adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Merge pull request #2827 from freqtrade/release/2020-01

Browse Source 
new Release 2020.01
This commit is contained in:
Matthias 2020-02-01 12:54:32 +01:00 committed by GitHub
commit 8371003c05
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
126 changed files with 4878 additions and 3177 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

18
.github/workflows/ci.yml vendored
View File

@ -64,19 +64,17 @@ jobs:
pip install -e .
- name: Tests
env:
COVERALLS_REPO_TOKEN: ${{ secrets.COVERALLS_REPO_TOKEN }}
COVERALLS_SERVICE_NAME: travis-ci
TRAVIS: "true"
run: |
pytest --random-order --cov=freqtrade --cov-config=.coveragerc
- name: Coveralls
if: startsWith(matrix.os, 'ubuntu')
env:
# Coveralls token. Not used as secret due to github not providing secrets to forked repositories
COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
run: |
# Allow failure for coveralls
# Fake travis environment to get coveralls working correctly
export TRAVIS_PULL_REQUEST="https://github.com/${GITHUB_REPOSITORY}/pull/$(cat $GITHUB_EVENT_PATH | jq -r .number)"
export TRAVIS_BRANCH=${GITHUB_REF#"ref/heads"}
export CI_BRANCH=${GITHUB_REF#"ref/heads"}
echo "${TRAVIS_BRANCH}"
coveralls || true
coveralls -v || true
- name: Backtesting
run: |

2
Dockerfile
View File

@ -1,4 +1,4 @@
FROM python:3.7.5-slim-stretch
FROM python:3.7.6-slim-stretch
RUN apt-get update \
&& apt-get -y install curl build-essential libssl-dev \

1
build_helpers/install_windows.ps1
View File

@ -2,6 +2,7 @@
# Downloaded from https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
# 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
pip install -r requirements-dev.txt

2
config.json.example
View File

@ -2,6 +2,7 @@
"max_open_trades": 3,
"stake_currency": "BTC",
"stake_amount": 0.05,
"tradable_balance_ratio": 0.99,
"fiat_display_currency": "USD",
"ticker_interval" : "5m",
"dry_run": false,
@ -59,7 +60,6 @@
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"capital_available_percentage": 0.5,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,

2
config_binance.json.example
View File

@ -2,6 +2,7 @@
"max_open_trades": 3,
"stake_currency": "BTC",
"stake_amount": 0.05,
"tradable_balance_ratio": 0.99,
"fiat_display_currency": "USD",
"ticker_interval" : "5m",
"dry_run": true,
@ -64,7 +65,6 @@
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"capital_available_percentage": 0.5,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,

4
config_full.json.example
View File

@ -2,8 +2,11 @@
"max_open_trades": 3,
"stake_currency": "BTC",
"stake_amount": 0.05,
"tradable_balance_ratio": 0.99,
"fiat_display_currency": "USD",
"amount_reserve_percent" : 0.05,
"amend_last_stake_amount": false,
"last_stake_amount_min_ratio": 0.5,
"dry_run": false,
"ticker_interval": "5m",
"trailing_stop": false,
@ -96,7 +99,6 @@
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"capital_available_percentage": 0.5,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,

2
config_kraken.json.example
View File

@ -2,6 +2,7 @@
"max_open_trades": 5,
"stake_currency": "EUR",
"stake_amount": 10,
"tradable_balance_ratio": 0.99,
"fiat_display_currency": "EUR",
"ticker_interval" : "5m",
"dry_run": true,
@ -70,7 +71,6 @@
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"capital_available_percentage": 0.5,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,

BIN
docs/assets/plot-dataframe.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 173 KiB

After

Width:  |  Height:  |  Size: 211 KiB

BIN
docs/assets/plot-dataframe2.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 190 KiB

26
docs/backtesting.md
View File

@ -78,13 +78,17 @@ Please also read about the [strategy startup period](strategy-customization.md#s
#### Supplying custom fee value
Sometimes your account has certain fee rebates (fee reductions starting with a certain account size or monthly volume), which are not visible to ccxt.
To account for this in backtesting, you can use `--fee 0.001` to supply this value to backtesting.
This fee must be a percentage, and will be applied twice (once for trade entry, and once for trade exit).
To account for this in backtesting, you can use the `--fee` command line option to supply this value to backtesting.
This fee must be a ratio, and will be applied twice (once for trade entry, and once for trade exit).
For example, if the buying and selling commission fee is 0.1% (i.e., 0.001 written as ratio), then you would run backtesting as the following:
```bash
freqtrade backtesting --fee 0.001
```
!!! Note
Only supply this option (or the corresponding configuration parameter) if you want to experiment with different fee values. By default, Backtesting fetches the default fee from the exchange pair/market info.
#### Running backtest with smaller testset by using timerange
@ -137,12 +141,12 @@ A backtesting result will look like that:
| ZEC/BTC | 22 | -0.46 | -10.18 | -0.00050971 | -5.09 | 2:22:00 | 7 | 15 |
| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 243 |
========================================================= SELL REASON STATS =========================================================
| Sell Reason | Count |
|:-------------------|--------:|
| trailing_stop_loss | 205 |
| stop_loss | 166 |
| sell_signal | 56 |
| force_sell | 2 |
| Sell Reason | Count | Profit | Loss |
|:-------------------|--------:|---------:|-------:|
| trailing_stop_loss | 205 | 150 | 55 |
| stop_loss | 166 | 0 | 166 |
| sell_signal | 56 | 36 | 20 |
| force_sell | 2 | 0 | 2 |
====================================================== LEFT OPEN TRADES REPORT ======================================================
| pair | buy count | avg profit % | cum profit % | tot profit BTC | tot profit % | avg duration | profit | loss |
|:---------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|
@ -154,6 +158,7 @@ A backtesting result will look like that:
The 1st table contains all trades the bot made, including "left open trades".
The 2nd table contains a recap of sell reasons.
This table can tell you which area needs some additional work (i.e. all `sell_signal` trades are losses, so we should disable the sell-signal or work on improving that).
The 3rd table contains all trades the bot had to `forcesell` at the end of the backtest period to present a full picture.
This is necessary to simulate realistic behaviour, since the backtest period has to end at some point, while realistically, you could leave the bot running forever.
@ -194,7 +199,10 @@ Since backtesting lacks some detailed information about what happens within a ca
- Buys happen at open-price
- Sell signal sells happen at open-price of the following candle
- Low happens before high for stoploss, protecting capital first.
- ROI sells are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the sell will be at 2%)
- ROI
- sells are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the sell will be at 2%)
- sells are never "below the candle", so a ROI of 2% may result in a sell at 2.4% if low was at 2.4% profit
- Forcesells caused by `<N>=-1` ROI entries use low as sell value, unless N falls on the candle open (e.g. `120: -1` for 1h candles)
- Stoploss sells happen exactly at stoploss price, even if low was lower
- Trailing stoploss
- High happens first - adjusting stoploss

