)
hyperopt.backtesting.strategy.tickerdata_to_dataframe = MagicMock()
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
hyperopt.start()
@ -787,9 +869,13 @@ def test_print_json_spaces_all(mocker, default_conf, caplog, capsys) -> None:
parallel.assert_called_once()
out, err = capsys.readouterr()
assert '{"params":{"mfi-value":null,"sell-mfi-value":null},"minimal_roi":{},"stoploss":null,"trailing_stop":null}' in out # noqa: E501
result_str = (
'{"params":{"mfi-value":null,"sell-mfi-value":null},"minimal_roi"'
':{},"stoploss":null,"trailing_stop":null}'
)
assert result_str in out # noqa: E501
assert dumper.called
# Should be called twice, once for tickerdata, once to save evaluations
# Should be called twice, once for historical candle data, once to save evaluations
assert dumper.call_count == 2
@ -804,10 +890,22 @@ def test_print_json_spaces_default(mocker, default_conf, caplog, capsys) -> None
parallel = mocker.patch(
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result', 'params': {},
'params_details': {'buy': {'mfi-value': None},
MagicMock(return_value=[{
'loss': 1, 'results_explanation': 'foo result', 'params': {},
'params_details': {
'buy': {'mfi-value': None},
'sell': {'sell-mfi-value': None},
'roi': {}, 'stoploss': {'stoploss': None}}}])
'roi': {}, 'stoploss': {'stoploss': None}
},
'results_metrics':
{
'trade_count': 1,
'avg_profit': 0.1,
'total_profit': 0.001,
'profit': 1.0,
'duration': 20.0
}
}])
)
patch_exchange(mocker)
@ -821,7 +919,7 @@ def test_print_json_spaces_default(mocker, default_conf, caplog, capsys) -> None
})
hyperopt = Hyperopt(default_conf)
hyperopt.backtesting.strategy.tickerdata_to_dataframe = MagicMock()
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
hyperopt.start()
@ -831,7 +929,7 @@ def test_print_json_spaces_default(mocker, default_conf, caplog, capsys) -> None
out, err = capsys.readouterr()
assert '{"params":{"mfi-value":null,"sell-mfi-value":null},"minimal_roi":{},"stoploss":null}' in out # noqa: E501
assert dumper.called
# Should be called twice, once for tickerdata, once to save evaluations
# Should be called twice, once for historical candle data, once to save evaluations
assert dumper.call_count == 2
@ -846,8 +944,18 @@ def test_print_json_spaces_roi_stoploss(mocker, default_conf, caplog, capsys) ->
parallel = mocker.patch(
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result', 'params': {},
'params_details': {'roi': {}, 'stoploss': {'stoploss': None}}}])
MagicMock(return_value=[{
'loss': 1, 'results_explanation': 'foo result', 'params': {},
'params_details': {'roi': {}, 'stoploss': {'stoploss': None}},
'results_metrics':
{
'trade_count': 1,
'avg_profit': 0.1,
'total_profit': 0.001,
'profit': 1.0,
'duration': 20.0
}
}])
)
patch_exchange(mocker)
@ -861,7 +969,7 @@ def test_print_json_spaces_roi_stoploss(mocker, default_conf, caplog, capsys) ->
})
hyperopt = Hyperopt(default_conf)
hyperopt.backtesting.strategy.tickerdata_to_dataframe = MagicMock()
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
hyperopt.start()
@ -871,7 +979,7 @@ def test_print_json_spaces_roi_stoploss(mocker, default_conf, caplog, capsys) ->
out, err = capsys.readouterr()
assert '{"minimal_roi":{},"stoploss":null}' in out
assert dumper.called
# Should be called twice, once for tickerdata, once to save evaluations
# Should be called twice, once for historical candle data, once to save evaluations
assert dumper.call_count == 2
@ -887,7 +995,16 @@ def test_simplified_interface_roi_stoploss(mocker, default_conf, caplog, capsys)
parallel = mocker.patch(
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
MagicMock(return_value=[{
'loss': 1, 'results_explanation': 'foo result', 'params': {'stoploss': 0.0}}])
'loss': 1, 'results_explanation': 'foo result', 'params': {'stoploss': 0.0},
'results_metrics':
{
'trade_count': 1,
'avg_profit': 0.1,
'total_profit': 0.001,
'profit': 1.0,
'duration': 20.0
}
}])
)
patch_exchange(mocker)
@ -899,7 +1016,7 @@ def test_simplified_interface_roi_stoploss(mocker, default_conf, caplog, capsys)
'hyperopt_jobs': 1, })
hyperopt = Hyperopt(default_conf)
hyperopt.backtesting.strategy.tickerdata_to_dataframe = MagicMock()
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
del hyperopt.custom_hyperopt.__class__.buy_strategy_generator
@ -914,7 +1031,7 @@ def test_simplified_interface_roi_stoploss(mocker, default_conf, caplog, capsys)
out, err = capsys.readouterr()
assert 'Best result:\n\n* 1/1: foo result Objective: 1.00000\n' in out
assert dumper.called
# Should be called twice, once for tickerdata, once to save evaluations
# Should be called twice, once for historical candle data, once to save evaluations
assert dumper.call_count == 2
assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
@ -942,7 +1059,7 @@ def test_simplified_interface_all_failed(mocker, default_conf, caplog, capsys) -
'hyperopt_jobs': 1, })
hyperopt = Hyperopt(default_conf)
hyperopt.backtesting.strategy.tickerdata_to_dataframe = MagicMock()
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
del hyperopt.custom_hyperopt.__class__.buy_strategy_generator
@ -965,7 +1082,17 @@ def test_simplified_interface_buy(mocker, default_conf, caplog, capsys) -> None:
parallel = mocker.patch(
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result', 'params': {}}])
MagicMock(return_value=[{
'loss': 1, 'results_explanation': 'foo result', 'params': {},
'results_metrics':
{
'trade_count': 1,
'avg_profit': 0.1,
'total_profit': 0.001,
'profit': 1.0,
'duration': 20.0
}
}])
)
patch_exchange(mocker)
@ -977,7 +1104,7 @@ def test_simplified_interface_buy(mocker, default_conf, caplog, capsys) -> None:
'hyperopt_jobs': 1, })
hyperopt = Hyperopt(default_conf)
hyperopt.backtesting.strategy.tickerdata_to_dataframe = MagicMock()
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
# TODO: sell_strategy_generator() is actually not called because
@ -992,7 +1119,7 @@ def test_simplified_interface_buy(mocker, default_conf, caplog, capsys) -> None:
out, err = capsys.readouterr()
assert 'Best result:\n\n* 1/1: foo result Objective: 1.00000\n' in out
assert dumper.called
# Should be called twice, once for tickerdata, once to save evaluations
# Should be called twice, once for historical candle data, once to save evaluations
assert dumper.call_count == 2
assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
@ -1012,7 +1139,17 @@ def test_simplified_interface_sell(mocker, default_conf, caplog, capsys) -> None
parallel = mocker.patch(
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
MagicMock(return_value=[{'loss': 1, 'results_explanation': 'foo result', 'params': {}}])
MagicMock(return_value=[{
'loss': 1, 'results_explanation': 'foo result', 'params': {},
'results_metrics':
{
'trade_count': 1,
'avg_profit': 0.1,
'total_profit': 0.001,
'profit': 1.0,
'duration': 20.0
}
}])
)
patch_exchange(mocker)
@ -1024,7 +1161,7 @@ def test_simplified_interface_sell(mocker, default_conf, caplog, capsys) -> None
'hyperopt_jobs': 1, })
hyperopt = Hyperopt(default_conf)
hyperopt.backtesting.strategy.tickerdata_to_dataframe = MagicMock()
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
# TODO: buy_strategy_generator() is actually not called because
@ -1039,7 +1176,7 @@ def test_simplified_interface_sell(mocker, default_conf, caplog, capsys) -> None
out, err = capsys.readouterr()
assert 'Best result:\n\n* 1/1: foo result Objective: 1.00000\n' in out
assert dumper.called
# Should be called twice, once for tickerdata, once to save evaluations
# Should be called twice, once for historical candle data, once to save evaluations
assert dumper.call_count == 2
assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
@ -1073,7 +1210,7 @@ def test_simplified_interface_failed(mocker, default_conf, caplog, capsys, metho
'hyperopt_jobs': 1, })
hyperopt = Hyperopt(default_conf)
hyperopt.backtesting.strategy.tickerdata_to_dataframe = MagicMock()
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
delattr(hyperopt.custom_hyperopt.__class__, method)