48
docs/bot-usage.md
View File

@ -45,14 +45,17 @@ optional arguments:
-h, --help show this help message and exit
--db-url PATH Override trades database URL, this is useful in custom
deployments (default: `sqlite:///tradesv3.sqlite` for
Live Run mode, `sqlite://` for Dry Run).
Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for
Dry Run).
--sd-notify Notify systemd service manager.
--dry-run Enforce dry-run for trading (removes Exchange secrets
and simulates trades).
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified.
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`).
@ -68,6 +71,7 @@ Strategy arguments:
Specify strategy class name which will be used by the
bot.
--strategy-path PATH Specify additional strategy lookup path.
```
### How to specify which configuration file be used?
@ -192,8 +196,8 @@ Backtesting also uses the config specified via `-c/--config`.
usage: freqtrade backtesting [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH] [-s NAME]
[--strategy-path PATH] [-i TICKER_INTERVAL]
[--timerange TIMERANGE] [--max_open_trades INT]
[--stake_amount STAKE_AMOUNT] [--fee FLOAT]
[--timerange TIMERANGE] [--max-open-trades INT]
[--stake-amount STAKE_AMOUNT] [--fee FLOAT]
[--eps] [--dmmp]
[--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
[--export EXPORT] [--export-filename PATH]
@ -205,10 +209,12 @@ optional arguments:
`1d`).
--timerange TIMERANGE
Specify what timerange of data to use.
--max_open_trades INT
Specify max_open_trades to use.
--stake_amount STAKE_AMOUNT
Specify stake_amount.
--max-open-trades INT
Override the value of the `max_open_trades`
configuration setting.
--stake-amount STAKE_AMOUNT
Override the value of the `stake_amount` configuration
setting.
--fee FLOAT Specify fee ratio. Will be applied twice (on trade
entry and exit).
--eps, --enable-position-stacking
@ -270,8 +276,8 @@ to find optimal parameter values for your stategy.
usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--userdir PATH] [-s NAME] [--strategy-path PATH]
[-i TICKER_INTERVAL] [--timerange TIMERANGE]
[--max_open_trades INT]
[--stake_amount STAKE_AMOUNT] [--fee FLOAT]
[--max-open-trades INT]
[--stake-amount STAKE_AMOUNT] [--fee FLOAT]
[--hyperopt NAME] [--hyperopt-path PATH] [--eps]
[-e INT]
[--spaces {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]]
@ -286,10 +292,12 @@ optional arguments:
`1d`).
--timerange TIMERANGE
Specify what timerange of data to use.
--max_open_trades INT
Specify max_open_trades to use.
--stake_amount STAKE_AMOUNT
Specify stake_amount.
--max-open-trades INT
Override the value of the `max_open_trades`
configuration setting.
--stake-amount STAKE_AMOUNT
Override the value of the `stake_amount` configuration
setting.
--fee FLOAT Specify fee ratio. Will be applied twice (on trade
entry and exit).
--hyperopt NAME Specify hyperopt class name which will be used by the
@ -360,7 +368,7 @@ To know your trade expectancy and winrate against historical data, you can use E
usage: freqtrade edge [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--userdir PATH] [-s NAME] [--strategy-path PATH]
[-i TICKER_INTERVAL] [--timerange TIMERANGE]
[--max_open_trades INT] [--stake_amount STAKE_AMOUNT]
[--max-open-trades INT] [--stake-amount STAKE_AMOUNT]
[--fee FLOAT] [--stoplosses STOPLOSS_RANGE]
optional arguments:
@ -370,10 +378,12 @@ optional arguments:
`1d`).
--timerange TIMERANGE
Specify what timerange of data to use.
--max_open_trades INT
Specify max_open_trades to use.
--stake_amount STAKE_AMOUNT
Specify stake_amount.
--max-open-trades INT
Override the value of the `max_open_trades`
configuration setting.
--stake-amount STAKE_AMOUNT
Override the value of the `stake_amount` configuration
setting.
--fee FLOAT Specify fee ratio. Will be applied twice (on trade
entry and exit).
--stoplosses STOPLOSS_RANGE

185
docs/configuration.md
View File

@ -38,16 +38,19 @@ The prevelance for all Options is as follows:
Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways.
| Command | Description |
|----------|-------------|
| `max_open_trades` | **Required.** Number of trades open your bot will have. If -1 then it is ignored (i.e. potentially unlimited open trades).<br> ***Datatype:*** *Positive integer or -1.*
| Parameter | Description |
|------------|-------------|
| `max_open_trades` | **Required.** Number of trades open your bot will have. If -1 then it is ignored (i.e. potentially unlimited open trades). [More information below](#configuring-amount-per-trade).<br> ***Datatype:*** *Positive integer or -1.*
| `stake_currency` | **Required.** Crypto-currency used for trading. [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *String*
| `stake_amount` | **Required.** Amount of crypto-currency your bot will use for each trade. Set it to `"unlimited"` to allow the bot to use all available balance. [More information below](#understand-stake_amount). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Positive float or `"unlimited"`.*
| `stake_amount` | **Required.** Amount of crypto-currency your bot will use for each trade. Set it to `"unlimited"` to allow the bot to use all available balance. [More information below](#configuring-amount-per-trade). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Positive float or `"unlimited"`.*
| `tradable_balance_ratio` | Ratio of the total account balance the bot is allowed to trade. [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.99` 99%).*<br> ***Datatype:*** *Positive float between `0.1` and `1.0`.*
| `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*
| `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` | Overrides the default amount of 999.9 stake currency units in the wallet used by the bot running in the Dry Run mode if you need it for any reason. <br> ***Datatype:*** *Float*
| `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*
| `process_only_new_candles` | Enable processing of indicators only when new candles arrive. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> ***Datatype:*** *Boolean*
| `minimal_roi` | **Required.** Set the threshold in percent the bot will use to sell a trade. [More information below](#understand-minimal_roi). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Dict*
| `stoploss` | **Required.** Value of the stoploss in percent used by the bot. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Float (as ratio)*
@ -55,14 +58,14 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `trailing_stop_positive` | Changes stoploss once profit has been reached. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Float*
| `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0` (no offset).* <br> ***Datatype:*** *Float*
| `trailing_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. <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. <br> ***Datatype:*** *Integer*
| `bid_strategy.ask_last_balance` | **Required.** Set the bidding price. More information [below](#understand-ask_last_balance).
| `bid_strategy.use_order_book` | Enable buying using the rates in Order Book Bids. <br> ***Datatype:*** *Boolean*
| `bid_strategy.order_book_top` | Bot will use the top N rate in Order Book Bids. I.e. a value of 2 will allow the bot to pick the 2nd bid rate in Order Book Bids. *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. <br>*Defaults to `false`.* <br> ***Datatype:*** *Boolean*
| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | The % difference of buy orders and sell orders found in Order Book. A value lesser than 1 means sell orders is greater, while value greater than 1 means buy orders is higher. *Defaults to `0`.* <br> ***Datatype:*** *Float (as ratio)*
| `ask_strategy.use_order_book` | Enable selling of open trades using Order Book Asks. <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.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.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*
| `ask_strategy.use_sell_signal` | Use sell signals produced by the strategy in addition to the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.* <br> ***Datatype:*** *Boolean*
@ -72,9 +75,9 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `order_time_in_force` | Configure time in force for buy and sell orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Dict*
| `exchange.name` | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename). <br> ***Datatype:*** *String*
| `exchange.sandbox` | Use the 'sandbox' version of the exchange, where the exchange provides a sandbox for risk-free integration. See [here](sandbox-testing.md) in more details.<br> ***Datatype:*** *Boolean*
| `exchange.key` | API key to use for the exchange. Only required when you are in production mode. **Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
| `exchange.secret` | API secret to use for the exchange. Only required when you are in production mode. **Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
| `exchange.password` | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests. **Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
| `exchange.key` | API key to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
| `exchange.secret` | API secret to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
| `exchange.password` | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests.<br>**Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
| `exchange.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Not used by VolumePairList (see [below](#dynamic-pairlists)). <br> ***Datatype:*** *List*
| `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting (see [below](#dynamic-pairlists)). <br> ***Datatype:*** *List*
| `exchange.ccxt_config` | Additional CCXT parameters passed to the regular ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> ***Datatype:*** *Dict*
@ -84,19 +87,19 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `experimental.block_bad_exchanges` | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now. <br>*Defaults to `true`.* <br> ***Datatype:*** *Boolean*
| `pairlists` | Define one or more pairlists to be used. [More information below](#dynamic-pairlists). <br>*Defaults to `StaticPairList`.* <br> ***Datatype:*** *List of Dicts*
| `telegram.enabled` | Enable the usage of Telegram. <br> ***Datatype:*** *Boolean*
| `telegram.token` | Your Telegram bot token. Only required if `telegram.enabled` is `true`. **Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
| `telegram.chat_id` | Your personal Telegram account id. Only required if `telegram.enabled` is `true`. **Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
| `telegram.token` | Your Telegram bot token. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
| `telegram.chat_id` | Your personal Telegram account id. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
| `webhook.enabled` | Enable usage of Webhook notifications <br> ***Datatype:*** *Boolean*
| `webhook.url` | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> ***Datatype:*** *String*
| `webhook.webhookbuy` | Payload to send on buy. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details. <br> ***Datatype:*** *String*
| `webhook.webhooksell` | Payload to send on sell. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details. <br> ***Datatype:*** *String*
| `webhook.webhookstatus` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details. <br> ***Datatype:*** *String*
| `webhook.webhookbuy` | Payload to send on buy. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> ***Datatype:*** *String*
| `webhook.webhooksell` | Payload to send on sell. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> ***Datatype:*** *String*
| `webhook.webhookstatus` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> ***Datatype:*** *String*
| `api_server.enabled` | Enable usage of API Server. See the [API Server documentation](rest-api.md) for more details. <br> ***Datatype:*** *Boolean*
| `api_server.listen_ip_address` | Bind IP address. See the [API Server documentation](rest-api.md) for more details. <br> ***Datatype:*** *IPv4*
| `api_server.listen_port` | Bind Port. See the [API Server documentation](rest-api.md) for more details. <br> ***Datatype:*** *Integer between 1024 and 65535*
| `api_server.username` | Username for API server. See the [API Server documentation](rest-api.md) for more details. **Keep it in secret, do not disclose publicly.**<br> ***Datatype:*** *String*
| `api_server.password` | Password for API server. See the [API Server documentation](rest-api.md) for more details. **Keep it in secret, do not disclose publicly.**<br> ***Datatype:*** *String*
| `db_url` | Declares database URL to use. NOTE: This defaults to `sqlite://` if `dry_run` is `true`, and to `sqlite:///tradesv3.sqlite` for production instances. <br> ***Datatype:*** *String, SQLAlchemy connect string*
| `api_server.listen_port` | Bind Port. See the [API Server documentation](rest-api.md) for more details. <br>***Datatype:*** *Integer between 1024 and 65535*
| `api_server.username` | Username for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> ***Datatype:*** *String*
| `api_server.password` | Password for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> ***Datatype:*** *String*
| `db_url` | Declares database URL to use. NOTE: This defaults to `sqlite:///tradesv3.dryrun.sqlite` if `dry_run` is `true`, and to `sqlite:///tradesv3.sqlite` for production instances. <br> ***Datatype:*** *String, SQLAlchemy connect string*
| `initial_state` | Defines the initial application state. More information below. <br>*Defaults to `stopped`.* <br> ***Datatype:*** *Enum, either `stopped` or `running`*
| `forcebuy_enable` | Enables the RPC Commands to force a buy. More information below. <br> ***Datatype:*** *Boolean*
| `strategy` | **Required** Defines Strategy class to use. Recommended to be set via `--strategy NAME`. <br> ***Datatype:*** *ClassName*
@ -124,24 +127,63 @@ Values set in the configuration file always overwrite values set in the strategy
* `order_time_in_force`
* `stake_currency`
* `stake_amount`
* `unfilledtimeout`
* `use_sell_signal` (ask_strategy)
* `sell_profit_only` (ask_strategy)
* `ignore_roi_if_buy_signal` (ask_strategy)
### Understand stake_amount
### Configuring amount per trade
The `stake_amount` configuration parameter is an amount of crypto-currency your bot will use for each trade.
There are several methods to configure how much of the stake currency the bot will use to enter a trade. All methods respect the [available balance configuration](#available-balance) as explained below.
The minimal configuration value is 0.0001. Please check your exchange's trading minimums to avoid problems.
#### Available balance
By default, the bot assumes that the `complete amount - 1%` is at it's disposal, and when using [dynamic stake amount](#dynamic-stake-amount), it will split the complete balance into `max_open_trades` buckets per trade.
Freqtrade will reserve 1% for eventual fees when entering a trade and will therefore not touch that by default.
You can configure the "untouched" amount by using the `tradable_balance_ratio` setting.
For example, if you have 10 ETH available in your wallet on the exchange and `tradable_balance_ratio=0.5` (which is 50%), then the bot will use a maximum amount of 5 ETH for trading and considers this as available balance. The rest of the wallet is untouched by the trades.
!!! Warning
The `tradable_balance_ratio` setting applies to the current balance (free balance + tied up in trades). Therefore, assuming the starting balance of 1000, a configuration with `tradable_balance_ratio=0.99` will not guarantee that 10 currency units will always remain available on the exchange. For example, the free amount may reduce to 5 units if the total balance is reduced to 500 (either by a losing streak, or by withdrawing balance).
#### Amend last stake amount
Assuming we have the tradable balance of 1000 USDT, `stake_amount=400`, and `max_open_trades=3`.
The bot would open 2 trades, and will be unable to fill the last trading slot, since the requested 400 USDT are no longer available, since 800 USDT are already tied in other trades.
To overcome this, the option `amend_last_stake_amount` can be set to `True`, which will enable the bot to reduce stake_amount to the available balance in order to fill the last trade slot.
In the example above this would mean:
- Trade1: 400 USDT
- Trade2: 400 USDT
- Trade3: 200 USDT
!!! Note
This option only applies with [Static stake amount](#static-stake-amount) - since [Dynamic stake amount](#dynamic-stake-amount) divides the balances evenly.
!!! Note
The minimum last stake amount can be configured using `amend_last_stake_amount` - which defaults to 0.5 (50%). This means that the minimum stake amount that's ever used is `stake_amount * 0.5`. This avoids very low stake amounts, that are close to the minimum tradable amount for the pair and can be refused by the exchange.
#### Static stake amount
The `stake_amount` configuration statically configures the amount of stake-currency your bot will use for each trade.
The minimal configuration value is 0.0001, however, please check your exchange's trading minimums for the stake currency you're using to avoid problems.
This setting works in combination with `max_open_trades`. The maximum capital engaged in trades is `stake_amount * max_open_trades`.
For example, the bot will at most use (0.05 BTC x 3) = 0.15 BTC, assuming a configuration of `max_open_trades=3` and `stake_amount=0.05`.
To allow the bot to trade all the available `stake_currency` in your account set
!!! Note
This setting respects the [available balance configuration](#available-balance).
```json
"stake_amount" : "unlimited",
```
#### Dynamic stake amount
Alternatively, you can use a dynamic stake amount, which will use the available balance on the exchange, and divide that equally by the amount of allowed trades (`max_open_trades`).
To configure this, set `stake_amount="unlimited"`. We also recommend to set `tradable_balance_ratio=0.99` (99%) - to keep a minimum balance for eventual fees.
In this case a trade amount is calculated as:
@ -149,6 +191,19 @@ In this case a trade amount is calculated as:
currency_balance / (max_open_trades - current_open_trades)
```
To allow the bot to trade all the available `stake_currency` in your account (minus `tradable_balance_ratio`) set
```json
"stake_amount" : "unlimited",
"tradable_balance_ratio": 0.99,
```
!!! Note
This configuration will allow increasing / decreasing stakes depending on the performance of the bot (lower stake if bot is loosing, higher stakes if the bot has a winning record, since higher balances are available).
!!! Note "When using Dry-Run Mode"
When using `"stake_amount" : "unlimited",` in combination with Dry-Run, the balance will be simulated starting with a stake of `dry_run_wallet` which will evolve over time. It is therefore important to set `dry_run_wallet` to a sensible value (like 0.05 or 0.01 for BTC and 1000 or 100 for USDT, for example), otherwise it may simulate trades with 100 BTC (or more) or 0.05 USDT (or less) at once - which may not correspond to your real available balance or is less than the exchange minimal limit for the order amount for the stake currency.
### Understand minimal_roi
The `minimal_roi` configuration parameter is a JSON object where the key is a duration
@ -169,6 +224,9 @@ This parameter can be set in either Strategy or Configuration file. If you use i
`minimal_roi` value from the strategy file.
If it is not set in either Strategy or Configuration, a default of 1000% `{"0": 10}` is used, and minimal roi is disabled unless your trade generates 1000% profit.
!!! Note "Special case to forcesell after a specific time"
A special case presents using `"<N>": -1` as ROI. This forces the bot to sell a trade after N Minutes, no matter if it's positive or negative, so represents a time-limited force-sell.
### Understand stoploss
Go to the [stoploss documentation](stoploss.md) for more details.
@ -201,13 +259,6 @@ before asking the strategy if we should buy or a sell an asset. After each wait
every opened trade wether or not we should sell, and for all the remaining pairs (either the dynamic list of pairs or
the static list of pairs) if we should buy.
### Understand ask_last_balance
The `ask_last_balance` configuration parameter sets the bidding price. Value `0.0` will use `ask` price, `1.0` will
use the `last` price and values between those interpolate between ask and last
price. Using `ask` price will guarantee quick success in bid, but bot will also
end up paying more then would probably have been necessary.
### Understand order_types
The `order_types` configuration parameter maps actions (`buy`, `sell`, `stoploss`) to order-types (`market`, `limit`, ...) as well as configures stoploss to be on the exchange and defines stoploss on exchange update interval in seconds.
@ -387,6 +438,54 @@ The valid values are:
"BTC", "ETH", "XRP", "LTC", "BCH", "USDT"
```
## Prices used for orders
Prices for regular orders can be controlled via the parameter structures `bid_strategy` for buying and `ask_strategy` for selling.
Prices are always retrieved right before an order is placed, either by querying the exchange tickers or by using the orderbook data.
!!! Note
Orderbook data used by Freqtrade are the data retrieved from exchange by the ccxt's function `fetch_order_book()`, i.e. are usually data from the L2-aggregated orderbook, while the ticker data are the structures returned by the ccxt's `fetch_ticker()`/`fetch_tickers()` functions. Refer to the ccxt library [documentation](https://github.com/ccxt/ccxt/wiki/Manual#market-data) for more details.
### Buy price
#### Check depth of market
When check depth of market is enabled (`bid_strategy.check_depth_of_market.enabled=True`), the buy signals are filtered based on the orderbook depth (sum of all amounts) for each orderbook side.
Orderbook `bid` (buy) side depth is then divided by the orderbook `ask` (sell) side depth and the resulting delta is compared to the value of the `bid_strategy.check_depth_of_market.bids_to_ask_delta` parameter. The buy order is only executed if the orderbook delta is greater than or equal to the configured delta value.
!!! 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 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.
#### 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 `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.
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.
### Sell 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.
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.
#### 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.
## Pairlists
Pairlists define the list of pairs that the bot should trade.
@ -498,8 +597,10 @@ creating trades on the exchange.
}
```
Once you will be happy with your bot performance running in the Dry-run mode,
you can switch it to production mode.
Once you will be happy with your bot performance running in the Dry-run mode, you can switch it to production mode.
!!! Note
A simulated wallet is available during dry-run mode, and will assume a starting capital of `dry_run_wallet` (defaults to 1000).
## Switch to production mode
@ -529,7 +630,7 @@ you run it in production mode.
```
!!! Note
If you have an exchange API key yet, [see our tutorial](/pre-requisite).
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.

21
docs/data-analysis.md
View File

@ -8,6 +8,27 @@ You can analyze the results of backtests and trading history easily using Jupyte
* Don't forget to start a Jupyter notebook server from within your conda or venv environment or use [nb_conda_kernels](https://github.com/Anaconda-Platform/nb_conda_kernels)*
* Copy the example notebook before use so your changes don't get clobbered with the next freqtrade update.
### Using virtual environment with system-wide Jupyter installation
Sometimes it can be desired to use a system-wide installation of Jupyter notebook, and use a jupyter kernel from the virtual environment.
This prevents you from installing the full jupyter suite multiple times per system, and provides an easy way to switch between tasks (freqtrade / other analytics tasks).
For this to work, first activate your virtual environment and run the following commands:
``` bash
# Activate virtual environment
source .env/bin/activate
pip install ipykernel
ipython kernel install --user --name=freqtrade
# Restart jupyter (lab / notebook)
# select kernel "freqtrade" in the notebook
```
!!! Note
This section is provided for completeness, the Freqtrade Team won't provide full support for problems with this setup and will recommend to install Jupyter in the virtual environment directly, as that is the easiest way to get jupyter notebooks up and running. For help with this setup please refer to the [Project Jupyter](https://jupyter.org/) [documentation](https://jupyter.org/documentation) or [help channels](https://jupyter.org/community).
## Fine print
Some tasks don't work especially well in notebooks. For example, anything using asynchronous execution is a problem for Jupyter. Also, freqtrade's primary entry point is the shell cli, so using pure python in a notebook bypasses arguments that provide required objects and parameters to helper functions. You may need to set those values or create expected objects manually.

44
docs/developer.md
View File

@ -183,17 +183,19 @@ raw = ct.fetch_ohlcv(pair, timeframe=timeframe)
# convert to dataframe
df1 = parse_ticker_dataframe(raw, timeframe, pair=pair, drop_incomplete=False)
print(df1["date"].tail(1))
print(df1.tail(1))
print(datetime.utcnow())
```
``` output
19 2019-06-08 00:00:00+00:00
date open high low close volume
499 2019-06-08 00:00:00+00:00 0.000007 0.000007 0.000007 0.000007 26264344.0
2019-06-09 12:30:27.873327
```
The output will show the last entry from the Exchange as well as the current UTC date.
If the day shows the same day, then the last candle can be assumed as incomplete and should be dropped (leave the setting `"ohlcv_partial_candle"` from the exchange-class untouched / True). Otherwise, set `"ohlcv_partial_candle"` to `False` to not drop Candles (shown in the example above).
Another way is to run this command multiple times in a row and observe if the volume is changing (while the date remains the same).
## Updating example notebooks
@ -246,6 +248,17 @@ Determine if crucial bugfixes have been made between this commit and the current
git log --oneline --no-decorate --no-merges master..new_release
```
To keep the release-log short, best wrap the full git changelog into a collapsible details secction.
```markdown
<details>
<summary>Expand full changelog</summary>
... Full git changelog
</details>
```
### Create github release / tag
Once the PR against master is merged (best right after merging):
@ -253,4 +266,29 @@ Once the PR against master is merged (best right after merging):
* Use the button "Draft a new release" in the Github UI (subsection releases).
* Use the version-number specified as tag.
* Use "master" as reference (this step comes after the above PR is merged).
* Use the above changelog as release comment (as codeblock).
* 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
To create a pypi release, please run the following commands:
Additional requirement: `wheel`, `twine` (for uploading), account on pypi with proper permissions.
``` bash
python setup.py sdist bdist_wheel
# For pypi test (to check if some change to the installation did work)
twine upload --repository-url https://test.pypi.org/legacy/ dist/*
# For production:
twine upload dist/*
```
Please don't push non-releases to the productive / real pypi instance.

3
docs/docker.md
View File

@ -164,8 +164,7 @@ docker run -d \
```
!!! Note
db-url defaults to `sqlite:///tradesv3.sqlite` but it defaults to `sqlite://` if `dry_run=True` is being used.
To override this behaviour use a custom db-url value: i.e.: `--db-url sqlite:///tradesv3.dryrun.sqlite`
When using docker, it's best to specify `--db-url` explicitly to ensure that the database URL and the mounted database file match.
!!! Note
All available bot command line parameters can be added to the end of the `docker run` command.