23
tests/optimize/test_optimize_reports.py
View File

@ -24,8 +24,8 @@ def test_generate_text_table(default_conf, mocker):
result_str = (
'| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC |'
' Tot Profit % | Avg Duration | Wins | Draws | Losses |\n'
'|:--------|-------:|---------------:|---------------:|-----------------:|'
'---------------:|:---------------|-------:|--------:|---------:|\n'
'|---------+--------+----------------+----------------+------------------+'
'----------------+----------------+--------+---------+----------|\n'
'| ETH/BTC | 2 | 15.00 | 30.00 | 0.60000000 |'
' 15.00 | 0:20:00 | 2 | 0 | 0 |\n'
'| TOTAL | 2 | 15.00 | 30.00 | 0.60000000 |'
@ -54,8 +54,8 @@ def test_generate_text_table_sell_reason(default_conf, mocker):
result_str = (
'| Sell Reason | Sells | Wins | Draws | Losses |'
' Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % |\n'
'|:--------------|--------:|-------:|--------:|---------:|'
'---------------:|---------------:|-----------------:|---------------:|\n'
'|---------------+---------+--------+---------+----------+'
'----------------+----------------+------------------+----------------|\n'
'| roi | 2 | 2 | 0 | 0 |'
' 15 | 30 | 0.6 | 15 |\n'
'| stop_loss | 1 | 0 | 0 | 1 |'
@ -97,12 +97,12 @@ def test_generate_text_table_strategy(default_conf, mocker):
result_str = (
'| Strategy | Buys | Avg Profit % | Cum Profit % | Tot'
' Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses |\n'
'|:--------------|-------:|---------------:|---------------:|------'
'-----------:|---------------:|:---------------|-------:|--------:|---------:|\n'
'| TestStrategy1 | 3 | 20.00 | 60.00 | '
' 1.10000000 | 30.00 | 0:17:00 | 3 | 0 | 0 |\n'
'| TestStrategy2 | 3 | 30.00 | 90.00 | '
' 1.30000000 | 45.00 | 0:20:00 | 3 | 0 | 0 |'
'|---------------+--------+----------------+----------------+------------------+'
'----------------+----------------+--------+---------+----------|\n'
'| TestStrategy1 | 3 | 20.00 | 60.00 | 1.10000000 |'
' 30.00 | 0:17:00 | 3 | 0 | 0 |\n'
'| TestStrategy2 | 3 | 30.00 | 90.00 | 1.30000000 |'
' 45.00 | 0:20:00 | 3 | 0 | 0 |'
)
assert generate_text_table_strategy('BTC', 2, all_results=results) == result_str
@ -111,8 +111,7 @@ def test_generate_edge_table(edge_conf, mocker):
results = {}
results['ETH/BTC'] = PairInfo(-0.01, 0.60, 2, 1, 3, 10, 60)
assert generate_edge_table(results).count(':|') == 7
assert generate_edge_table(results).count('+') == 7
assert generate_edge_table(results).count('| ETH/BTC |') == 1
assert generate_edge_table(results).count(
'| Risk Reward Ratio | Required Risk Reward | Expectancy |') == 1

2
tests/pairlist/test_pairlist.py
View File

@ -240,8 +240,6 @@ def test_pairlist_class(mocker, whitelist_conf, markets, pairlist):
(['ETH/BTC', 'TKN/BTC', 'ETH/USDT'], "is not compatible with your stake currency"),
# BCH/BTC not available
(['ETH/BTC', 'TKN/BTC', 'BCH/BTC'], "is not compatible with exchange"),
# BLK/BTC in blacklist
(['ETH/BTC', 'TKN/BTC', 'BLK/BTC'], "in your blacklist. Removing "),
# BTT/BTC is inactive
(['ETH/BTC', 'TKN/BTC', 'BTT/BTC'], "Market is not active")
])

52
tests/rpc/test_fiat_convert.py
View File

@ -8,7 +8,7 @@ import pytest
from requests.exceptions import RequestException
from freqtrade.rpc.fiat_convert import CryptoFiat, CryptoToFiatConverter
from tests.conftest import log_has
from tests.conftest import log_has, log_has_re
def test_pair_convertion_object():
@ -22,8 +22,8 @@ def test_pair_convertion_object():
assert pair_convertion.CACHE_DURATION == 6 * 60 * 60
# Check a regular usage
assert pair_convertion.crypto_symbol == 'BTC'
assert pair_convertion.fiat_symbol == 'USD'
assert pair_convertion.crypto_symbol == 'btc'
assert pair_convertion.fiat_symbol == 'usd'
assert pair_convertion.price == 12345.0
assert pair_convertion.is_expired() is False
@ -57,15 +57,15 @@ def test_fiat_convert_add_pair(mocker):
fiat_convert._add_pair(crypto_symbol='btc', fiat_symbol='usd', price=12345.0)
pair_len = len(fiat_convert._pairs)
assert pair_len == 1
assert fiat_convert._pairs[0].crypto_symbol == 'BTC'
assert fiat_convert._pairs[0].fiat_symbol == 'USD'
assert fiat_convert._pairs[0].crypto_symbol == 'btc'
assert fiat_convert._pairs[0].fiat_symbol == 'usd'
assert fiat_convert._pairs[0].price == 12345.0
fiat_convert._add_pair(crypto_symbol='btc', fiat_symbol='Eur', price=13000.2)
pair_len = len(fiat_convert._pairs)
assert pair_len == 2
assert fiat_convert._pairs[1].crypto_symbol == 'BTC'
assert fiat_convert._pairs[1].fiat_symbol == 'EUR'
assert fiat_convert._pairs[1].crypto_symbol == 'btc'
assert fiat_convert._pairs[1].fiat_symbol == 'eur'
assert fiat_convert._pairs[1].price == 13000.2
@ -100,15 +100,15 @@ def test_fiat_convert_get_price(mocker):
fiat_convert = CryptoToFiatConverter()
with pytest.raises(ValueError, match=r'The fiat US DOLLAR is not supported.'):
fiat_convert.get_price(crypto_symbol='BTC', fiat_symbol='US Dollar')
with pytest.raises(ValueError, match=r'The fiat us dollar is not supported.'):
fiat_convert.get_price(crypto_symbol='btc', fiat_symbol='US Dollar')
# Check the value return by the method
pair_len = len(fiat_convert._pairs)
assert pair_len == 0
assert fiat_convert.get_price(crypto_symbol='BTC', fiat_symbol='USD') == 28000.0
assert fiat_convert._pairs[0].crypto_symbol == 'BTC'
assert fiat_convert._pairs[0].fiat_symbol == 'USD'
assert fiat_convert.get_price(crypto_symbol='btc', fiat_symbol='usd') == 28000.0
assert fiat_convert._pairs[0].crypto_symbol == 'btc'
assert fiat_convert._pairs[0].fiat_symbol == 'usd'
assert fiat_convert._pairs[0].price == 28000.0
assert fiat_convert._pairs[0]._expiration != 0
assert len(fiat_convert._pairs) == 1
@ -116,13 +116,13 @@ def test_fiat_convert_get_price(mocker):
# Verify the cached is used
fiat_convert._pairs[0].price = 9867.543
expiration = fiat_convert._pairs[0]._expiration
assert fiat_convert.get_price(crypto_symbol='BTC', fiat_symbol='USD') == 9867.543
assert fiat_convert.get_price(crypto_symbol='btc', fiat_symbol='usd') == 9867.543
assert fiat_convert._pairs[0]._expiration == expiration
# Verify the cache expiration
expiration = time.time() - 2 * 60 * 60
fiat_convert._pairs[0]._expiration = expiration
assert fiat_convert.get_price(crypto_symbol='BTC', fiat_symbol='USD') == 28000.0
assert fiat_convert.get_price(crypto_symbol='btc', fiat_symbol='usd') == 28000.0
assert fiat_convert._pairs[0]._expiration is not expiration
@ -143,15 +143,15 @@ def test_loadcryptomap(mocker):
fiat_convert = CryptoToFiatConverter()
assert len(fiat_convert._cryptomap) == 2
assert fiat_convert._cryptomap["BTC"] == "1"
assert fiat_convert._cryptomap["btc"] == "bitcoin"
def test_fiat_init_network_exception(mocker):
# Because CryptoToFiatConverter is a Singleton we reset the listings
listmock = MagicMock(side_effect=RequestException)
mocker.patch.multiple(
'freqtrade.rpc.fiat_convert.Market',
listings=listmock,
'freqtrade.rpc.fiat_convert.CoinGeckoAPI',
get_coins_list=listmock,
)
# with pytest.raises(RequestEsxception):
fiat_convert = CryptoToFiatConverter()
@ -163,24 +163,24 @@ def test_fiat_init_network_exception(mocker):
def test_fiat_convert_without_network(mocker):
# Because CryptoToFiatConverter is a Singleton we reset the value of _coinmarketcap
# Because CryptoToFiatConverter is a Singleton we reset the value of _coingekko
fiat_convert = CryptoToFiatConverter()
cmc_temp = CryptoToFiatConverter._coinmarketcap
CryptoToFiatConverter._coinmarketcap = None
cmc_temp = CryptoToFiatConverter._coingekko
CryptoToFiatConverter._coingekko = None
assert fiat_convert._coinmarketcap is None
assert fiat_convert._find_price(crypto_symbol='BTC', fiat_symbol='USD') == 0.0
CryptoToFiatConverter._coinmarketcap = cmc_temp
assert fiat_convert._coingekko is None
assert fiat_convert._find_price(crypto_symbol='btc', fiat_symbol='usd') == 0.0
CryptoToFiatConverter._coingekko = cmc_temp
def test_fiat_invalid_response(mocker, caplog):
# Because CryptoToFiatConverter is a Singleton we reset the listings
listmock = MagicMock(return_value="{'novalidjson':DEADBEEFf}")
mocker.patch.multiple(
'freqtrade.rpc.fiat_convert.Market',
listings=listmock,
'freqtrade.rpc.fiat_convert.CoinGeckoAPI',
get_coins_list=listmock,
)
# with pytest.raises(RequestEsxception):
fiat_convert = CryptoToFiatConverter()
@ -189,7 +189,7 @@ def test_fiat_invalid_response(mocker, caplog):
length_cryptomap = len(fiat_convert._cryptomap)
assert length_cryptomap == 0
assert log_has('Could not load FIAT Cryptocurrency map for the following problem: TypeError',
assert log_has_re('Could not load FIAT Cryptocurrency map for the following problem: .*',
caplog)