153
docs/edge.md
View File

@ -1,4 +1,4 @@
# Edge positioning
# Edge positioning
This page explains how to use Edge Positioning module in your bot in order to enter into a trade only if the trade has a reasonable win rate and risk reward ratio, and consequently adjust your position size and stoploss.
@ -9,6 +9,7 @@ This page explains how to use Edge Positioning module in your bot in order to en
Edge does not consider anything else than buy/sell/stoploss signals. So trailing stoploss, ROI, and everything else are ignored in its calculation.
## Introduction
Trading is all about probability. No one can claim that he has a strategy working all the time. You have to assume that sometimes you lose.
But it doesn't mean there is no rule, it only means rules should work "most of the time". Let's play a game: we toss a coin, heads: I give you 10$, tails: you give me 10$. Is it an interesting game? No, it's quite boring, isn't it?
@ -22,43 +23,61 @@ Let's complicate it more: you win 80% of the time but only 2$, I win 20% of the
The question is: How do you calculate that? How do you know if you wanna play?
The answer comes to two factors:
- Win Rate
- Risk Reward Ratio
### Win Rate
Win Rate (*W*) is is the mean over some amount of trades (*N*) what is the percentage of winning trades to total number of trades (note that we don't consider how much you gained but only if you won or not).
W = (Number of winning trades) / (Total number of trades) = (Number of winning trades) / N
```
W = (Number of winning trades) / (Total number of trades) = (Number of winning trades) / N
```
Complementary Loss Rate (*L*) is defined as
L = (Number of losing trades) / (Total number of trades) = (Number of losing trades) / N
```
L = (Number of losing trades) / (Total number of trades) = (Number of losing trades) / N
```
or, which is the same, as
L = 1 – W
```
L = 1 – W
```
### Risk Reward Ratio
Risk Reward Ratio (*R*) is a formula used to measure the expected gains of a given investment against the risk of loss. It is basically what you potentially win divided by what you potentially lose:
R = Profit / Loss
```
R = Profit / Loss
```
Over time, on many trades, you can calculate your risk reward by dividing your average profit on winning trades by your average loss on losing trades:
Average profit = (Sum of profits) / (Number of winning trades)
```
Average profit = (Sum of profits) / (Number of winning trades)
Average loss = (Sum of losses) / (Number of losing trades)
Average loss = (Sum of losses) / (Number of losing trades)
R = (Average profit) / (Average loss)
R = (Average profit) / (Average loss)
```
### Expectancy
At this point we can combine *W* and *R* to create an expectancy ratio. This is a simple process of multiplying the risk reward ratio by the percentage of winning trades and subtracting the percentage of losing trades, which is calculated as follows:
Expectancy Ratio = (Risk Reward Ratio X Win Rate) – Loss Rate = (R X W) – L
```
Expectancy Ratio = (Risk Reward Ratio X Win Rate) – Loss Rate = (R X W) – L
```
So lets say your Win rate is 28% and your Risk Reward Ratio is 5:
Expectancy = (5 X 0.28) – 0.72 = 0.68
```
Expectancy = (5 X 0.28) – 0.72 = 0.68
```
Superficially, this means that on average you expect this strategy’s trades to return .68 times the size of your loses. This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
@ -69,6 +88,7 @@ You can also use this value to evaluate the effectiveness of modifications to th
**NOTICE:** It's important to keep in mind that Edge is testing your expectancy using historical data, there's no guarantee that you will have a similar edge in the future. It's still vital to do this testing in order to build confidence in your methodology, but be wary of "curve-fitting" your approach to the historical data as things are unlikely to play out the exact same way for future trades.
## How does it work?
If enabled in config, Edge will go through historical data with a range of stoplosses in order to find buy and sell/stoploss signals. It then calculates win rate and expectancy over *N* trades for each stoploss. Here is an example:
| Pair | Stoploss | Win Rate | Risk Reward Ratio | Expectancy |
@ -83,6 +103,7 @@ The goal here is to find the best stoploss for the strategy in order to have the
Edge module then forces stoploss value it evaluated to your strategy dynamically.
### Position size
Edge also dictates the stake amount for each trade to the bot according to the following factors:
- Allowed capital at risk
@ -90,13 +111,17 @@ Edge also dictates the stake amount for each trade to the bot according to the f
Allowed capital at risk is calculated as follows:
Allowed capital at risk = (Capital available_percentage) X (Allowed risk per trade)
```
Allowed capital at risk = (Capital available_percentage) X (Allowed risk per trade)
```
Stoploss is calculated as described above against historical data.
Your position size then will be:
Position size = (Allowed capital at risk) / Stoploss
```
Position size = (Allowed capital at risk) / Stoploss
```
Example:
@ -115,100 +140,30 @@ Available capital doesn’t change before a position is sold. Let’s assume tha
So the Bot receives another buy signal for trade 4 with a stoploss at 2% then your position size would be **0.055 / 0.02 = 2.75 ETH**.
## Configurations
Edge module has following configuration options:
#### enabled
If true, then Edge will run periodically.
(defaults to false)
#### process_throttle_secs
How often should Edge run in seconds?
(defaults to 3600 so one hour)
#### calculate_since_number_of_days
Number of days of data against which Edge calculates Win Rate, Risk Reward and Expectancy
Note that it downloads historical data so increasing this number would lead to slowing down the bot.
(defaults to 7)
#### capital_available_percentage
This is the percentage of the total capital on exchange in stake currency.
As an example if you have 10 ETH available in your wallet on the exchange and this value is 0.5 (which is 50%), then the bot will use a maximum amount of 5 ETH for trading and considers it as available capital.
(defaults to 0.5)
#### allowed_risk
Percentage of allowed risk per trade.
(defaults to 0.01 so 1%)
#### stoploss_range_min
Minimum stoploss.
(defaults to -0.01)
#### stoploss_range_max
Maximum stoploss.
(defaults to -0.10)
#### stoploss_range_step
As an example if this is set to -0.01 then Edge will test the strategy for \[-0.01, -0,02, -0,03 ..., -0.09, -0.10\] ranges.
Note than having a smaller step means having a bigger range which could lead to slow calculation.
If you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10.
(defaults to -0.01)
#### minimum_winrate
It filters out pairs which don't have at least minimum_winrate.
This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio.
(defaults to 0.60)
#### minimum_expectancy
It filters out pairs which have the expectancy lower than this number.
Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return.
(defaults to 0.20)
#### 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.
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.
(defaults to 10, it is highly recommended not to decrease this number)
#### 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.
**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.).
(defaults to 1 day, i.e. to 60 * 24 = 1440 minutes)
#### 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.
(defaults to false)
| Parameter | Description |
|------------|-------------|
| `enabled` | If true, then Edge will run periodically. <br>*Defaults to `false`.* <br> ***Datatype:*** *Boolean*
| `process_throttle_secs` | How often should Edge run in seconds. <br>*Defaults to `3600` (once per hour).* <br> ***Datatype:*** *Integer*
| `calculate_since_number_of_days` | Number of days of data against which Edge calculates Win Rate, Risk Reward and Expectancy. <br> **Note** that it downloads historical data so increasing this number would lead to slowing down the bot. <br>*Defaults to `7`.* <br> ***Datatype:*** *Integer*
| `capital_available_percentage` | **DEPRECATED - [replaced with `tradable_balance_ratio`](configuration.md#Available balance)** This is the percentage of the total capital on exchange in stake currency. <br>As an example if you have 10 ETH available in your wallet on the exchange and this value is 0.5 (which is 50%), then the bot will use a maximum amount of 5 ETH for trading and considers it as available capital. <br>*Defaults to `0.5`.* <br> ***Datatype:*** *Float*
| `allowed_risk` | Ratio of allowed risk per trade. <br>*Defaults to `0.01` (1%)).* <br> ***Datatype:*** *Float*
| `stoploss_range_min` | Minimum stoploss. <br>*Defaults to `-0.01`.* <br> ***Datatype:*** *Float*
| `stoploss_range_max` | Maximum stoploss. <br>*Defaults to `-0.10`.* <br> ***Datatype:*** *Float*
| `stoploss_range_step` | As an example if this is set to -0.01 then Edge will test the strategy for `[-0.01, -0,02, -0,03 ..., -0.09, -0.10]` ranges. <br> **Note** than having a smaller step means having a bigger range which could lead to slow calculation. <br> If you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10. <br>*Defaults to `-0.001`.* <br> ***Datatype:*** *Float*
| `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*
| `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
You can run Edge independently in order to see in details the result. Here is an example:
```bash
``` bash
freqtrade edge
```

21
docs/exchanges.md
View File

@ -61,3 +61,24 @@ print(res)
```shell
$ 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.
Whether your exchange returns incomplete candles or not can be checked using [the helper script](developer.md#Incomplete-candles) from the Contributor documentation.
If the exchange does return incomplete candles and you would like to have incomplete candles in your strategy, you can set the following parameter in the configuration file.
``` json
{
"exchange": {
"_ft_has_params": {"ohlcv_partial_candle": false}
}
}
```
!!! Warning "Danger of repainting"
Changing this parameter makes the strategy responsible to avoid repainting and handle this accordingly. Doing this is therefore not recommended, and should only be performed by experienced users who are fully aware of the impact this setting has.

20
docs/hyperopt.md
View File

@ -6,8 +6,12 @@ algorithms included in the `scikit-optimize` package to accomplish this. The
search will burn all your CPU cores, make your laptop sound like a fighter jet
and still take a long time.
In general, the search for best parameters starts with a few random combinations and then uses Bayesian search with a
ML regressor algorithm (currently ExtraTreesRegressor) to quickly find a combination of parameters in the search hyperspace
that minimizes the value of the [loss function](#loss-functions).
Hyperopt requires historic data to be available, just as backtesting does.
To learn how to get data for the pairs and exchange you're interrested in, head over to the [Data Downloading](data-download.md) section of the documentation.
To learn how to get data for the pairs and exchange you're interested in, head over to the [Data Downloading](data-download.md) section of the documentation.
!!! Bug
Hyperopt can crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
@ -170,10 +174,6 @@ with different value combinations. It will then use the given historical data an
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.
The search for best parameters starts with a few random combinations and then uses a
regressor algorithm (currently ExtraTreesRegressor) to quickly find a parameter combination
that minimizes the value of the [loss function](#loss-functions).
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
add it to the `populate_indicators()` method in your custom hyperopt file.
@ -284,6 +284,16 @@ number).
You can also enable position stacking in the configuration file by explicitly setting
`"position_stacking"=true`.
### Reproducible results
The search for optimal parameters starts with a few (currently 30) random combinations in the hyperspace of parameters, random Hyperopt epochs. These random epochs are marked with a leading asterisk sign at the Hyperopt output.
The initial state for generation of these random values (random state) is controlled by the value of the `--random-state` command line option. You can set it to some arbitrary value of your choice to obtain reproducible results.
If you have not set this value explicitly in the command line options, Hyperopt seeds the random state with some random value for you. The random state value for each Hyperopt run is shown in the log, so you can copy and paste it into the `--random-state` command line option to repeat the set of the initial random epochs used.
If you have not changed anything in the command line options, configuration, timerange, Strategy and Hyperopt classes, historical data and the Loss Function -- you should obtain same hyperoptimization results with same random state value used.
## Understand the Hyperopt Result
Once Hyperopt is completed you can use the result to create a new strategy.

31
docs/index.md
View File

@ -11,8 +11,10 @@
<a class="github-button" href="https://github.com/freqtrade/freqtrade/archive/master.zip" data-icon="octicon-cloud-download" data-size="large" aria-label="Download freqtrade/freqtrade on GitHub">Download</a>
<!-- Place this tag where you want the button to render. -->
<a class="github-button" href="https://github.com/freqtrade" data-size="large" aria-label="Follow @freqtrade on GitHub">Follow @freqtrade</a>
## Introduction
Freqtrade is a cryptocurrency trading bot written in Python.
Freqtrade is a crypto-currency algorithmic trading software developed in python (3.6+) and supported on Windows, macOS and Linux.
!!! Danger "DISCLAIMER"
This software is for educational purposes only. Do not risk money which you are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS.
@ -23,18 +25,15 @@ Freqtrade is a cryptocurrency trading bot written in Python.
## Features
- Based on Python 3.6+: For botting on any operating system — Windows, macOS and Linux.
- Persistence: Persistence is achieved through sqlite database.
- Dry-run mode: Run the bot without playing money.
- Backtesting: Run a simulation of your buy/sell strategy with historical data.
- Strategy Optimization by machine learning: Use machine learning to optimize your buy/sell strategy parameters with real exchange data.
- Edge position sizing: Calculate your win rate, risk reward ratio, the best stoploss and adjust your position size before taking a position for each specific market.
- Whitelist crypto-currencies: Select which crypto-currency you want to trade or use dynamic whitelists based on market (pair) trade volume.
- Blacklist crypto-currencies: Select which crypto-currency you want to avoid.
- Manageable via Telegram or REST APi: Manage the bot with Telegram or via the builtin REST API.
- Display profit/loss in fiat: Display your profit/loss in any of 33 fiat currencies supported.
- Daily summary of profit/loss: Receive the daily summary of your profit/loss.
- Performance status report: Receive the performance status of your current trades.
- Develop your Strategy: Write your strategy in python, using [pandas](https://pandas.pydata.org/). Example strategies to inspire you are available in the [strategy repository](https://github.com/freqtrade/freqtrade-strategies).
- Download market data: Download historical data of the exchange and the markets your may want to trade with.
- Backtest: Test your strategy on downloaded historical data.
- Optimize: Find the best parameters for your strategy using hyperoptimization which employs machining learning methods. You can optimize buy, sell, take profit (ROI), stop-loss and trailing stop-loss parameters for your strategy.
- Select markets: Create your static list or use an automatic one based on top traded volumes and/or prices (not available during backtesting). You can also explicitly blacklist markets you don't want to trade.
- Run: Test your strategy with simulated money (Dry-Run mode) or deploy it with real money (Live-Trade mode).
- Run using Edge (optional module): The concept is to find the best historical [trade expectancy](edge.md#expectancy) by markets based on variation of the stop-loss and then allow/reject markets to trade. The sizing of the trade is based on a risk of a percentage of your capital.
- Control/Monitor: Use Telegram or a REST API (start/stop the bot, show profit/loss, daily summary, current open trades results, etc.).
- Analyse: Further analysis can be performed on either Backtesting data or Freqtrade trading history (SQL database), including automated standard plots, and methods to load the data into [interactive environments](data-analysis.md).
## Requirements
@ -61,10 +60,10 @@ To run this bot we recommend you a cloud instance with a minimum of:
## Support
Help / Slack
For any questions not covered by the documentation or for further information about the bot, we encourage you to join our Slack channel.
### Help / Slack
For any questions not covered by the documentation or for further information about the bot, we encourage you to join our passionate Slack community.
Click [here](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) to join Slack channel.
Click [here](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) to join the Freqtrade Slack channel.
## Ready to try?

15
docs/installation.md
View File

@ -270,3 +270,18 @@ The easiest way is to download install Microsoft Visual Studio Community [here](
Now you have an environment ready, the next step is
[Bot Configuration](configuration.md).
## Troubleshooting
### MacOS installation error
Newer versions of MacOS may have installation failed with errors like `error: command 'g++' failed with exit status 1`.
This error will require explicit installation of the SDK Headers, which are not installed by default in this version of MacOS.
For MacOS 10.14, this can be accomplished with the below command.
``` bash
open /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10.14.pkg
```
If this file is inexistant, then you're probably on a different version of MacOS, so you may need to consult the internet for specific resolution details.

143
docs/plotting.md
View File

@ -23,58 +23,43 @@ The `freqtrade plot-dataframe` subcommand shows an interactive graph with three
Possible arguments:
```
usage: freqtrade plot-dataframe [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH] [-s NAME]
[--strategy-path PATH] [-p PAIRS [PAIRS ...]]
[--indicators1 INDICATORS1 [INDICATORS1 ...]]
[--indicators2 INDICATORS2 [INDICATORS2 ...]]
[--plot-limit INT] [--db-url PATH]
[--trade-source {DB,file}] [--export EXPORT]
[--export-filename PATH]
[--timerange TIMERANGE] [-i TICKER_INTERVAL]
usage: freqtrade plot-dataframe [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [-s NAME]
[--strategy-path PATH] [-p PAIRS [PAIRS ...]] [--indicators1 INDICATORS1 [INDICATORS1 ...]]
[--indicators2 INDICATORS2 [INDICATORS2 ...]] [--plot-limit INT] [--db-url PATH]
[--trade-source {DB,file}] [--export EXPORT] [--export-filename PATH] [--timerange TIMERANGE]
[-i TICKER_INTERVAL]
optional arguments:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Show profits for only these pairs. Pairs are space-
separated.
Show profits for only these pairs. Pairs are space-separated.
--indicators1 INDICATORS1 [INDICATORS1 ...]
Set indicators from your strategy you want in the
first row of the graph. Space-separated list. Example:
Set indicators from your strategy you want in the first row of the graph. Space-separated list. Example:
`ema3 ema5`. Default: `['sma', 'ema3', 'ema5']`.
--indicators2 INDICATORS2 [INDICATORS2 ...]
Set indicators from your strategy you want in the
third row of the graph. Space-separated list. Example:
Set indicators from your strategy you want in the third row of the graph. Space-separated list. Example:
`fastd fastk`. Default: `['macd', 'macdsignal']`.
--plot-limit INT Specify tick limit for plotting. Notice: too high
values cause huge files. Default: 750.
--db-url PATH Override trades database URL, this is useful in custom
deployments (default: `sqlite:///tradesv3.sqlite` for
Live Run mode, `sqlite://` for Dry Run).
--plot-limit INT Specify tick limit for plotting. Notice: too high values cause huge files. Default: 750.
--db-url PATH Override trades database URL, this is useful in custom deployments (default: `sqlite:///tradesv3.sqlite`
for Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for Dry Run).
--trade-source {DB,file}
Specify the source for trades (Can be DB or file
(backtest file)) Default: file
--export EXPORT Export backtest results, argument are: trades.
Example: `--export=trades`
Specify the source for trades (Can be DB or file (backtest file)) Default: file
--export EXPORT Export backtest results, argument are: trades. Example: `--export=trades`
--export-filename PATH
Save backtest results to the file with this filename
(default: `user_data/backtest_results/backtest-
result.json`). Requires `--export` to be set as well.
Example: `--export-filename=user_data/backtest_results
/backtest_today.json`
Save backtest results to the file with this filename. Requires `--export` to be set as well. Example:
`--export-filename=user_data/backtest_results/backtest_today.json`
--timerange TIMERANGE
Specify what timerange of data to use.
-i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
`1d`).
Specify ticker interval (`1m`, `5m`, `30m`, `1h`, `1d`).
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified.
--logfile FILE Log to the file specified. Special values are: 'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`).
Multiple --config options may be used. Can be set to
Specify configuration file (default: `config.json`). 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.
@ -83,8 +68,7 @@ Common arguments:
Strategy arguments:
-s NAME, --strategy NAME
Specify strategy class name (default:
`DefaultStrategy`).
Specify strategy class name which will be used by the bot.
--strategy-path PATH Specify additional strategy lookup path.
```
@ -136,16 +120,77 @@ To plot trades from a backtesting result, use `--export-filename <filename>`
freqtrade plot-dataframe --strategy AwesomeStrategy --export-filename user_data/backtest_results/backtest-result.json -p BTC/ETH
```
### Plot dataframe basics
![plot-dataframe2](assets/plot-dataframe2.png)
The `plot-dataframe` subcommand requires backtesting data, a strategy and either a backtesting-results file or a database, containing trades corresponding to the strategy.
The resulting plot will have the following elements:
* Green triangles: Buy signals from the strategy. (Note: not every buy signal generates a trade, compare to cyan circles.)
* Red triangles: Sell signals from the strategy. (Also, not every sell signal terminates a trade, compare to red and green squares.)
* Cyan circles: Trade entry points.
* Red squares: Trade exit points for trades with loss or 0% profit.
* Green squares: Trade exit points for profitable trades.
* Indicators with values corresponding to the candle scale (e.g. SMA/EMA), as specified with `--indicators1`.
* Volume (bar chart at the bottom of the main chart).
* Indicators with values in different scales (e.g. MACD, RSI) below the volume bars, as specified with `--indicators2`.
!!! Note "Bollinger Bands"
Bollinger bands are automatically added to the plot if the columns `bb_lowerband` and `bb_upperband` exist, and are painted as a light blue area spanning from the lower band to the upper band.
#### Advanced plot configuration
An advanced plot configuration can be specified in the strategy in the `plot_config` parameter.
Additional features when using plot_config include:
* Specify colors per indicator
* Specify additional subplots
The sample plot configuration below specifies fixed colors for the indicators. Otherwise consecutive plots may produce different colorschemes each time, making comparisons difficult.
It also allows multiple subplots to display both MACD and RSI at the same time.
Sample configuration with inline comments explaining the process:
``` python
plot_config = {
'main_plot': {
# Configuration for main plot indicators.
# Specifies `ema10` to be red, and `ema50` to be a shade of gray
'ema10': {'color': 'red'},
'ema50': {'color': '#CCCCCC'},
# By omitting color, a random color is selected.
'sar': {},
},
'subplots': {
# Create subplot MACD
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
# Additional subplot RSI
"RSI": {
'rsi': {'color': 'red'},
}
}
}
```
!!! Note
The above configuration assumes that `ema10`, `ema50`, `macd`, `macdsignal` and `rsi` are columns in the DataFrame created by the strategy.
## Plot profit
![plot-profit](assets/plot-profit.png)
The `freqtrade plot-profit` subcommand shows an interactive graph with three plots:
The `plot-profit` subcommand shows an interactive graph with three plots:
1) Average closing price for all pairs
2) The summarized profit made by backtesting.
Note that this is not the real-world profit, but more of an estimate.
3) Profit for each individual pair
* Average closing price for all pairs.
* The summarized profit made by backtesting.
Note that this is not the real-world profit, but more of an estimate.
* Profit for each individual pair.
The first graph is good to get a grip of how the overall market progresses.
@ -173,14 +218,14 @@ optional arguments:
--export EXPORT Export backtest results, argument are: trades.
Example: `--export=trades`
--export-filename PATH
Save backtest results to the file with this filename
(default: `user_data/backtest_results/backtest-
result.json`). Requires `--export` to be set as well.
Example: `--export-filename=user_data/backtest_results
/backtest_today.json`
Save backtest results to the file with this filename.
Requires `--export` to be set as well. Example:
`--export-filename=user_data/backtest_results/backtest
_today.json`
--db-url PATH Override trades database URL, this is useful in custom
deployments (default: `sqlite:///tradesv3.sqlite` for
Live Run mode, `sqlite://` for Dry Run).
Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for
Dry Run).
--trade-source {DB,file}
Specify the source for trades (Can be DB or file
(backtest file)) Default: file
@ -190,7 +235,9 @@ optional arguments:
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified.
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`).

2
docs/requirements-docs.txt
View File

@ -1,2 +1,2 @@
mkdocs-material==4.5.1
mkdocs-material==4.6.0
mdx_truly_sane_lists==1.2

50
docs/strategy-customization.md
View File

@ -455,6 +455,51 @@ Sample return value: ETH/BTC had 5 trades, with a total profit of 1.5% (ratio of
!!! Warning
Trade history is not available during backtesting or hyperopt.
### Prevent trades from happening for a specific pair
Freqtrade locks pairs automatically for the current candle (until that candle is over) when a pair is sold, preventing an immediate re-buy of that pair.
Locked pairs will show the message `Pair <pair> is currently locked.`.
#### Locking pairs from within the strategy
Sometimes it may be desired to lock a pair after certain events happen (e.g. multiple losing trades in a row).
Freqtrade has an easy method to do this from within the strategy, by calling `self.lock_pair(pair, until)`.
`until` must be a datetime object in the future, after which trading will be reenabled for that pair.
Locks can also be lifted manually, by calling `self.unlock_pair(pair)`.
To verify if a pair is currently locked, use `self.is_pair_locked(pair)`.
!!! Note
Locked pairs are not persisted, so a restart of the bot, or calling `/reload_conf` will reset locked pairs.
!!! Warning
Locking pairs is not functioning during backtesting.
##### Pair locking example
``` python
from freqtrade.persistence import Trade
from datetime import timedelta, datetime, timezone
# Put the above lines a the top of the strategy file, next to all the other imports
# --------
# Within populate indicators (or populate_buy):
if self.config['runmode'] in ('live', 'dry_run'):
# fetch closed trades for the last 2 days
trades = Trade.get_trades([Trade.pair == metadata['pair'],
Trade.open_date > datetime.utcnow() - timedelta(days=2),
Trade.is_open == False,
]).all()
# Analyze the conditions you'd like to lock the pair .... will probably be different for every strategy
sumprofit = sum(trade.close_profit for trade in trades)
if sumprofit < 0:
# Lock pair for 12 hours
self.lock_pair(metadata['pair'], until=datetime.now(timezone.utc) + timedelta(hours=12))
```
### Print created dataframe
To inspect the created dataframe, you can issue a print-statement in either `populate_buy_trend()` or `populate_sell_trend()`.
@ -479,11 +524,6 @@ def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
Printing more than a few rows is also possible (simply use `print(dataframe)` instead of `print(dataframe.tail())`), however not recommended, as that will be very verbose (~500 lines per pair every 5 seconds).
### Where can i find a strategy template?
The strategy template is located in the file
[user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_strategy.py).
### Specify custom strategy location
If you want to use a strategy from a different directory you can pass `--strategy-path`

6
docs/strategy_analysis_example.md
View File

@ -44,9 +44,9 @@ candles.head()
```python
# Load strategy using values set above
from freqtrade.resolvers import StrategyResolver
strategy = StrategyResolver({'strategy': strategy_name,
'user_data_dir': user_data_dir,
'strategy_path': strategy_location}).strategy
strategy = StrategyResolver.load_strategy({'strategy': strategy_name,
'user_data_dir': user_data_dir,
'strategy_path': strategy_location})
# Generate buy/sell signals using strategy
df = strategy.analyze_ticker(candles, {'pair': pair})

41
docs/utils.md
View File

@ -108,6 +108,47 @@ With custom user directory
freqtrade new-hyperopt --userdir ~/.freqtrade/ --hyperopt AwesomeHyperopt
```
## List Strategies
Use the `list-strategies` subcommand to see all strategies in one particular directory.
```
freqtrade list-strategies --help
usage: freqtrade list-strategies [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [--strategy-path PATH] [-1]
optional arguments:
-h, --help show this help message and exit
--strategy-path PATH Specify additional strategy lookup path.
-1, --one-column Print output in one column.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are: 'syslog', 'journald'. See the documentation for more details.
-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.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
!!! Warning
Using this command will try to load all python files from a directory. This can be a security risk if untrusted files reside in this directory, since all module-level code is executed.
Example: search default strategy directory within userdir
``` bash
freqtrade list-strategies --userdir ~/.freqtrade/
```
Example: search dedicated strategy path
``` bash
freqtrade list-strategies --strategy-path ~/.freqtrade/strategies/
```
## List Exchanges
Use the `list-exchanges` subcommand to see the exchanges available for the bot.