60
tests/rpc/test_rpc.py
View File

@ -51,13 +51,13 @@ def test_rpc_trade_status(default_conf, ticker, fee, mocker) -> None:
'open_date_hum': ANY,
'close_date': None,
'close_date_hum': None,
'open_rate': 1.099e-05,
'open_rate': 1.098e-05,
'close_rate': None,
'current_rate': 1.098e-05,
'amount': 90.99181074,
'current_rate': 1.099e-05,
'amount': 91.07468124,
'stake_amount': 0.001,
'close_profit': None,
'current_profit': -0.59,
'current_profit': -0.41,
'stop_loss': 0.0,
'initial_stop_loss': 0.0,
'initial_stop_loss_pct': None,
@ -65,10 +65,8 @@ def test_rpc_trade_status(default_conf, ticker, fee, mocker) -> None:
'open_order': '(limit buy rem=0.00000000)'
} == results[0]
mocker.patch('freqtrade.exchange.Exchange.fetch_ticker',
mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_sell_rate',
MagicMock(side_effect=DependencyException(f"Pair 'ETH/BTC' not available")))
# invalidate ticker cache
rpc._freqtrade.exchange._cached_ticker = {}
results = rpc._rpc_trade_status()
assert isnan(results[0]['current_profit'])
assert isnan(results[0]['current_rate'])
@ -80,10 +78,10 @@ def test_rpc_trade_status(default_conf, ticker, fee, mocker) -> None:
'open_date_hum': ANY,
'close_date': None,
'close_date_hum': None,
'open_rate': 1.099e-05,
'open_rate': 1.098e-05,
'close_rate': None,
'current_rate': ANY,
'amount': 90.99181074,
'amount': 91.07468124,
'stake_amount': 0.001,
'close_profit': None,
'current_profit': ANY,
@ -97,8 +95,8 @@ def test_rpc_trade_status(default_conf, ticker, fee, mocker) -> None:
def test_rpc_status_table(default_conf, ticker, fee, mocker) -> None:
mocker.patch.multiple(
'freqtrade.rpc.fiat_convert.Market',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
'freqtrade.rpc.fiat_convert.CoinGeckoAPI',
get_price=MagicMock(return_value={'bitcoin': {'usd': 15000.0}}),
)
mocker.patch('freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', return_value=15000.0)
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
@ -123,7 +121,7 @@ def test_rpc_status_table(default_conf, ticker, fee, mocker) -> None:
assert "Pair" in headers
assert 'instantly' == result[0][2]
assert 'ETH/BTC' in result[0][1]
assert '-0.59%' == result[0][3]
assert '-0.41%' == result[0][3]
# Test with fiatconvert
rpc._fiat_converter = CryptoToFiatConverter()
@ -132,12 +130,10 @@ def test_rpc_status_table(default_conf, ticker, fee, mocker) -> None:
assert "Pair" in headers
assert 'instantly' == result[0][2]
assert 'ETH/BTC' in result[0][1]
assert '-0.59% (-0.09)' == result[0][3]
assert '-0.41% (-0.06)' == result[0][3]
mocker.patch('freqtrade.exchange.Exchange.fetch_ticker',
mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_sell_rate',
MagicMock(side_effect=DependencyException(f"Pair 'ETH/BTC' not available")))
# invalidate ticker cache
rpc._freqtrade.exchange._cached_ticker = {}
result, headers = rpc._rpc_status_table(default_conf['stake_currency'], 'USD')
assert 'instantly' == result[0][2]
assert 'ETH/BTC' in result[0][1]
@ -182,7 +178,7 @@ def test_rpc_daily_profit(default_conf, update, ticker, fee,
day[1] == '0.00006217 BTC')
assert (day[2] == '0.000 USD' or
day[2] == '0.933 USD')
day[2] == '0.767 USD')
# ensure first day is current date
assert str(days[0][0]) == str(datetime.utcnow().date())
@ -194,8 +190,8 @@ def test_rpc_daily_profit(default_conf, update, ticker, fee,
def test_rpc_trade_statistics(default_conf, ticker, ticker_sell_up, fee,
limit_buy_order, limit_sell_order, mocker) -> None:
mocker.patch.multiple(
'freqtrade.rpc.fiat_convert.Market',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
'freqtrade.rpc.fiat_convert.CoinGeckoAPI',
get_price=MagicMock(return_value={'bitcoin': {'usd': 15000.0}}),
)
mocker.patch('freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', return_value=15000.0)
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
@ -249,9 +245,9 @@ def test_rpc_trade_statistics(default_conf, ticker, ticker_sell_up, fee,
assert prec_satoshi(stats['profit_closed_coin'], 6.217e-05)
assert prec_satoshi(stats['profit_closed_percent'], 6.2)
assert prec_satoshi(stats['profit_closed_fiat'], 0.93255)
assert prec_satoshi(stats['profit_all_coin'], 5.632e-05)
assert prec_satoshi(stats['profit_all_percent'], 2.81)
assert prec_satoshi(stats['profit_all_fiat'], 0.8448)
assert prec_satoshi(stats['profit_all_coin'], 5.802e-05)
assert prec_satoshi(stats['profit_all_percent'], 2.89)
assert prec_satoshi(stats['profit_all_fiat'], 0.8703)
assert stats['trade_count'] == 2
assert stats['first_trade_date'] == 'just now'
assert stats['latest_trade_date'] == 'just now'
@ -260,10 +256,8 @@ def test_rpc_trade_statistics(default_conf, ticker, ticker_sell_up, fee,
assert prec_satoshi(stats['best_rate'], 6.2)
# Test non-available pair
mocker.patch('freqtrade.exchange.Exchange.fetch_ticker',
mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_sell_rate',
MagicMock(side_effect=DependencyException(f"Pair 'ETH/BTC' not available")))
# invalidate ticker cache
rpc._freqtrade.exchange._cached_ticker = {}
stats = rpc._rpc_trade_statistics(stake_currency, fiat_display_currency)
assert stats['trade_count'] == 2
assert stats['first_trade_date'] == 'just now'
@ -279,8 +273,8 @@ def test_rpc_trade_statistics(default_conf, ticker, ticker_sell_up, fee,
def test_rpc_trade_statistics_closed(mocker, default_conf, ticker, fee,
ticker_sell_up, limit_buy_order, limit_sell_order):
mocker.patch.multiple(
'freqtrade.rpc.fiat_convert.Market',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
'freqtrade.rpc.fiat_convert.CoinGeckoAPI',
get_price=MagicMock(return_value={'bitcoin': {'usd': 15000.0}}),
)
mocker.patch('freqtrade.rpc.fiat_convert.CryptoToFiatConverter._find_price',
return_value=15000.0)
@ -347,8 +341,8 @@ def test_rpc_balance_handle_error(default_conf, mocker):
# ETH will be skipped due to mocked Error below
mocker.patch.multiple(
'freqtrade.rpc.fiat_convert.Market',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
'freqtrade.rpc.fiat_convert.CoinGeckoAPI',
get_price=MagicMock(return_value={'bitcoin': {'usd': 15000.0}}),
)
mocker.patch('freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', return_value=15000.0)
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
@ -386,8 +380,8 @@ def test_rpc_balance_handle(default_conf, mocker, tickers):
}
mocker.patch.multiple(
'freqtrade.rpc.fiat_convert.Market',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
'freqtrade.rpc.fiat_convert.CoinGeckoAPI',
get_price=MagicMock(return_value={'bitcoin': {'usd': 15000.0}}),
)
mocker.patch('freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', return_value=15000.0)
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
@ -674,7 +668,7 @@ def test_rpcforcebuy(mocker, default_conf, ticker, fee, limit_buy_order) -> None
trade = rpc._rpc_forcebuy(pair, None)
assert isinstance(trade, Trade)
assert trade.pair == pair
assert trade.open_rate == ticker()['ask']
assert trade.open_rate == ticker()['bid']
# Test buy duplicate
with pytest.raises(RPCException, match=r'position for ETH/BTC already open - id: 1'):
@ -687,7 +681,7 @@ def test_rpcforcebuy(mocker, default_conf, ticker, fee, limit_buy_order) -> None
# Test buy pair not with stakes
with pytest.raises(RPCException, match=r'Wrong pair selected. Please pairs with stake.*'):
rpc._rpc_forcebuy('XRP/ETH', 0.0001)
rpc._rpc_forcebuy('LTC/ETH', 0.0001)
pair = 'XRP/BTC'
# Test not buying

8
tests/rpc/test_rpc_apiserver.py
View File

@ -426,20 +426,20 @@ 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': 90.99181074,
assert rc.json == [{'amount': 91.07468124,
'base_currency': 'BTC',
'close_date': None,
'close_date_hum': None,
'close_profit': None,
'close_rate': None,
'current_profit': -0.59,
'current_rate': 1.098e-05,
'current_profit': -0.41,
'current_rate': 1.099e-05,
'initial_stop_loss': 0.0,
'initial_stop_loss_pct': None,
'open_date': ANY,
'open_date_hum': 'just now',
'open_order': '(limit buy rem=0.00000000)',
'open_rate': 1.099e-05,
'open_rate': 1.098e-05,
'pair': 'ETH/BTC',
'stake_amount': 0.001,
'stop_loss': 0.0,

44
tests/rpc/test_rpc_telegram.py
View File

@ -720,13 +720,13 @@ def test_forcesell_handle(default_conf, update, ticker, fee,
'exchange': 'Bittrex',
'pair': 'ETH/BTC',
'gain': 'profit',
'limit': 1.172e-05,
'amount': 90.99181073703367,
'limit': 1.173e-05,
'amount': 91.07468123861567,
'order_type': 'limit',
'open_rate': 1.099e-05,
'current_rate': 1.172e-05,
'profit_amount': 6.126e-05,
'profit_percent': 0.0611052,
'open_rate': 1.098e-05,
'current_rate': 1.173e-05,
'profit_amount': 6.314e-05,
'profit_ratio': 0.0629778,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
'sell_reason': SellType.FORCE_SELL.value,
@ -779,13 +779,13 @@ def test_forcesell_down_handle(default_conf, update, ticker, fee,
'exchange': 'Bittrex',
'pair': 'ETH/BTC',
'gain': 'loss',
'limit': 1.044e-05,
'amount': 90.99181073703367,
'limit': 1.043e-05,
'amount': 91.07468123861567,
'order_type': 'limit',
'open_rate': 1.099e-05,
'current_rate': 1.044e-05,
'profit_amount': -5.492e-05,
'profit_percent': -0.05478342,
'open_rate': 1.098e-05,
'current_rate': 1.043e-05,
'profit_amount': -5.497e-05,
'profit_ratio': -0.05482878,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
'sell_reason': SellType.FORCE_SELL.value,
@ -827,13 +827,13 @@ def test_forcesell_all_handle(default_conf, update, ticker, fee, mocker) -> None
'exchange': 'Bittrex',
'pair': 'ETH/BTC',
'gain': 'loss',
'limit': 1.098e-05,
'amount': 90.99181073703367,
'limit': 1.099e-05,
'amount': 91.07468123861567,
'order_type': 'limit',
'open_rate': 1.099e-05,
'current_rate': 1.098e-05,
'profit_amount': -5.91e-06,
'profit_percent': -0.00589291,
'open_rate': 1.098e-05,
'current_rate': 1.099e-05,
'profit_amount': -4.09e-06,
'profit_ratio': -0.00408133,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
'sell_reason': SellType.FORCE_SELL.value,
@ -1210,7 +1210,7 @@ def test_send_msg_buy_notification(default_conf, mocker) -> None:
'*Amount:* `1333.33333333`\n' \
'*Open Rate:* `0.00001099`\n' \
'*Current Rate:* `0.00001099`\n' \
'*Total:* `(0.001000 BTC, 0.000 USD)`'
'*Total:* `(0.001000 BTC, 12.345 USD)`'
def test_send_msg_buy_cancel_notification(default_conf, mocker) -> None:
@ -1253,7 +1253,7 @@ def test_send_msg_sell_notification(default_conf, mocker) -> None:
'open_rate': 7.5e-05,
'current_rate': 3.201e-05,
'profit_amount': -0.05746268,
'profit_percent': -0.57405275,
'profit_ratio': -0.57405275,
'stake_currency': 'ETH',
'fiat_currency': 'USD',
'sell_reason': SellType.STOP_LOSS.value,
@ -1282,7 +1282,7 @@ def test_send_msg_sell_notification(default_conf, mocker) -> None:
'open_rate': 7.5e-05,
'current_rate': 3.201e-05,
'profit_amount': -0.05746268,
'profit_percent': -0.57405275,
'profit_ratio': -0.57405275,
'stake_currency': 'ETH',
'sell_reason': SellType.STOP_LOSS.value,
'open_date': arrow.utcnow().shift(days=-1, hours=-2, minutes=-30),
@ -1448,7 +1448,7 @@ def test_send_msg_sell_notification_no_fiat(default_conf, mocker) -> None:
'open_rate': 7.5e-05,
'current_rate': 3.201e-05,
'profit_amount': -0.05746268,
'profit_percent': -0.57405275,
'profit_ratio': -0.57405275,
'stake_currency': 'ETH',
'fiat_currency': 'USD',
'sell_reason': SellType.STOP_LOSS.value,