2
docs/webhook-config.md
View File

@ -63,6 +63,8 @@ Possible parameters are:
* `fiat_currency`
* `sell_reason`
* `order_type`
* `open_date`
* `close_date`
### Webhookstatus

33
freqtrade/__init__.py
View File

@ -1,5 +1,5 @@
""" FreqTrade bot """
__version__ = '2019.11'
__version__ = '2020.01'
if __version__ == 'develop':
@ -11,34 +11,3 @@ if __version__ == 'develop':
except Exception:
# git not available, ignore
pass
class DependencyException(Exception):
"""
Indicates that an assumed dependency is not met.
This could happen when there is currently not enough money on the account.
"""
class OperationalException(Exception):
"""
Requires manual intervention and will usually stop the bot.
This happens when an exchange returns an unexpected error during runtime
or given configuration is invalid.
"""
class InvalidOrderException(Exception):
"""
This is returned when the order is not valid. Example:
If stoploss on exchange order is hit, then trying to cancel the order
should return this exception.
"""
class TemporaryError(Exception):
"""
Temporary network or exchange related error.
This could happen when an exchange is congested, unavailable, or the user
has networking problems. Usually resolves itself after a time.
"""

25
freqtrade/commands/__init__.py Normal file
View File

@ -0,0 +1,25 @@
# flake8: noqa: F401
"""
Commands module.
Contains all start-commands, subcommands and CLI Interface creation.
Note: Be careful with file-scoped imports in these subfiles.
as they are parsed on startup, nothing containing optional modules should be loaded.
"""
from freqtrade.commands.arguments import Arguments
from freqtrade.commands.data_commands import start_download_data
from freqtrade.commands.deploy_commands import (start_create_userdir,
start_new_hyperopt,
start_new_strategy)
from freqtrade.commands.hyperopt_commands import (start_hyperopt_list,
start_hyperopt_show)
from freqtrade.commands.list_commands import (start_list_exchanges,
start_list_markets,
start_list_strategies,
start_list_timeframes)
from freqtrade.commands.optimize_commands import (start_backtesting,
start_edge, start_hyperopt)
from freqtrade.commands.pairlist_commands import start_test_pairlist
from freqtrade.commands.plot_commands import (start_plot_dataframe,
start_plot_profit)
from freqtrade.commands.trade_commands import start_trading

31
freqtrade/configuration/arguments.py → freqtrade/commands/arguments.py
View File

@ -7,7 +7,7 @@ from pathlib import Path
from typing import Any, Dict, List, Optional
from freqtrade import constants
from freqtrade.configuration.cli_options import AVAILABLE_CLI_OPTIONS
from freqtrade.commands.cli_options import AVAILABLE_CLI_OPTIONS
ARGS_COMMON = ["verbosity", "logfile", "version", "config", "datadir", "user_data_dir"]
@ -30,6 +30,8 @@ ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
ARGS_EDGE = ARGS_COMMON_OPTIMIZE + ["stoploss_range"]
ARGS_LIST_STRATEGIES = ["strategy_path", "print_one_column"]
ARGS_LIST_EXCHANGES = ["print_one_column", "list_exchanges_all"]
ARGS_LIST_TIMEFRAMES = ["exchange", "print_one_column"]
@ -62,7 +64,8 @@ ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperop
"print_json", "hyperopt_show_no_header"]
NO_CONF_REQURIED = ["download-data", "list-timeframes", "list-markets", "list-pairs",
"hyperopt-list", "hyperopt-show", "plot-dataframe", "plot-profit"]
"list-strategies", "hyperopt-list", "hyperopt-show", "plot-dataframe",
"plot-profit"]
NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-hyperopt", "new-strategy"]
@ -127,13 +130,14 @@ class Arguments:
self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot')
self._build_args(optionlist=['version'], parser=self.parser)
from freqtrade.optimize import start_backtesting, start_hyperopt, start_edge
from freqtrade.utils import (start_create_userdir, start_download_data,
start_hyperopt_list, start_hyperopt_show,
start_list_exchanges, start_list_markets,
start_new_hyperopt, start_new_strategy,
start_list_timeframes, start_test_pairlist, start_trading)
from freqtrade.plot.plot_utils import start_plot_dataframe, start_plot_profit
from freqtrade.commands import (start_create_userdir, start_download_data,
start_hyperopt_list, start_hyperopt_show,
start_list_exchanges, start_list_markets,
start_list_strategies, start_new_hyperopt,
start_new_strategy, start_list_timeframes,
start_plot_dataframe, start_plot_profit,
start_backtesting, start_hyperopt, start_edge,
start_test_pairlist, start_trading)
subparsers = self.parser.add_subparsers(dest='command',
# Use custom message when no subhandler is added
@ -185,6 +189,15 @@ class Arguments:
build_hyperopt_cmd.set_defaults(func=start_new_hyperopt)
self._build_args(optionlist=ARGS_BUILD_HYPEROPT, parser=build_hyperopt_cmd)
# Add list-strategies subcommand
list_strategies_cmd = subparsers.add_parser(
'list-strategies',
help='Print available strategies.',
parents=[_common_parser],
)
list_strategies_cmd.set_defaults(func=start_list_strategies)
self._build_args(optionlist=ARGS_LIST_STRATEGIES, parser=list_strategies_cmd)
# Add list-exchanges subcommand
list_exchanges_cmd = subparsers.add_parser(
'list-exchanges',

20
freqtrade/configuration/cli_options.py → freqtrade/commands/cli_options.py
View File

@ -1,7 +1,7 @@
"""
Definition of cli arguments used in arguments.py
"""
import argparse
from argparse import ArgumentTypeError
from freqtrade import __version__, constants
@ -12,7 +12,7 @@ def check_int_positive(value: str) -> int:
if uint <= 0:
raise ValueError
except ValueError:
raise argparse.ArgumentTypeError(
raise ArgumentTypeError(
f"{value} is invalid for this parameter, should be a positive integer value"
)
return uint
@ -24,7 +24,7 @@ def check_int_nonzero(value: str) -> int:
if uint == 0:
raise ValueError
except ValueError:
raise argparse.ArgumentTypeError(
raise ArgumentTypeError(
f"{value} is invalid for this parameter, should be a non-zero integer value"
)
return uint
@ -118,14 +118,14 @@ AVAILABLE_CLI_OPTIONS = {
help='Specify what timerange of data to use.',
),
"max_open_trades": Arg(
'--max_open_trades',
help='Specify max_open_trades to use.',
'--max-open-trades',
help='Override the value of the `max_open_trades` configuration setting.',
type=int,
metavar='INT',
),
"stake_amount": Arg(
'--stake_amount',
help='Specify stake_amount.',
'--stake-amount',
help='Override the value of the `stake_amount` configuration setting.',
type=float,
),
# Backtesting
@ -363,15 +363,13 @@ AVAILABLE_CLI_OPTIONS = {
"indicators1": Arg(
'--indicators1',
help='Set indicators from your strategy you want in the first row of the graph. '
'Space-separated list. Example: `ema3 ema5`. Default: `%(default)s`.',
default=['sma', 'ema3', 'ema5'],
"Space-separated list. Example: `ema3 ema5`. Default: `['sma', 'ema3', 'ema5']`.",
nargs='+',
),
"indicators2": Arg(
'--indicators2',
help='Set indicators from your strategy you want in the third row of the graph. '
'Space-separated list. Example: `fastd fastk`. Default: `%(default)s`.',
default=['macd', 'macdsignal'],
"Space-separated list. Example: `fastd fastk`. Default: `['macd', 'macdsignal']`.",
nargs='+',
),
"plot_limit": Arg(

63
freqtrade/commands/data_commands.py Normal file
View File

@ -0,0 +1,63 @@
import logging
import sys
from typing import Any, Dict, List
import arrow
from freqtrade.configuration import TimeRange, setup_utils_configuration
from freqtrade.data.history import (convert_trades_to_ohlcv,
refresh_backtest_ohlcv_data,
refresh_backtest_trades_data)
from freqtrade.exceptions import OperationalException
from freqtrade.resolvers import ExchangeResolver
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
def start_download_data(args: Dict[str, Any]) -> None:
"""
Download data (former download_backtest_data.py script)
"""
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
timerange = TimeRange()
if 'days' in config:
time_since = arrow.utcnow().shift(days=-config['days']).strftime("%Y%m%d")
timerange = TimeRange.parse_timerange(f'{time_since}-')
if 'pairs' not in config:
raise OperationalException(
"Downloading data requires a list of pairs. "
"Please check the documentation on how to configure this.")
logger.info(f'About to download pairs: {config["pairs"]}, '
f'intervals: {config["timeframes"]} to {config["datadir"]}')
pairs_not_available: List[str] = []
# Init exchange
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config)
try:
if config.get('download_trades'):
pairs_not_available = refresh_backtest_trades_data(
exchange, pairs=config["pairs"], datadir=config['datadir'],
timerange=timerange, erase=config.get("erase"))
# Convert downloaded trade data to different timeframes
convert_trades_to_ohlcv(
pairs=config["pairs"], timeframes=config["timeframes"],
datadir=config['datadir'], timerange=timerange, erase=config.get("erase"))
else:
pairs_not_available = refresh_backtest_ohlcv_data(
exchange, pairs=config["pairs"], timeframes=config["timeframes"],
datadir=config['datadir'], timerange=timerange, erase=config.get("erase"))
except KeyboardInterrupt:
sys.exit("SIGINT received, aborting ...")
finally:
if pairs_not_available:
logger.info(f"Pairs [{','.join(pairs_not_available)}] not available "
f"on exchange {exchange.name}.")

112
freqtrade/commands/deploy_commands.py Normal file
View File

@ -0,0 +1,112 @@
import logging
import sys
from pathlib import Path
from typing import Any, Dict
from freqtrade.configuration import setup_utils_configuration
from freqtrade.configuration.directory_operations import (copy_sample_files,
create_userdata_dir)
from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGY
from freqtrade.exceptions import OperationalException
from freqtrade.misc import render_template
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
def start_create_userdir(args: Dict[str, Any]) -> None:
"""
Create "user_data" directory to contain user data strategies, hyperopt, ...)
:param args: Cli args from Arguments()
:return: None
"""
if "user_data_dir" in args and args["user_data_dir"]:
userdir = create_userdata_dir(args["user_data_dir"], create_dir=True)
copy_sample_files(userdir, overwrite=args["reset"])
else:
logger.warning("`create-userdir` requires --userdir to be set.")
sys.exit(1)
def deploy_new_strategy(strategy_name, strategy_path: Path, subtemplate: str):
"""
Deploy new strategy from template to strategy_path
"""
indicators = render_template(templatefile=f"subtemplates/indicators_{subtemplate}.j2",)
buy_trend = render_template(templatefile=f"subtemplates/buy_trend_{subtemplate}.j2",)
sell_trend = render_template(templatefile=f"subtemplates/sell_trend_{subtemplate}.j2",)
plot_config = render_template(templatefile=f"subtemplates/plot_config_{subtemplate}.j2",)
strategy_text = render_template(templatefile='base_strategy.py.j2',
arguments={"strategy": strategy_name,
"indicators": indicators,
"buy_trend": buy_trend,
"sell_trend": sell_trend,
"plot_config": plot_config,
})
logger.info(f"Writing strategy to `{strategy_path}`.")
strategy_path.write_text(strategy_text)
def start_new_strategy(args: Dict[str, Any]) -> None:
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
if "strategy" in args and args["strategy"]:
if args["strategy"] == "DefaultStrategy":
raise OperationalException("DefaultStrategy is not allowed as name.")
new_path = config['user_data_dir'] / USERPATH_STRATEGY / (args["strategy"] + ".py")
if new_path.exists():
raise OperationalException(f"`{new_path}` already exists. "
"Please choose another Strategy Name.")
deploy_new_strategy(args['strategy'], new_path, args['template'])
else:
raise OperationalException("`new-strategy` requires --strategy to be set.")
def deploy_new_hyperopt(hyperopt_name, hyperopt_path: Path, subtemplate: str):
"""
Deploys a new hyperopt template to hyperopt_path
"""
buy_guards = render_template(
templatefile=f"subtemplates/hyperopt_buy_guards_{subtemplate}.j2",)
sell_guards = render_template(
templatefile=f"subtemplates/hyperopt_sell_guards_{subtemplate}.j2",)
buy_space = render_template(
templatefile=f"subtemplates/hyperopt_buy_space_{subtemplate}.j2",)
sell_space = render_template(
templatefile=f"subtemplates/hyperopt_sell_space_{subtemplate}.j2",)
strategy_text = render_template(templatefile='base_hyperopt.py.j2',
arguments={"hyperopt": hyperopt_name,
"buy_guards": buy_guards,
"sell_guards": sell_guards,
"buy_space": buy_space,
"sell_space": sell_space,
})
logger.info(f"Writing hyperopt to `{hyperopt_path}`.")
hyperopt_path.write_text(strategy_text)
def start_new_hyperopt(args: Dict[str, Any]) -> None:
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
if "hyperopt" in args and args["hyperopt"]:
if args["hyperopt"] == "DefaultHyperopt":
raise OperationalException("DefaultHyperopt is not allowed as name.")
new_path = config['user_data_dir'] / USERPATH_HYPEROPTS / (args["hyperopt"] + ".py")
if new_path.exists():
raise OperationalException(f"`{new_path}` already exists. "
"Please choose another Strategy Name.")
deploy_new_hyperopt(args['hyperopt'], new_path, args['template'])
else:
raise OperationalException("`new-hyperopt` requires --hyperopt to be set.")

114
freqtrade/commands/hyperopt_commands.py Normal file
View File

@ -0,0 +1,114 @@
import logging
from operator import itemgetter
from typing import Any, Dict, List
from colorama import init as colorama_init
from freqtrade.configuration import setup_utils_configuration
from freqtrade.exceptions import OperationalException
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
def start_hyperopt_list(args: Dict[str, Any]) -> None:
"""
List hyperopt epochs previously evaluated
"""
from freqtrade.optimize.hyperopt import Hyperopt
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
only_best = config.get('hyperopt_list_best', False)
only_profitable = config.get('hyperopt_list_profitable', False)
print_colorized = config.get('print_colorized', False)
print_json = config.get('print_json', False)
no_details = config.get('hyperopt_list_no_details', False)
no_header = False
trials_file = (config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_results.pickle')
# Previous evaluations
trials = Hyperopt.load_previous_results(trials_file)
total_epochs = len(trials)
trials = _hyperopt_filter_trials(trials, only_best, only_profitable)
# 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)
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 only_best, print_colorized)
except KeyboardInterrupt:
print('User interrupted..')
if trials and not no_details:
sorted_trials = sorted(trials, key=itemgetter('loss'))
results = sorted_trials[0]
Hyperopt.print_epoch_details(results, total_epochs, print_json, no_header)
def start_hyperopt_show(args: Dict[str, Any]) -> None:
"""
Show details of a hyperopt epoch previously evaluated
"""
from freqtrade.optimize.hyperopt import Hyperopt
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
only_best = config.get('hyperopt_list_best', False)
only_profitable = config.get('hyperopt_list_profitable', False)
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)
total_epochs = len(trials)
trials = _hyperopt_filter_trials(trials, only_best, only_profitable)
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}.")
if n < -trials_epochs:
raise OperationalException(
f"The index of the epoch to show should be greater than {-trials_epochs - 1}.")
# Translate epoch index from human-readable format to pythonic
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,
header_str="Epoch details")
def _hyperopt_filter_trials(trials: List, only_best: bool, only_profitable: bool) -> List:
"""
Filter our items from the list of hyperopt results
"""
if only_best:
trials = [x for x in trials if x['is_best']]
if only_profitable:
trials = [x for x in trials if x['results_metrics']['profit'] > 0]
logger.info(f"{len(trials)} " +
("best " if only_best else "") +
("profitable " if only_profitable else "") +
"epochs found.")
return trials