8
tests/rpc/test_rpc_webhook.py
View File

@ -28,12 +28,12 @@ def get_webhook_dict() -> dict:
"webhooksell": {
"value1": "Selling {pair}",
"value2": "limit {limit:8f}",
"value3": "profit: {profit_amount:8f} {stake_currency}"
"value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
},
"webhooksellcancel": {
"value1": "Cancelling Open Sell Order for {pair}",
"value2": "limit {limit:8f}",
"value3": "profit: {profit_amount:8f} {stake_currency}"
"value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
},
"webhookstatus": {
"value1": "Status: {status}",
@ -110,7 +110,7 @@ def test_send_msg(default_conf, mocker):
'open_rate': 0.004,
'current_rate': 0.005,
'profit_amount': 0.001,
'profit_percent': 0.20,
'profit_ratio': 0.20,
'stake_currency': 'BTC',
'sell_reason': SellType.STOP_LOSS.value
}
@ -136,7 +136,7 @@ def test_send_msg(default_conf, mocker):
'open_rate': 0.004,
'current_rate': 0.005,
'profit_amount': 0.001,
'profit_percent': 0.20,
'profit_ratio': 0.20,
'stake_currency': 'BTC',
'sell_reason': SellType.STOP_LOSS.value
}

2
tests/strategy/strats/default_strategy.py
View File

@ -68,7 +68,7 @@ class DefaultStrategy(IStrategy):
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""

48
tests/strategy/test_interface.py
View File

@ -21,69 +21,69 @@ from .strats.default_strategy import DefaultStrategy
_STRATEGY = DefaultStrategy(config={})
def test_returns_latest_buy_signal(mocker, default_conf, ticker_history):
def test_returns_latest_buy_signal(mocker, default_conf, ohlcv_history):
mocker.patch.object(
_STRATEGY, '_analyze_ticker_internal',
return_value=DataFrame([{'buy': 1, 'sell': 0, 'date': arrow.utcnow()}])
)
assert _STRATEGY.get_signal('ETH/BTC', '5m', ticker_history) == (True, False)
assert _STRATEGY.get_signal('ETH/BTC', '5m', ohlcv_history) == (True, False)
mocker.patch.object(
_STRATEGY, '_analyze_ticker_internal',
return_value=DataFrame([{'buy': 0, 'sell': 1, 'date': arrow.utcnow()}])
)
assert _STRATEGY.get_signal('ETH/BTC', '5m', ticker_history) == (False, True)
assert _STRATEGY.get_signal('ETH/BTC', '5m', ohlcv_history) == (False, True)
def test_returns_latest_sell_signal(mocker, default_conf, ticker_history):
def test_returns_latest_sell_signal(mocker, default_conf, ohlcv_history):
mocker.patch.object(
_STRATEGY, '_analyze_ticker_internal',
return_value=DataFrame([{'sell': 1, 'buy': 0, 'date': arrow.utcnow()}])
)
assert _STRATEGY.get_signal('ETH/BTC', '5m', ticker_history) == (False, True)
assert _STRATEGY.get_signal('ETH/BTC', '5m', ohlcv_history) == (False, True)
mocker.patch.object(
_STRATEGY, '_analyze_ticker_internal',
return_value=DataFrame([{'sell': 0, 'buy': 1, 'date': arrow.utcnow()}])
)
assert _STRATEGY.get_signal('ETH/BTC', '5m', ticker_history) == (True, False)
assert _STRATEGY.get_signal('ETH/BTC', '5m', ohlcv_history) == (True, False)
def test_get_signal_empty(default_conf, mocker, caplog):
assert (False, False) == _STRATEGY.get_signal('foo', default_conf['ticker_interval'],
DataFrame())
assert log_has('Empty ticker history for pair foo', caplog)
assert log_has('Empty candle (OHLCV) data for pair foo', caplog)
caplog.clear()
assert (False, False) == _STRATEGY.get_signal('bar', default_conf['ticker_interval'],
[])
assert log_has('Empty ticker history for pair bar', caplog)
assert log_has('Empty candle (OHLCV) data for pair bar', caplog)
def test_get_signal_exception_valueerror(default_conf, mocker, caplog, ticker_history):
def test_get_signal_exception_valueerror(default_conf, mocker, caplog, ohlcv_history):
caplog.set_level(logging.INFO)
mocker.patch.object(
_STRATEGY, '_analyze_ticker_internal',
side_effect=ValueError('xyz')
)
assert (False, False) == _STRATEGY.get_signal('foo', default_conf['ticker_interval'],
ticker_history)
ohlcv_history)
assert log_has_re(r'Strategy caused the following exception: xyz.*', caplog)
def test_get_signal_empty_dataframe(default_conf, mocker, caplog, ticker_history):
def test_get_signal_empty_dataframe(default_conf, mocker, caplog, ohlcv_history):
caplog.set_level(logging.INFO)
mocker.patch.object(
_STRATEGY, '_analyze_ticker_internal',
return_value=DataFrame([])
)
assert (False, False) == _STRATEGY.get_signal('xyz', default_conf['ticker_interval'],
ticker_history)
ohlcv_history)
assert log_has('Empty dataframe for pair xyz', caplog)
def test_get_signal_old_dataframe(default_conf, mocker, caplog, ticker_history):
def test_get_signal_old_dataframe(default_conf, mocker, caplog, ohlcv_history):
caplog.set_level(logging.INFO)
# default_conf defines a 5m interval. we check interval * 2 + 5m
# this is necessary as the last candle is removed (partial candles) by default
@ -94,7 +94,7 @@ def test_get_signal_old_dataframe(default_conf, mocker, caplog, ticker_history):
return_value=DataFrame(ticks)
)
assert (False, False) == _STRATEGY.get_signal('xyz', default_conf['ticker_interval'],
ticker_history)
ohlcv_history)
assert log_has('Outdated history for pair xyz. Last tick is 16 minutes old', caplog)
@ -107,15 +107,15 @@ def test_get_signal_handles_exceptions(mocker, default_conf):
assert _STRATEGY.get_signal(exchange, 'ETH/BTC', '5m') == (False, False)
def test_tickerdata_to_dataframe(default_conf, testdatadir) -> None:
def test_ohlcvdata_to_dataframe(default_conf, testdatadir) -> None:
default_conf.update({'strategy': 'DefaultStrategy'})
strategy = StrategyResolver.load_strategy(default_conf)
timerange = TimeRange.parse_timerange('1510694220-1510700340')
tickerlist = load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange,
data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange,
fill_up_missing=True)
data = strategy.tickerdata_to_dataframe(tickerlist)
assert len(data['UNITTEST/BTC']) == 102 # partial candle was removed
processed = strategy.ohlcvdata_to_dataframe(data)
assert len(processed['UNITTEST/BTC']) == 102 # partial candle was removed
def test_min_roi_reached(default_conf, fee) -> None:
@ -226,7 +226,7 @@ def test_min_roi_reached3(default_conf, fee) -> None:
assert strategy.min_roi_reached(trade, 0.31, arrow.utcnow().shift(minutes=-2).datetime)
def test_analyze_ticker_default(ticker_history, mocker, caplog) -> None:
def test_analyze_ticker_default(ohlcv_history, mocker, caplog) -> None:
caplog.set_level(logging.DEBUG)
ind_mock = MagicMock(side_effect=lambda x, meta: x)
buy_mock = MagicMock(side_effect=lambda x, meta: x)
@ -239,7 +239,7 @@ def test_analyze_ticker_default(ticker_history, mocker, caplog) -> None:
)
strategy = DefaultStrategy({})
strategy.analyze_ticker(ticker_history, {'pair': 'ETH/BTC'})
strategy.analyze_ticker(ohlcv_history, {'pair': 'ETH/BTC'})
assert ind_mock.call_count == 1
assert buy_mock.call_count == 1
assert buy_mock.call_count == 1
@ -248,7 +248,7 @@ def test_analyze_ticker_default(ticker_history, mocker, caplog) -> None:
assert not log_has('Skipping TA Analysis for already analyzed candle', caplog)
caplog.clear()
strategy.analyze_ticker(ticker_history, {'pair': 'ETH/BTC'})
strategy.analyze_ticker(ohlcv_history, {'pair': 'ETH/BTC'})
# No analysis happens as process_only_new_candles is true
assert ind_mock.call_count == 2
assert buy_mock.call_count == 2
@ -257,7 +257,7 @@ def test_analyze_ticker_default(ticker_history, mocker, caplog) -> None:
assert not log_has('Skipping TA Analysis for already analyzed candle', caplog)
def test__analyze_ticker_internal_skip_analyze(ticker_history, mocker, caplog) -> None:
def test__analyze_ticker_internal_skip_analyze(ohlcv_history, mocker, caplog) -> None:
caplog.set_level(logging.DEBUG)
ind_mock = MagicMock(side_effect=lambda x, meta: x)
buy_mock = MagicMock(side_effect=lambda x, meta: x)
@ -272,7 +272,7 @@ def test__analyze_ticker_internal_skip_analyze(ticker_history, mocker, caplog) -
strategy = DefaultStrategy({})
strategy.process_only_new_candles = True
ret = strategy._analyze_ticker_internal(ticker_history, {'pair': 'ETH/BTC'})
ret = strategy._analyze_ticker_internal(ohlcv_history, {'pair': 'ETH/BTC'})
assert 'high' in ret.columns
assert 'low' in ret.columns
assert 'close' in ret.columns
@ -284,7 +284,7 @@ def test__analyze_ticker_internal_skip_analyze(ticker_history, mocker, caplog) -
assert not log_has('Skipping TA Analysis for already analyzed candle', caplog)
caplog.clear()
ret = strategy._analyze_ticker_internal(ticker_history, {'pair': 'ETH/BTC'})
ret = strategy._analyze_ticker_internal(ohlcv_history, {'pair': 'ETH/BTC'})
# No analysis happens as process_only_new_candles is true
assert ind_mock.call_count == 1
assert buy_mock.call_count == 1

14
tests/test_configuration.py
View File

@ -34,13 +34,6 @@ def all_conf():
return conf
def test_load_config_invalid_pair(default_conf) -> None:
default_conf['exchange']['pair_whitelist'].append('ETH-BTC')
with pytest.raises(ValidationError, match=r'.*does not match.*'):
validate_config_schema(default_conf)
def test_load_config_missing_attributes(default_conf) -> None:
conf = deepcopy(default_conf)
conf.pop('exchange')
@ -326,6 +319,7 @@ def test_load_dry_run(default_conf, mocker, config_value, expected, arglist) ->
validated_conf = configuration.load_config()
assert validated_conf['dry_run'] is expected
assert validated_conf['runmode'] == (RunMode.DRY_RUN if expected else RunMode.LIVE)
def test_load_custom_strategy(default_conf, mocker) -> None:
@ -810,12 +804,6 @@ def test_validate_whitelist(default_conf):
validate_config_consistency(conf)
conf = deepcopy(default_conf)
conf['stake_currency'] = 'USDT'
with pytest.raises(OperationalException,
match=r"Stake-currency 'USDT' not compatible with pair-whitelist.*"):
validate_config_consistency(conf)
def test_load_config_test_comments() -> None:
"""

177
tests/test_freqtradebot.py
View File

@ -761,8 +761,8 @@ def test_process_trade_creation(default_conf, ticker, limit_buy_order,
assert trade.is_open
assert trade.open_date is not None
assert trade.exchange == 'bittrex'
assert trade.open_rate == 0.00001099
assert trade.amount == 90.99181073703367
assert trade.open_rate == 0.00001098
assert trade.amount == 91.07468123861567
assert log_has(
'Buy signal found: about create a new trade with stake_amount: 0.001 ...', caplog
@ -782,7 +782,7 @@ def test_process_exchange_failures(default_conf, ticker, mocker) -> None:
worker = Worker(args=None, config=default_conf)
patch_get_signal(worker.freqtrade)
worker._process()
worker._process_running()
assert sleep_mock.has_calls()
@ -799,7 +799,7 @@ def test_process_operational_exception(default_conf, ticker, mocker) -> None:
assert worker.freqtrade.state == State.RUNNING
worker._process()
worker._process_running()
assert worker.freqtrade.state == State.STOPPED
assert 'OperationalException' in msg_mock.call_args_list[-1][0][0]['status']
@ -906,22 +906,47 @@ def test_process_informative_pairs_added(default_conf, ticker, mocker) -> None:
assert ("ETH/BTC", default_conf["ticker_interval"]) in refresh_mock.call_args[0][0]
@pytest.mark.parametrize("ask,last,last_ab,expected", [
(20, 10, 0.0, 20), # Full ask side
(20, 10, 1.0, 10), # Full last side
(20, 10, 0.5, 15), # Between ask and last
(20, 10, 0.7, 13), # Between ask and last
(20, 10, 0.3, 17), # Between ask and last
(5, 10, 1.0, 5), # last bigger than ask
(5, 10, 0.5, 5), # last bigger than ask
@pytest.mark.parametrize("side,ask,bid,last,last_ab,expected", [
('ask', 20, 19, 10, 0.0, 20), # Full ask side
('ask', 20, 19, 10, 1.0, 10), # Full last side
('ask', 20, 19, 10, 0.5, 15), # Between ask and last
('ask', 20, 19, 10, 0.7, 13), # Between ask and last
('ask', 20, 19, 10, 0.3, 17), # Between ask and last
('ask', 5, 6, 10, 1.0, 5), # last bigger than ask
('ask', 5, 6, 10, 0.5, 5), # last bigger than ask
('ask', 10, 20, None, 0.5, 10), # last not available - uses ask
('ask', 4, 5, None, 0.5, 4), # last not available - uses ask
('ask', 4, 5, None, 1, 4), # last not available - uses ask
('ask', 4, 5, None, 0, 4), # last not available - uses ask
('bid', 10, 20, 10, 0.0, 20), # Full bid side
('bid', 10, 20, 10, 1.0, 10), # Full last side
('bid', 10, 20, 10, 0.5, 15), # Between bid and last
('bid', 10, 20, 10, 0.7, 13), # Between bid and last
('bid', 10, 20, 10, 0.3, 17), # Between bid and last
('bid', 4, 5, 10, 1.0, 5), # last bigger than bid
('bid', 4, 5, 10, 0.5, 5), # last bigger than bid
('bid', 10, 20, None, 0.5, 20), # last not available - uses bid
('bid', 4, 5, None, 0.5, 5), # last not available - uses bid
('bid', 4, 5, None, 1, 5), # last not available - uses bid
('bid', 4, 5, None, 0, 5), # last not available - uses bid
])
def test_get_buy_rate(mocker, default_conf, ask, last, last_ab, expected) -> None:
def test_get_buy_rate(mocker, default_conf, caplog, side, ask, bid,
last, last_ab, expected) -> None:
default_conf['bid_strategy']['ask_last_balance'] = last_ab
default_conf['bid_strategy']['price_side'] = side
freqtrade = get_patched_freqtradebot(mocker, default_conf)
mocker.patch('freqtrade.exchange.Exchange.fetch_ticker',
MagicMock(return_value={'ask': ask, 'last': last}))
MagicMock(return_value={'ask': ask, 'last': last, 'bid': bid}))
assert freqtrade.get_buy_rate('ETH/BTC', True) == expected
assert not log_has("Using cached buy rate for ETH/BTC.", caplog)
assert freqtrade.get_buy_rate('ETH/BTC', False) == expected
assert log_has("Using cached buy rate for ETH/BTC.", caplog)
# Running a 2nd time with Refresh on!
caplog.clear()
assert freqtrade.get_buy_rate('ETH/BTC', True) == expected
assert not log_has("Using cached buy rate for ETH/BTC.", caplog)
def test_execute_buy(mocker, default_conf, fee, limit_buy_order) -> None:
@ -1309,7 +1334,7 @@ def test_handle_stoploss_on_exchange_trailing(mocker, default_conf, fee, caplog,
stoploss_order_mock.assert_not_called()
assert freqtrade.handle_trade(trade) is False
assert trade.stop_loss == 0.00002344 * 0.95
assert trade.stop_loss == 0.00002346 * 0.95
# setting stoploss_on_exchange_interval to 0 seconds
freqtrade.strategy.order_types['stoploss_on_exchange_interval'] = 0
@ -1317,10 +1342,10 @@ def test_handle_stoploss_on_exchange_trailing(mocker, default_conf, fee, caplog,
assert freqtrade.handle_stoploss_on_exchange(trade) is False
cancel_order_mock.assert_called_once_with(100, 'ETH/BTC')
stoploss_order_mock.assert_called_once_with(amount=85.25149190110828,
stoploss_order_mock.assert_called_once_with(amount=85.32423208191126,
pair='ETH/BTC',
order_types=freqtrade.strategy.order_types,
stop_price=0.00002344 * 0.95)
stop_price=0.00002346 * 0.95)
# price fell below stoploss, so dry-run sells trade.
mocker.patch('freqtrade.exchange.Exchange.fetch_ticker', MagicMock(return_value={
@ -1502,12 +1527,12 @@ def test_tsl_on_exchange_compatible_with_edge(mocker, edge_conf, fee, caplog,
assert freqtrade.handle_stoploss_on_exchange(trade) is False
# stoploss should be set to 1% as trailing is on
assert trade.stop_loss == 0.00002344 * 0.99
assert trade.stop_loss == 0.00002346 * 0.99
cancel_order_mock.assert_called_once_with(100, 'NEO/BTC')
stoploss_order_mock.assert_called_once_with(amount=2131074.168797954,
stoploss_order_mock.assert_called_once_with(amount=2132892.491467577,
pair='NEO/BTC',
order_types=freqtrade.strategy.order_types,
stop_price=0.00002344 * 0.99)