156
freqtrade/commands/list_commands.py Normal file
View File

@ -0,0 +1,156 @@
import csv
import logging
import sys
from collections import OrderedDict
from pathlib import Path
from typing import Any, Dict
import rapidjson
from tabulate import tabulate
from freqtrade.configuration import setup_utils_configuration
from freqtrade.constants import USERPATH_STRATEGY
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import (available_exchanges, ccxt_exchanges,
market_is_active, symbol_is_pair)
from freqtrade.misc import plural
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
def start_list_exchanges(args: Dict[str, Any]) -> None:
"""
Print available exchanges
:param args: Cli args from Arguments()
:return: None
"""
exchanges = ccxt_exchanges() if args['list_exchanges_all'] else available_exchanges()
if args['print_one_column']:
print('\n'.join(exchanges))
else:
if args['list_exchanges_all']:
print(f"All exchanges supported by the ccxt library: {', '.join(exchanges)}")
else:
print(f"Exchanges available for Freqtrade: {', '.join(exchanges)}")
def start_list_strategies(args: Dict[str, Any]) -> None:
"""
Print Strategies available in a directory
"""
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
directory = Path(config.get('strategy_path', config['user_data_dir'] / USERPATH_STRATEGY))
strategies = StrategyResolver.search_all_objects(directory)
# Sort alphabetically
strategies = sorted(strategies, key=lambda x: x['name'])
strats_to_print = [{'name': s['name'], 'location': s['location'].name} for s in strategies]
if args['print_one_column']:
print('\n'.join([s['name'] for s in strategies]))
else:
print(tabulate(strats_to_print, headers='keys', tablefmt='pipe'))
def start_list_timeframes(args: Dict[str, Any]) -> None:
"""
Print ticker intervals (timeframes) available on Exchange
"""
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
# Do not use ticker_interval set in the config
config['ticker_interval'] = None
# Init exchange
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
if args['print_one_column']:
print('\n'.join(exchange.timeframes))
else:
print(f"Timeframes available for the exchange `{exchange.name}`: "
f"{', '.join(exchange.timeframes)}")
def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
"""
Print pairs/markets on the exchange
:param args: Cli args from Arguments()
:param pairs_only: if True print only pairs, otherwise print all instruments (markets)
:return: None
"""
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
# Init exchange
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
# By default only active pairs/markets are to be shown
active_only = not args.get('list_pairs_all', False)
base_currencies = args.get('base_currencies', [])
quote_currencies = args.get('quote_currencies', [])
try:
pairs = exchange.get_markets(base_currencies=base_currencies,
quote_currencies=quote_currencies,
pairs_only=pairs_only,
active_only=active_only)
# Sort the pairs/markets by symbol
pairs = OrderedDict(sorted(pairs.items()))
except Exception as e:
raise OperationalException(f"Cannot get markets. Reason: {e}") from e
else:
summary_str = ((f"Exchange {exchange.name} has {len(pairs)} ") +
("active " if active_only else "") +
(plural(len(pairs), "pair" if pairs_only else "market")) +
(f" with {', '.join(base_currencies)} as base "
f"{plural(len(base_currencies), 'currency', 'currencies')}"
if base_currencies else "") +
(" and" if base_currencies and quote_currencies else "") +
(f" with {', '.join(quote_currencies)} as quote "
f"{plural(len(quote_currencies), 'currency', 'currencies')}"
if quote_currencies else ""))
headers = ["Id", "Symbol", "Base", "Quote", "Active",
*(['Is pair'] if not pairs_only else [])]
tabular_data = []
for _, v in pairs.items():
tabular_data.append({'Id': v['id'], 'Symbol': v['symbol'],
'Base': v['base'], 'Quote': v['quote'],
'Active': market_is_active(v),
**({'Is pair': symbol_is_pair(v['symbol'])}
if not pairs_only else {})})
if (args.get('print_one_column', False) or
args.get('list_pairs_print_json', False) or
args.get('print_csv', False)):
# Print summary string in the log in case of machine-readable
# regular formats.
logger.info(f"{summary_str}.")
else:
# Print empty string separating leading logs and output in case of
# human-readable formats.
print()
if len(pairs):
if args.get('print_list', False):
# print data as a list, with human-readable summary
print(f"{summary_str}: {', '.join(pairs.keys())}.")
elif args.get('print_one_column', False):
print('\n'.join(pairs.keys()))
elif args.get('list_pairs_print_json', False):
print(rapidjson.dumps(list(pairs.keys()), default=str))
elif args.get('print_csv', False):
writer = csv.DictWriter(sys.stdout, fieldnames=headers)
writer.writeheader()
writer.writerows(tabular_data)
else:
# print data as a table, with the human-readable summary
print(f"{summary_str}:")
print(tabulate(tabular_data, headers='keys', tablefmt='pipe'))
elif not (args.get('print_one_column', False) or
args.get('list_pairs_print_json', False) or
args.get('print_csv', False)):
print(f"{summary_str}.")

102
freqtrade/commands/optimize_commands.py Normal file
View File

@ -0,0 +1,102 @@
import logging
from typing import Any, Dict
from freqtrade import constants
from freqtrade.configuration import setup_utils_configuration
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
def setup_optimize_configuration(args: Dict[str, Any], method: RunMode) -> Dict[str, Any]:
"""
Prepare the configuration for the Hyperopt module
:param args: Cli args from Arguments()
:return: Configuration
"""
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)
return config
def start_backtesting(args: Dict[str, Any]) -> None:
"""
Start Backtesting script
:param args: Cli args from Arguments()
:return: None
"""
# Import here to avoid loading backtesting module when it's not used
from freqtrade.optimize.backtesting import Backtesting
# Initialize configuration
config = setup_optimize_configuration(args, RunMode.BACKTEST)
logger.info('Starting freqtrade in Backtesting mode')
# Initialize backtesting object
backtesting = Backtesting(config)
backtesting.start()
def start_hyperopt(args: Dict[str, Any]) -> None:
"""
Start hyperopt script
:param args: Cli args from Arguments()
:return: None
"""
# Import here to avoid loading hyperopt module when it's not used
try:
from filelock import FileLock, Timeout
from freqtrade.optimize.hyperopt import Hyperopt
except ImportError as e:
raise OperationalException(
f"{e}. Please ensure that the hyperopt dependencies are installed.") from e
# Initialize configuration
config = setup_optimize_configuration(args, RunMode.HYPEROPT)
logger.info('Starting freqtrade in Hyperopt mode')
lock = FileLock(Hyperopt.get_lock_filename(config))
try:
with lock.acquire(timeout=1):
# Remove noisy log messages
logging.getLogger('hyperopt.tpe').setLevel(logging.WARNING)
logging.getLogger('filelock').setLevel(logging.WARNING)
# Initialize backtesting object
hyperopt = Hyperopt(config)
hyperopt.start()
except Timeout:
logger.info("Another running instance of freqtrade Hyperopt detected.")
logger.info("Simultaneous execution of multiple Hyperopt commands is not supported. "
"Hyperopt module is resource hungry. Please run your Hyperopt sequentially "
"or on separate machines.")
logger.info("Quitting now.")
# TODO: return False here in order to help freqtrade to exit
# with non-zero exit code...
# Same in Edge and Backtesting start() functions.
def start_edge(args: Dict[str, Any]) -> None:
"""
Start Edge script
:param args: Cli args from Arguments()
:return: None
"""
from freqtrade.optimize.edge_cli import EdgeCli
# Initialize configuration
config = setup_optimize_configuration(args, RunMode.EDGE)
logger.info('Starting freqtrade in Edge mode')
# Initialize Edge object
edge_cli = EdgeCli(config)
edge_cli.start()

42
freqtrade/commands/pairlist_commands.py Normal file
View File

@ -0,0 +1,42 @@
import logging
from typing import Any, Dict
import rapidjson
from freqtrade.configuration import setup_utils_configuration
from freqtrade.resolvers import ExchangeResolver
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
def start_test_pairlist(args: Dict[str, Any]) -> None:
"""
Test Pairlist configuration
"""
from freqtrade.pairlist.pairlistmanager import PairListManager
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
quote_currencies = args.get('quote_currencies')
if not quote_currencies:
quote_currencies = [config.get('stake_currency')]
results = {}
for curr in quote_currencies:
config['stake_currency'] = curr
# Do not use ticker_interval set in the config
pairlists = PairListManager(exchange, config)
pairlists.refresh_pairlist()
results[curr] = pairlists.whitelist
for curr, pairlist in results.items():
if not args.get('print_one_column', False):
print(f"Pairs for {curr}: ")
if args.get('print_one_column', False):
print('\n'.join(pairlist))
elif args.get('list_pairs_print_json', False):
print(rapidjson.dumps(list(pairlist), default=str))
else:
print(pairlist)

4
freqtrade/plot/plot_utils.py → freqtrade/commands/plot_commands.py
View File

@ -1,8 +1,8 @@
from typing import Any, Dict
from freqtrade import OperationalException
from freqtrade.configuration import setup_utils_configuration
from freqtrade.exceptions import OperationalException
from freqtrade.state import RunMode
from freqtrade.utils import setup_utils_configuration
def validate_plot_args(args: Dict[str, Any]):

27
freqtrade/commands/trade_commands.py Normal file
View File

@ -0,0 +1,27 @@
import logging
from typing import Any, Dict
logger = logging.getLogger(__name__)
def start_trading(args: Dict[str, Any]) -> int:
"""
Main entry point for trading mode
"""
# Import here to avoid loading worker module when it's not used
from freqtrade.worker import Worker
# Create and run worker
worker = None
try:
worker = Worker(args)
worker.run()
except KeyboardInterrupt:
logger.info('SIGINT received, aborting ...')
finally:
if worker:
logger.info("worker found ... calling exit")
worker.exit()
return 0

12
freqtrade/configuration/__init__.py
View File

@ -1,5 +1,7 @@
from freqtrade.configuration.arguments import Arguments # noqa: F401
from freqtrade.configuration.check_exchange import check_exchange, remove_credentials # noqa: F401
from freqtrade.configuration.timerange import TimeRange # noqa: F401
from freqtrade.configuration.configuration import Configuration # noqa: F401
from freqtrade.configuration.config_validation import validate_config_consistency # noqa: F401
# flake8: noqa: F401
from freqtrade.configuration.config_setup import setup_utils_configuration
from freqtrade.configuration.check_exchange import check_exchange, remove_credentials
from freqtrade.configuration.timerange import TimeRange
from freqtrade.configuration.configuration import Configuration
from freqtrade.configuration.config_validation import validate_config_consistency

4
freqtrade/configuration/check_exchange.py
View File

@ -1,9 +1,9 @@
import logging
from typing import Any, Dict
from freqtrade import OperationalException
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import (available_exchanges, get_exchange_bad_reason,
is_exchange_known_ccxt, is_exchange_bad,
is_exchange_bad, is_exchange_known_ccxt,
is_exchange_officially_supported)
from freqtrade.state import RunMode

25
freqtrade/configuration/config_setup.py Normal file
View File

@ -0,0 +1,25 @@
import logging
from typing import Any, Dict
from .config_validation import validate_config_consistency
from .configuration import Configuration
from .check_exchange import remove_credentials
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
def setup_utils_configuration(args: Dict[str, Any], method: RunMode) -> Dict[str, Any]:
"""
Prepare the configuration for utils subcommands
:param args: Cli args from Arguments()
:return: Configuration
"""
configuration = Configuration(args, method)
config = configuration.get_config()
# Ensure we do not use Exchange credentials
remove_credentials(config)
validate_config_consistency(config)
return config

25
freqtrade/configuration/config_validation.py
View File

@ -1,10 +1,12 @@
import logging
from copy import deepcopy
from typing import Any, Dict
from jsonschema import Draft4Validator, validators
from jsonschema.exceptions import ValidationError, best_match
from freqtrade import constants, OperationalException
from freqtrade import constants
from freqtrade.exceptions import OperationalException
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
@ -41,15 +43,20 @@ def validate_config_schema(conf: Dict[str, Any]) -> Dict[str, Any]:
:param conf: Config in JSON format
:return: Returns the config if valid, otherwise throw an exception
"""
conf_schema = deepcopy(constants.CONF_SCHEMA)
if conf.get('runmode', RunMode.OTHER) in (RunMode.DRY_RUN, RunMode.LIVE):
conf_schema['required'] = constants.SCHEMA_TRADE_REQUIRED
else:
conf_schema['required'] = constants.SCHEMA_MINIMAL_REQUIRED
try:
FreqtradeValidator(constants.CONF_SCHEMA).validate(conf)
FreqtradeValidator(conf_schema).validate(conf)
return conf
except ValidationError as e:
logger.critical(
f"Invalid configuration. See config.json.example. Reason: {e}"
)
raise ValidationError(
best_match(Draft4Validator(constants.CONF_SCHEMA).iter_errors(conf)).message
best_match(Draft4Validator(conf_schema).iter_errors(conf)).message
)
@ -66,12 +73,24 @@ def validate_config_consistency(conf: Dict[str, Any]) -> None:
_validate_trailing_stoploss(conf)
_validate_edge(conf)
_validate_whitelist(conf)
_validate_unlimited_amount(conf)
# validate configuration before returning
logger.info('Validating configuration ...')
validate_config_schema(conf)
def _validate_unlimited_amount(conf: Dict[str, Any]) -> None:
"""
If edge is disabled, either max_open_trades or stake_amount need to be set.
:raise: OperationalException if config validation failed
"""
if (not conf.get('edge', {}).get('enabled')
and conf.get('max_open_trades') == float('inf')
and conf.get('stake_amount') == constants.UNLIMITED_STAKE_AMOUNT):
raise OperationalException("`max_open_trades` and `stake_amount` cannot both be unlimited.")
def _validate_trailing_stoploss(conf: Dict[str, Any]) -> None:
if conf.get('stoploss') == 0.0:

11
freqtrade/configuration/configuration.py
View File

@ -7,15 +7,16 @@ from copy import deepcopy
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional
from freqtrade import OperationalException, constants
from freqtrade import constants
from freqtrade.configuration.check_exchange import check_exchange
from freqtrade.configuration.deprecated_settings import process_temporary_deprecated_settings
from freqtrade.configuration.directory_operations import (create_datadir,
create_userdata_dir)
from freqtrade.configuration.load_config import load_config_file
from freqtrade.exceptions import OperationalException
from freqtrade.loggers import setup_logging
from freqtrade.misc import deep_merge_dicts, json_load
from freqtrade.state import RunMode, TRADING_MODES, NON_UTIL_MODES
from freqtrade.state import NON_UTIL_MODES, TRADING_MODES, RunMode
logger = logging.getLogger(__name__)
@ -223,13 +224,13 @@ class Configuration:
logger.info('max_open_trades set to unlimited ...')
elif 'max_open_trades' in self.args and self.args["max_open_trades"]:
config.update({'max_open_trades': self.args["max_open_trades"]})
logger.info('Parameter --max_open_trades detected, '
logger.info('Parameter --max-open-trades detected, '
'overriding max_open_trades to: %s ...', config.get('max_open_trades'))
elif config['runmode'] in NON_UTIL_MODES:
logger.info('Using max_open_trades: %s ...', config.get('max_open_trades'))
self._args_to_config(config, argname='stake_amount',
logstring='Parameter --stake_amount detected, '
logstring='Parameter --stake-amount detected, '
'overriding stake_amount to: {} ...')
self._args_to_config(config, argname='fee',
@ -403,7 +404,7 @@ class Configuration:
config['pairs'] = config.get('exchange', {}).get('pair_whitelist')
else:
# Fall back to /dl_path/pairs.json
pairs_file = Path(config['datadir']) / "pairs.json"
pairs_file = config['datadir'] / "pairs.json"
if pairs_file.exists():
with pairs_file.open('r') as f:
config['pairs'] = json_load(f)