stop_price=0.00002346 * 0.99)
def test_enter_positions(mocker, default_conf, caplog) -> None:
@ -2290,6 +2315,7 @@ def test_handle_timedout_limit_buy(mocker, default_conf, limit_buy_order) -> Non
Trade.session = MagicMock()
trade = MagicMock()
trade.pair = 'LTC/ETH'
limit_buy_order['remaining'] = limit_buy_order['amount']
assert freqtrade.handle_timedout_limit_buy(trade, limit_buy_order)
assert cancel_order_mock.call_count == 1
@ -2313,6 +2339,7 @@ def test_handle_timedout_limit_buy_corder_empty(mocker, default_conf, limit_buy_
Trade.session = MagicMock()
trade = MagicMock()
trade.pair = 'LTC/ETH'
limit_buy_order['remaining'] = limit_buy_order['amount']
assert freqtrade.handle_timedout_limit_buy(trade, limit_buy_order)
assert cancel_order_mock.call_count == 1
@ -2380,12 +2407,12 @@ def test_execute_sell_up(default_conf, ticker, fee, ticker_sell_up, mocker) -> N
'pair': 'ETH/BTC',
'gain': 'profit',
'limit': 1.172e-05,
'amount': 90.99181073703367,
'amount': 91.07468123861567,
'order_type': 'limit',
'open_rate': 1.099e-05,
'current_rate': 1.172e-05,
'profit_amount': 6.126e-05,
'profit_percent': 0.0611052,
'open_rate': 1.098e-05,
'current_rate': 1.173e-05,
'profit_amount': 6.223e-05,
'profit_ratio': 0.0620716,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
'sell_reason': SellType.ROI.value,
@ -2429,12 +2456,12 @@ def test_execute_sell_down(default_conf, ticker, fee, ticker_sell_down, mocker)
'pair': 'ETH/BTC',
'gain': 'loss',
'limit': 1.044e-05,
'amount': 90.99181073703367,
'amount': 91.07468123861567,
'order_type': 'limit',
'open_rate': 1.099e-05,
'current_rate': 1.044e-05,
'profit_amount': -5.492e-05,
'profit_percent': -0.05478342,
'open_rate': 1.098e-05,
'current_rate': 1.043e-05,
'profit_amount': -5.406e-05,
'profit_ratio': -0.05392257,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
'sell_reason': SellType.STOP_LOSS.value,
@ -2485,12 +2512,12 @@ def test_execute_sell_down_stoploss_on_exchange_dry_run(default_conf, ticker, fe
'pair': 'ETH/BTC',
'gain': 'loss',
'limit': 1.08801e-05,
'amount': 90.99181073703367,
'amount': 91.07468123861567,
'order_type': 'limit',
'open_rate': 1.099e-05,
'current_rate': 1.044e-05,
'profit_amount': -1.498e-05,
'profit_percent': -0.01493766,
'open_rate': 1.098e-05,
'current_rate': 1.043e-05,
'profit_amount': -1.408e-05,
'profit_ratio': -0.01404051,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
'sell_reason': SellType.STOP_LOSS.value,
@ -2675,7 +2702,7 @@ def test_execute_sell_market_order(default_conf, ticker, fee,
freqtrade.execute_sell(trade=trade, limit=ticker_sell_up()['bid'], sell_reason=SellType.ROI)
assert not trade.is_open
assert trade.close_profit == 0.0611052
assert trade.close_profit == 0.0620716
assert rpc_mock.call_count == 2
last_msg = rpc_mock.call_args_list[-1][0][0]
@ -2685,12 +2712,12 @@ def test_execute_sell_market_order(default_conf, ticker, fee,
'pair': 'ETH/BTC',
'gain': 'profit',
'limit': 1.172e-05,
'amount': 90.99181073703367,
'amount': 91.07468123861567,
'order_type': 'market',
'open_rate': 1.099e-05,
'current_rate': 1.172e-05,
'profit_amount': 6.126e-05,
'profit_percent': 0.0611052,
'open_rate': 1.098e-05,
'current_rate': 1.173e-05,
'profit_amount': 6.223e-05,
'profit_ratio': 0.0620716,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
'sell_reason': SellType.ROI.value,
@ -3712,29 +3739,55 @@ def test_order_book_ask_strategy(default_conf, limit_buy_order, limit_sell_order
assert freqtrade.handle_trade(trade) is True
def test_get_sell_rate(default_conf, mocker, ticker, order_book_l2) -> None:
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_order_book=order_book_l2,
fetch_ticker=ticker,
)
@pytest.mark.parametrize('side,ask,bid,expected', [
('bid', 10.0, 11.0, 11.0),
('bid', 10.0, 11.2, 11.2),
('bid', 10.0, 11.0, 11.0),
('bid', 9.8, 11.0, 11.0),
('bid', 0.0001, 0.002, 0.002),
('ask', 10.0, 11.0, 10.0),
('ask', 10.11, 11.2, 10.11),
('ask', 0.001, 0.002, 0.001),
('ask', 0.006, 1.0, 0.006),
])
def test_get_sell_rate(default_conf, mocker, caplog, side, bid, ask, expected) -> None:
default_conf['ask_strategy']['price_side'] = side
mocker.patch('freqtrade.exchange.Exchange.fetch_ticker', return_value={'ask': ask, 'bid': bid})
pair = "ETH/BTC"
# Test regular mode
ft = get_patched_freqtradebot(mocker, default_conf)
rate = ft.get_sell_rate(pair, True)
assert not log_has("Using cached sell rate for ETH/BTC.", caplog)
assert isinstance(rate, float)
assert rate == 0.00001098
assert rate == expected
# Use caching
rate = ft.get_sell_rate(pair, False)
assert rate == expected
assert log_has("Using cached sell rate for ETH/BTC.", caplog)
@pytest.mark.parametrize('side,expected', [
('bid', 0.043936), # Value from order_book_l2 fiture - bids side
('ask', 0.043949), # Value from order_book_l2 fiture - asks side
])
def test_get_sell_rate_orderbook(default_conf, mocker, caplog, side, expected, order_book_l2):
# Test orderbook mode
default_conf['ask_strategy']['price_side'] = side
default_conf['ask_strategy']['use_order_book'] = True
default_conf['ask_strategy']['order_book_min'] = 1
default_conf['ask_strategy']['order_book_max'] = 2
# TODO: min/max is irrelevant for this test until refactoring
pair = "ETH/BTC"
mocker.patch('freqtrade.exchange.Exchange.get_order_book', order_book_l2)
ft = get_patched_freqtradebot(mocker, default_conf)
rate = ft.get_sell_rate(pair, True)
assert not log_has("Using cached sell rate for ETH/BTC.", caplog)
assert isinstance(rate, float)
assert rate == 0.043936
assert rate == expected
rate = ft.get_sell_rate(pair, False)
assert rate == expected
assert log_has("Using cached sell rate for ETH/BTC.", caplog)
def test_startup_state(default_conf, mocker):
@ -3763,30 +3816,6 @@ def test_startup_trade_reinit(default_conf, edge_conf, mocker):
assert reinit_mock.call_count == 0
def test_process_i_am_alive(default_conf, mocker, caplog):
patch_RPCManager(mocker)
patch_exchange(mocker)
mocker.patch('freqtrade.exchange.Exchange.exchange_has', MagicMock(return_value=True))
ftbot = get_patched_freqtradebot(mocker, default_conf)
message = r"Bot heartbeat\. PID=.*"
ftbot.process()
assert log_has_re(message, caplog)
assert ftbot._heartbeat_msg != 0
caplog.clear()
# Message is not shown before interval is up
ftbot.process()
assert not log_has_re(message, caplog)
caplog.clear()
# Set clock - 70 seconds
ftbot._heartbeat_msg -= 70
ftbot.process()
assert log_has_re(message, caplog)
@pytest.mark.usefixtures("init_persistence")
def test_sync_wallet_dry_run(mocker, default_conf, ticker, fee, limit_buy_order, caplog):
default_conf['dry_run'] = True

6
tests/test_misc.py
View File

@ -6,7 +6,7 @@ from unittest.mock import MagicMock
import pytest
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.data.converter import ohlcv_to_dataframe
from freqtrade.misc import (datesarray_to_datetimearray, file_dump_json,
file_load_json, format_ms_time, pair_to_filename,
plural, render_template,
@ -19,8 +19,8 @@ def test_shorten_date() -> None:
assert shorten_date(str_data) == str_shorten_data
def test_datesarray_to_datetimearray(ticker_history_list):
dataframes = parse_ticker_dataframe(ticker_history_list, "5m", pair="UNITTEST/BTC",
def test_datesarray_to_datetimearray(ohlcv_history_list):
dataframes = ohlcv_to_dataframe(ohlcv_history_list, "5m", pair="UNITTEST/BTC",
fill_missing=True)
dates = datesarray_to_datetimearray(dataframes['date'])

30
tests/test_plotting.py
View File

@ -3,15 +3,16 @@ from copy import deepcopy
from pathlib import Path
from unittest.mock import MagicMock
import pandas as pd
import plotly.graph_objects as go
import pytest
from plotly.subplots import make_subplots
from freqtrade.commands import start_plot_dataframe, start_plot_profit
from freqtrade.configuration import TimeRange
from freqtrade.data import history
from freqtrade.data.btanalysis import create_cum_profit, load_backtest_data
from freqtrade.exceptions import OperationalException
from freqtrade.commands import start_plot_dataframe, start_plot_profit
from freqtrade.plot.plotting import (add_indicators, add_profit,
create_plotconfig,
generate_candlestick_graph,
@ -48,17 +49,17 @@ def test_init_plotscript(default_conf, mocker, testdatadir):
default_conf['trade_source'] = "file"
default_conf['ticker_interval'] = "5m"
default_conf["datadir"] = testdatadir
default_conf['exportfilename'] = str(testdatadir / "backtest-result_test.json")
default_conf['exportfilename'] = testdatadir / "backtest-result_test.json"
ret = init_plotscript(default_conf)
assert "tickers" in ret
assert "ohlcv" in ret
assert "trades" in ret
assert "pairs" in ret
default_conf['pairs'] = ["TRX/BTC", "ADA/BTC"]
ret = init_plotscript(default_conf)
assert "tickers" in ret
assert "TRX/BTC" in ret["tickers"]
assert "ADA/BTC" in ret["tickers"]
assert "ohlcv" in ret
assert "TRX/BTC" in ret["ohlcv"]
assert "ADA/BTC" in ret["ohlcv"]
def test_add_indicators(default_conf, testdatadir, caplog):
@ -266,15 +267,16 @@ def test_generate_profit_graph(testdatadir):
trades = load_backtest_data(filename)
timerange = TimeRange.parse_timerange("20180110-20180112")
pairs = ["TRX/BTC", "ADA/BTC"]
trades = trades[trades['close_time'] < pd.Timestamp('2018-01-12', tz='UTC')]
tickers = history.load_data(datadir=testdatadir,
data = history.load_data(datadir=testdatadir,
pairs=pairs,
timeframe='5m',
timerange=timerange
)
timerange=timerange)
trades = trades[trades['pair'].isin(pairs)]
fig = generate_profit_graph(pairs, tickers, trades, timeframe="5m")
fig = generate_profit_graph(pairs, data, trades, timeframe="5m")
assert isinstance(fig, go.Figure)
assert fig.layout.title.text == "Freqtrade Profit plot"
@ -283,13 +285,15 @@ def test_generate_profit_graph(testdatadir):
assert fig.layout.yaxis3.title.text == "Profit"
figure = fig.layout.figure
assert len(figure.data) == 4
assert len(figure.data) == 5
avgclose = find_trace_in_fig_data(figure.data, "Avg close price")
assert isinstance(avgclose, go.Scatter)
profit = find_trace_in_fig_data(figure.data, "Profit")
assert isinstance(profit, go.Scatter)
profit = find_trace_in_fig_data(figure.data, "Max drawdown 0.00%")
assert isinstance(profit, go.Scatter)
for pair in pairs:
profit_pair = find_trace_in_fig_data(figure.data, f"Profit {pair}")
@ -314,7 +318,7 @@ def test_start_plot_dataframe(mocker):
def test_load_and_plot_trades(default_conf, mocker, caplog, testdatadir):
default_conf['trade_source'] = 'file'
default_conf["datadir"] = testdatadir
default_conf['exportfilename'] = str(testdatadir / "backtest-result_test.json")
default_conf['exportfilename'] = testdatadir / "backtest-result_test.json"
default_conf['indicators1'] = ["sma5", "ema10"]
default_conf['indicators2'] = ["macd"]
default_conf['pairs'] = ["ETH/BTC", "LTC/BTC"]
@ -370,7 +374,7 @@ def test_start_plot_profit_error(mocker):
def test_plot_profit(default_conf, mocker, testdatadir, caplog):
default_conf['trade_source'] = 'file'
default_conf["datadir"] = testdatadir
default_conf['exportfilename'] = str(testdatadir / "backtest-result_test.json")
default_conf['exportfilename'] = testdatadir / "backtest-result_test.json"
default_conf['pairs'] = ["ETH/BTC", "LTC/BTC"]
profit_mock = MagicMock()

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