12
freqtrade/configuration/deprecated_settings.py
View File

@ -5,7 +5,7 @@ Functions to handle deprecated settings
import logging
from typing import Any, Dict
from freqtrade import OperationalException
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
@ -80,3 +80,13 @@ def process_temporary_deprecated_settings(config: Dict[str, Any]) -> None:
f"Using precision_filter setting is deprecated and has been replaced by"
"PrecisionFilter. Please refer to the docs on configuration details")
config['pairlists'].append({'method': 'PrecisionFilter'})
if (config.get('edge', {}).get('enabled', False)
and 'capital_available_percentage' in config.get('edge', {})):
logger.warning(
"DEPRECATED: "
"Using 'edge.capital_available_percentage' has been deprecated in favor of "
"'tradable_balance_ratio'. Please migrate your configuration to "
"'tradable_balance_ratio' and remove 'capital_available_percentage' "
"from the edge configuration."
)

6
freqtrade/configuration/directory_operations.py
View File

@ -3,13 +3,13 @@ import shutil
from pathlib import Path
from typing import Any, Dict, Optional
from freqtrade import OperationalException
from freqtrade.exceptions import OperationalException
from freqtrade.constants import USER_DATA_FILES
logger = logging.getLogger(__name__)
def create_datadir(config: Dict[str, Any], datadir: Optional[str] = None) -> str:
def create_datadir(config: Dict[str, Any], datadir: Optional[str] = None) -> Path:
folder = Path(datadir) if datadir else Path(f"{config['user_data_dir']}/data")
if not datadir:
@ -20,7 +20,7 @@ def create_datadir(config: Dict[str, Any], datadir: Optional[str] = None) -> str
if not folder.is_dir():
folder.mkdir(parents=True)
logger.info(f'Created data directory: {datadir}')
return str(folder)
return folder
def create_userdata_dir(directory: str, create_dir=False) -> Path:

2
freqtrade/configuration/load_config.py
View File

@ -6,7 +6,7 @@ import logging
import sys
from typing import Any, Dict
from freqtrade import OperationalException
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)

59
freqtrade/constants.py
View File

@ -10,7 +10,7 @@ HYPEROPT_EPOCH = 100 # epochs
RETRY_TIMEOUT = 30 # sec
DEFAULT_HYPEROPT_LOSS = 'DefaultHyperOptLoss'
DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite'
DEFAULT_DB_DRYRUN_URL = 'sqlite://'
DEFAULT_DB_DRYRUN_URL = 'sqlite:///tradesv3.dryrun.sqlite'
UNLIMITED_STAKE_AMOUNT = 'unlimited'
DEFAULT_AMOUNT_RESERVE_PERCENT = 0.05
REQUIRED_ORDERTIF = ['buy', 'sell']
@ -18,7 +18,7 @@ REQUIRED_ORDERTYPES = ['buy', 'sell', 'stoploss', 'stoploss_on_exchange']
ORDERTYPE_POSSIBILITIES = ['limit', 'market']
ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', 'PrecisionFilter', 'PriceFilter']
DRY_RUN_WALLET = 999.9
DRY_RUN_WALLET = 1000
MATH_CLOSE_PREC = 1e-14 # Precision used for float comparisons
USERPATH_HYPEROPTS = 'hyperopts'
@ -33,12 +33,6 @@ USER_DATA_FILES = {
'strategy_analysis_example.ipynb': 'notebooks',
}
TIMEFRAMES = [
'1m', '3m', '5m', '15m', '30m',
'1h', '2h', '4h', '6h', '8h', '12h',
'1d', '3d', '1w',
]
SUPPORTED_FIAT = [
"AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK", "DKK",
"EUR", "GBP", "HKD", "HUF", "IDR", "ILS", "INR", "JPY",
@ -66,16 +60,26 @@ CONF_SCHEMA = {
'type': 'object',
'properties': {
'max_open_trades': {'type': ['integer', 'number'], 'minimum': -1},
'ticker_interval': {'type': 'string', 'enum': TIMEFRAMES},
'stake_currency': {'type': 'string', 'enum': ['BTC', 'XBT', 'ETH', 'USDT', 'EUR', 'USD']},
'ticker_interval': {'type': 'string'},
'stake_currency': {'type': 'string'},
'stake_amount': {
'type': ['number', 'string'],
'minimum': 0.0001,
'pattern': UNLIMITED_STAKE_AMOUNT
},
'tradable_balance_ratio': {
'type': 'number',
'minimum': 0.1,
'maximum': 1,
'default': 0.99
},
'amend_last_stake_amount': {'type': 'boolean', 'default': False},
'last_stake_amount_min_ratio': {
'type': 'number', 'minimum': 0.0, 'maximum': 1.0, 'default': 0.5
},
'fiat_display_currency': {'type': 'string', 'enum': SUPPORTED_FIAT},
'dry_run': {'type': 'boolean'},
'dry_run_wallet': {'type': 'number'},
'dry_run_wallet': {'type': 'number', 'default': DRY_RUN_WALLET},
'process_only_new_candles': {'type': 'boolean'},
'minimal_roi': {
'type': 'object',
@ -266,18 +270,27 @@ CONF_SCHEMA = {
'max_trade_duration_minute': {'type': 'integer'},
'remove_pumps': {'type': 'boolean'}
},
'required': ['process_throttle_secs', 'allowed_risk', 'capital_available_percentage']
'required': ['process_throttle_secs', 'allowed_risk']
}
},
'required': [
'exchange',
'max_open_trades',
'stake_currency',
'stake_amount',
'dry_run',
'bid_strategy',
'unfilledtimeout',
'stoploss',
'minimal_roi',
]
}
SCHEMA_TRADE_REQUIRED = [
'exchange',
'max_open_trades',
'stake_currency',
'stake_amount',
'tradable_balance_ratio',
'last_stake_amount_min_ratio',
'dry_run',
'dry_run_wallet',
'bid_strategy',
'unfilledtimeout',
'stoploss',
'minimal_roi',
]
SCHEMA_MINIMAL_REQUIRED = [
'exchange',
'dry_run',
]

4
freqtrade/data/btanalysis.py
View File

@ -47,7 +47,7 @@ def load_backtest_data(filename) -> pd.DataFrame:
utc=True,
infer_datetime_format=True
)
df['profitabs'] = df['close_rate'] - df['open_rate']
df['profit'] = df['close_rate'] - df['open_rate']
df = df.sort_values("open_time").reset_index(drop=True)
return df
@ -108,7 +108,7 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
trades = pd.DataFrame([(t.pair,
t.open_date.replace(tzinfo=timezone.utc),
t.close_date.replace(tzinfo=timezone.utc) if t.close_date else None,
t.calc_profit(), t.calc_profit_percent(),
t.calc_profit(), t.calc_profit_ratio(),
t.open_rate, t.close_rate, t.amount,
(round((t.close_date.timestamp() - t.open_date.timestamp()) / 60, 2)
if t.close_date else None),

3
freqtrade/data/dataprovider.py
View File

@ -5,7 +5,6 @@ including Klines, tickers, historic data
Common Interface for bot and strategy to access data.
"""
import logging
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
from pandas import DataFrame
@ -65,7 +64,7 @@ class DataProvider:
"""
return load_pair_history(pair=pair,
timeframe=timeframe or self._config['ticker_interval'],
datadir=Path(self._config['datadir'])
datadir=self._config['datadir']
)
def get_pair_dataframe(self, pair: str, timeframe: str = None) -> DataFrame:

137
freqtrade/data/history.py
View File

@ -16,10 +16,12 @@ from typing import Any, Dict, List, Optional, Tuple
import arrow
from pandas import DataFrame
from freqtrade import OperationalException, misc
from freqtrade import misc
from freqtrade.configuration import TimeRange
from freqtrade.data.converter import parse_ticker_dataframe, trades_to_ohlcv
from freqtrade.exchange import Exchange, timeframe_to_minutes, timeframe_to_seconds
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import (Exchange, timeframe_to_minutes,
timeframe_to_seconds)
logger = logging.getLogger(__name__)
@ -68,7 +70,7 @@ def trim_dataframe(df: DataFrame, timerange: TimeRange, df_date_col: str = 'date
def load_tickerdata_file(datadir: Path, pair: str, timeframe: str,
timerange: Optional[TimeRange] = None) -> Optional[list]:
timerange: Optional[TimeRange] = None) -> List[Dict]:
"""
Load a pair from file, either .json.gz or .json
:return: tickerlist or None if unsuccessful
@ -128,39 +130,26 @@ def load_pair_history(pair: str,
timeframe: str,
datadir: Path,
timerange: Optional[TimeRange] = None,
refresh_pairs: bool = False,
exchange: Optional[Exchange] = None,
fill_up_missing: bool = True,
drop_incomplete: bool = True,
startup_candles: int = 0,
) -> DataFrame:
"""
Loads cached ticker history for the given pair.
Load cached ticker history for the given pair.
:param pair: Pair to load data for
:param timeframe: Ticker timeframe (e.g. "5m")
:param datadir: Path to the data storage location.
:param timerange: Limit data to be loaded to this timerange
:param refresh_pairs: Refresh pairs from exchange.
(Note: Requires exchange to be passed as well.)
:param exchange: Exchange object (needed when using "refresh_pairs")
:param fill_up_missing: Fill missing values with "No action"-candles
:param drop_incomplete: Drop last candle assuming it may be incomplete.
:param startup_candles: Additional candles to load at the start of the period
:return: DataFrame with ohlcv data, or empty DataFrame
"""
timerange_startup = deepcopy(timerange)
if startup_candles > 0 and timerange_startup:
timerange_startup.subtract_start(timeframe_to_seconds(timeframe) * startup_candles)
# The user forced the refresh of pairs
if refresh_pairs:
download_pair_history(datadir=datadir,
exchange=exchange,
pair=pair,
timeframe=timeframe,
timerange=timerange)
pairdata = load_tickerdata_file(datadir, pair, timeframe, timerange=timerange_startup)
if pairdata:
@ -180,30 +169,22 @@ def load_pair_history(pair: str,
def load_data(datadir: Path,
timeframe: str,
pairs: List[str],
refresh_pairs: bool = False,
exchange: Optional[Exchange] = None,
timerange: Optional[TimeRange] = None,
fill_up_missing: bool = True,
startup_candles: int = 0,
fail_without_data: bool = False
) -> Dict[str, DataFrame]:
"""
Loads ticker history data for a list of pairs
Load ticker history data for a list of pairs.
:param datadir: Path to the data storage location.
:param timeframe: Ticker Timeframe (e.g. "5m")
:param pairs: List of pairs to load
:param refresh_pairs: Refresh pairs from exchange.
(Note: Requires exchange to be passed as well.)
:param exchange: Exchange object (needed when using "refresh_pairs")
:param timerange: Limit data to be loaded to this timerange
:param fill_up_missing: Fill missing values with "No action"-candles
:param startup_candles: Additional candles to load at the start of the period
:param fail_without_data: Raise OperationalException if no data is found.
:return: dict(<pair>:<Dataframe>)
TODO: refresh_pairs is still used by edge to keep the data uptodate.
This should be replaced in the future. Instead, writing the current candles to disk
from dataprovider should be implemented, as this would avoid loading ohlcv data twice.
exchange and refresh_pairs are then not needed here nor in load_pair_history.
"""
result: Dict[str, DataFrame] = {}
if startup_candles > 0 and timerange:
@ -212,8 +193,6 @@ def load_data(datadir: Path,
for pair in pairs:
hist = load_pair_history(pair=pair, timeframe=timeframe,
datadir=datadir, timerange=timerange,
refresh_pairs=refresh_pairs,
exchange=exchange,
fill_up_missing=fill_up_missing,
startup_candles=startup_candles)
if not hist.empty:
@ -224,6 +203,27 @@ def load_data(datadir: Path,
return result
def refresh_data(datadir: Path,
timeframe: str,
pairs: List[str],
exchange: Exchange,
timerange: Optional[TimeRange] = None,
) -> None:
"""
Refresh ticker history data for a list of pairs.
:param datadir: Path to the data storage location.
:param timeframe: Ticker 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
"""
for pair in pairs:
_download_pair_history(pair=pair, timeframe=timeframe,
datadir=datadir, timerange=timerange,
exchange=exchange)
def pair_data_filename(datadir: Path, pair: str, timeframe: str) -> Path:
pair_s = pair.replace("/", "_")
filename = datadir.joinpath(f'{pair_s}-{timeframe}.json')
@ -277,11 +277,11 @@ def _load_cached_data_for_updating(datadir: Path, pair: str, timeframe: str,
return (data, since_ms)
def download_pair_history(datadir: Path,
exchange: Optional[Exchange],
pair: str,
timeframe: str = '5m',
timerange: Optional[TimeRange] = None) -> bool:
def _download_pair_history(datadir: Path,
exchange: Exchange,
pair: str,
timeframe: str = '5m',
timerange: Optional[TimeRange] = None) -> bool:
"""
Download latest candles from the exchange for the pair and timeframe passed in parameters
The data is downloaded starting from the last correct data that
@ -295,11 +295,6 @@ def download_pair_history(datadir: Path,
:param timerange: range of time to download
:return: bool with success state
"""
if not exchange:
raise OperationalException(
"Exchange needs to be initialized when downloading pair history data"
)
try:
logger.info(
f'Download history data for pair: "{pair}", timeframe: {timeframe} '
@ -312,11 +307,12 @@ def download_pair_history(datadir: Path,
logger.debug("Current End: %s", misc.format_ms_time(data[-1][0]) if data else 'None')
# Default since_ms to 30 days if nothing is given
new_data = exchange.get_historic_ohlcv(pair=pair, timeframe=timeframe,
since_ms=since_ms if since_ms
else
new_data = exchange.get_historic_ohlcv(pair=pair,
timeframe=timeframe,
since_ms=since_ms if since_ms else
int(arrow.utcnow().shift(
days=-30).float_timestamp) * 1000)
days=-30).float_timestamp) * 1000
)
data.extend(new_data)
logger.debug("New Start: %s", misc.format_ms_time(data[0][0]))
@ -334,12 +330,12 @@ def download_pair_history(datadir: Path,
def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes: List[str],
dl_path: Path, timerange: Optional[TimeRange] = None,
datadir: Path, timerange: Optional[TimeRange] = None,
erase=False) -> List[str]:
"""
Refresh stored ohlcv data for backtesting and hyperopt operations.
Used by freqtrade download-data
:return: Pairs not available
Used by freqtrade download-data subcommand.
:return: List of pairs that are not available.
"""
pairs_not_available = []
for pair in pairs:
@ -349,23 +345,23 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes
continue
for timeframe in timeframes:
dl_file = pair_data_filename(dl_path, pair, timeframe)
dl_file = pair_data_filename(datadir, pair, timeframe)
if erase and dl_file.exists():
logger.info(
f'Deleting existing data for pair {pair}, interval {timeframe}.')
dl_file.unlink()
logger.info(f'Downloading pair {pair}, interval {timeframe}.')
download_pair_history(datadir=dl_path, exchange=exchange,
pair=pair, timeframe=str(timeframe),
timerange=timerange)
_download_pair_history(datadir=datadir, exchange=exchange,
pair=pair, timeframe=str(timeframe),
timerange=timerange)
return pairs_not_available
def download_trades_history(datadir: Path,
exchange: Exchange,
pair: str,
timerange: Optional[TimeRange] = None) -> bool:
def _download_trades_history(datadir: Path,
exchange: Exchange,
pair: str,
timerange: Optional[TimeRange] = None) -> bool:
"""
Download trade history from the exchange.
Appends to previously downloaded trades data.
@ -381,11 +377,11 @@ def download_trades_history(datadir: Path,
logger.debug("Current Start: %s", trades[0]['datetime'] if trades else 'None')
logger.debug("Current End: %s", trades[-1]['datetime'] if trades else 'None')
# Default since_ms to 30 days if nothing is given
new_trades = exchange.get_historic_trades(pair=pair,
since=since if since else
int(arrow.utcnow().shift(
days=-30).float_timestamp) * 1000,
# until=xxx,
from_id=from_id,
)
trades.extend(new_trades[1])
@ -407,9 +403,9 @@ def download_trades_history(datadir: Path,
def refresh_backtest_trades_data(exchange: Exchange, pairs: List[str], datadir: Path,
timerange: TimeRange, erase=False) -> List[str]:
"""
Refresh stored trades data.
Used by freqtrade download-data
:return: Pairs not available
Refresh stored trades data for backtesting and hyperopt operations.
Used by freqtrade download-data subcommand.
:return: List of pairs that are not available.
"""
pairs_not_available = []
for pair in pairs:
@ -425,9 +421,9 @@ def refresh_backtest_trades_data(exchange: Exchange, pairs: List[str], datadir:
dl_file.unlink()
logger.info(f'Downloading trades for pair {pair}.')
download_trades_history(datadir=datadir, exchange=exchange,
pair=pair,
timerange=timerange)
_download_trades_history(datadir=datadir, exchange=exchange,
pair=pair,
timerange=timerange)
return pairs_not_available
@ -448,22 +444,23 @@ def convert_trades_to_ohlcv(pairs: List[str], timeframes: List[str],
store_tickerdata_file(datadir, pair, timeframe, data=ohlcv)
def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
def get_timerange(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
"""
Get the maximum timeframe for the given backtest data
Get the maximum common timerange for the given backtest data.
:param data: dictionary with preprocessed backtesting data
:return: tuple containing min_date, max_date
"""
timeframe = [
timeranges = [
(arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
for frame in data.values()
]
return min(timeframe, key=operator.itemgetter(0))[0], \
max(timeframe, key=operator.itemgetter(1))[1]
return (min(timeranges, key=operator.itemgetter(0))[0],
max(timeranges, key=operator.itemgetter(1))[1])
def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,
max_date: datetime, timeframe_mins: int) -> bool:
max_date: datetime, timeframe_min: int) -> bool:
"""
Validates preprocessed backtesting data for missing values and shows warnings about it that.
@ -471,10 +468,10 @@ 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_mins: ticker Timeframe in minutes
:param timeframe_min: ticker Timeframe in minutes
"""
# total difference in minutes / timeframe-minutes
expected_frames = int((max_date - min_date).total_seconds() // 60 // timeframe_mins)
expected_frames = int((max_date - min_date).total_seconds() // 60 // timeframe_min)
found_missing = False
dflen = len(data)
if dflen < expected_frames:

457
freqtrade/edge/__init__.py
View File

@ -1,456 +1 @@
# pragma pylint: disable=W0603
""" Edge positioning package """
import logging
from pathlib import Path
from typing import Any, Dict, NamedTuple
import arrow
import numpy as np
import utils_find_1st as utf1st
from pandas import DataFrame
from freqtrade import constants, OperationalException
from freqtrade.configuration import TimeRange
from freqtrade.data import history
from freqtrade.strategy.interface import SellType
logger = logging.getLogger(__name__)
class PairInfo(NamedTuple):
stoploss: float
winrate: float
risk_reward_ratio: float
required_risk_reward: float
expectancy: float
nb_trades: int
avg_trade_duration: float
class Edge:
"""
Calculates Win Rate, Risk Reward Ratio, Expectancy
against historical data for a give set of markets and a strategy
it then adjusts stoploss and position size accordingly
and force it into the strategy
Author: https://github.com/mishaker
"""
config: Dict = {}
_cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
def __init__(self, config: Dict[str, Any], exchange, strategy) -> None:
self.config = config
self.exchange = exchange
self.strategy = strategy
self.edge_config = self.config.get('edge', {})
self._cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
self._final_pairs: list = []
# checking max_open_trades. it should be -1 as with Edge
# the number of trades is determined by position size
if self.config['max_open_trades'] != float('inf'):
logger.critical('max_open_trades should be -1 in config !')
if self.config['stake_amount'] != constants.UNLIMITED_STAKE_AMOUNT:
raise OperationalException('Edge works only with unlimited stake amount')
self._capital_percentage: float = self.edge_config.get('capital_available_percentage')
self._allowed_risk: float = self.edge_config.get('allowed_risk')
self._since_number_of_days: int = self.edge_config.get('calculate_since_number_of_days', 14)
self._last_updated: int = 0 # Timestamp of pairs last updated time
self._refresh_pairs = True
self._stoploss_range_min = float(self.edge_config.get('stoploss_range_min', -0.01))
self._stoploss_range_max = float(self.edge_config.get('stoploss_range_max', -0.05))
self._stoploss_range_step = float(self.edge_config.get('stoploss_range_step', -0.001))
# calculating stoploss range
self._stoploss_range = np.arange(
self._stoploss_range_min,
self._stoploss_range_max,
self._stoploss_range_step
)
self._timerange: TimeRange = TimeRange.parse_timerange("%s-" % arrow.now().shift(
days=-1 * self._since_number_of_days).format('YYYYMMDD'))
if config.get('fee'):
self.fee = config['fee']
else:
self.fee = self.exchange.get_fee()
def calculate(self) -> bool:
pairs = self.config['exchange']['pair_whitelist']
heartbeat = self.edge_config.get('process_throttle_secs')
if (self._last_updated > 0) and (
self._last_updated + heartbeat > arrow.utcnow().timestamp):
return False
data: Dict[str, Any] = {}
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
logger.info('Using local backtesting data (using whitelist in given config) ...')
data = history.load_data(
datadir=Path(self.config['datadir']),
pairs=pairs,
timeframe=self.strategy.ticker_interval,
refresh_pairs=self._refresh_pairs,
exchange=self.exchange,
timerange=self._timerange,
startup_candles=self.strategy.startup_candle_count,
)
if not data:
# Reinitializing cached pairs
self._cached_pairs = {}
logger.critical("No data found. Edge is stopped ...")
return False
preprocessed = self.strategy.tickerdata_to_dataframe(data)
# Print timeframe
min_date, max_date = history.get_timeframe(preprocessed)
logger.info(
'Measuring data from %s up to %s (%s days) ...',
min_date.isoformat(),
max_date.isoformat(),
(max_date - min_date).days
)
headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
trades: list = []
for pair, pair_data in preprocessed.items():
# Sorting dataframe by date and reset index
pair_data = pair_data.sort_values(by=['date'])
pair_data = pair_data.reset_index(drop=True)
ticker_data = 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)
# If no trade found then exit
if len(trades) == 0:
logger.info("No trades found.")
return False
# Fill missing, calculable columns, profit, duration , abs etc.
trades_df = self._fill_calculable_fields(DataFrame(trades))
self._cached_pairs = self._process_expectancy(trades_df)
self._last_updated = arrow.utcnow().timestamp
return True
def stake_amount(self, pair: str, free_capital: float,
total_capital: float, capital_in_trade: float) -> float:
stoploss = self.stoploss(pair)
available_capital = (total_capital + capital_in_trade) * self._capital_percentage
allowed_capital_at_risk = available_capital * self._allowed_risk
max_position_size = abs(allowed_capital_at_risk / stoploss)
position_size = min(max_position_size, free_capital)
if pair in self._cached_pairs:
logger.info(
'winrate: %s, expectancy: %s, position size: %s, pair: %s,'
' capital in trade: %s, free capital: %s, total capital: %s,'
' stoploss: %s, available capital: %s.',
self._cached_pairs[pair].winrate,
self._cached_pairs[pair].expectancy,
position_size, pair,
capital_in_trade, free_capital, total_capital,
stoploss, available_capital
)
return round(position_size, 15)
def stoploss(self, pair: str) -> float:
if pair in self._cached_pairs:
return self._cached_pairs[pair].stoploss
else:
logger.warning('tried to access stoploss of a non-existing pair, '
'strategy stoploss is returned instead.')
return self.strategy.stoploss
def adjust(self, pairs) -> list:
"""
Filters out and sorts "pairs" according to Edge calculated pairs
"""
final = []
for pair, info in self._cached_pairs.items():
if info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) and \
info.winrate > float(self.edge_config.get('minimum_winrate', 0.60)) and \
pair in pairs:
final.append(pair)
if self._final_pairs != final:
self._final_pairs = final
if self._final_pairs:
logger.info(
'Minimum expectancy and minimum winrate are met only for %s,'
' so other pairs are filtered out.',
self._final_pairs
)
else:
logger.info(
'Edge removed all pairs as no pair with minimum expectancy '
'and minimum winrate was found !'
)
return self._final_pairs
def accepted_pairs(self) -> list:
"""
return a list of accepted pairs along with their winrate, expectancy and stoploss
"""
final = []
for pair, info in self._cached_pairs.items():
if info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) and \
info.winrate > float(self.edge_config.get('minimum_winrate', 0.60)):
final.append({
'Pair': pair,
'Winrate': info.winrate,
'Expectancy': info.expectancy,
'Stoploss': info.stoploss,
})
return final
def _fill_calculable_fields(self, result: DataFrame) -> DataFrame:
"""
The result frame contains a number of columns that are calculable
from other columns. These are left blank till all rows are added,
to be populated in single vector calls.
Columns to be populated are:
- Profit
- trade duration
- profit abs
:param result Dataframe
:return: result Dataframe
"""
# stake and fees
# stake = 0.015
# 0.05% is 0.0005
# fee = 0.001
# we set stake amount to an arbitrary amount.
# as it doesn't change the calculation.
# all returned values are relative. they are percentages.
stake = 0.015
fee = self.fee
open_fee = fee / 2
close_fee = fee / 2
result['trade_duration'] = result['close_time'] - result['open_time']
result['trade_duration'] = result['trade_duration'].map(
lambda x: int(x.total_seconds() / 60))
# Spends, Takes, Profit, Absolute Profit
# Buy Price
result['buy_vol'] = stake / result['open_rate'] # How many target are we buying
result['buy_fee'] = stake * open_fee
result['buy_spend'] = stake + result['buy_fee'] # How much we're spending
# Sell price
result['sell_sum'] = result['buy_vol'] * result['close_rate']
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']
# Absolute profit
result['profit_abs'] = result['sell_take'] - result['buy_spend']
return result
def _process_expectancy(self, results: DataFrame) -> Dict[str, Any]:
"""
This calculates WinRate, Required Risk Reward, Risk Reward and Expectancy of all pairs
The calulation will be done per pair and per strategy.
"""
# Removing pairs having less than min_trades_number
min_trades_number = self.edge_config.get('min_trade_number', 10)
results = results.groupby(['pair', 'stoploss']).filter(lambda x: len(x) > min_trades_number)
###################################
# Removing outliers (Only Pumps) from the dataset
# The method to detect outliers is to calculate standard deviation
# Then every value more than (standard deviation + 2*average) is out (pump)
#
# Removing Pumps
if self.edge_config.get('remove_pumps', False):
results = results.groupby(['pair', 'stoploss']).apply(
lambda x: x[x['profit_abs'] < 2 * x['profit_abs'].std() + x['profit_abs'].mean()])
##########################################################################
# Removing trades having a duration more than X minutes (set in config)
max_trade_duration = self.edge_config.get('max_trade_duration_minute', 1440)
results = results[results.trade_duration < max_trade_duration]
#######################################################################
if results.empty:
return {}
groupby_aggregator = {
'profit_abs': [
('nb_trades', 'count'), # number of all trades
('profit_sum', lambda x: x[x > 0].sum()), # cumulative profit of all winning trades
('loss_sum', lambda x: abs(x[x < 0].sum())), # cumulative loss of all losing trades
('nb_win_trades', lambda x: x[x > 0].count()) # number of winning trades
],
'trade_duration': [('avg_trade_duration', 'mean')]
}
# Group by (pair and stoploss) by applying above aggregator
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
df.columns = df.columns.droplevel(0)
# Calculating number of losing trades, average win and average loss
df['nb_loss_trades'] = df['nb_trades'] - df['nb_win_trades']
df['average_win'] = df['profit_sum'] / df['nb_win_trades']
df['average_loss'] = df['loss_sum'] / df['nb_loss_trades']
# Win rate = number of profitable trades / number of trades
df['winrate'] = df['nb_win_trades'] / df['nb_trades']
# risk_reward_ratio = average win / average loss
df['risk_reward_ratio'] = df['average_win'] / df['average_loss']
# required_risk_reward = (1 / winrate) - 1
df['required_risk_reward'] = (1 / df['winrate']) - 1
# expectancy = (risk_reward_ratio * winrate) - (lossrate)
df['expectancy'] = (df['risk_reward_ratio'] * df['winrate']) - (1 - df['winrate'])
# sort by expectancy and stoploss
df = df.sort_values(by=['expectancy', 'stoploss'], ascending=False).groupby(
'pair').first().sort_values(by=['expectancy'], ascending=False).reset_index()
final = {}
for x in df.itertuples():
final[x.pair] = PairInfo(
x.stoploss,
x.winrate,
x.risk_reward_ratio,
x.required_risk_reward,
x.expectancy,
x.nb_trades,
x.avg_trade_duration
)
# 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
result: list = []
for stoploss in stoploss_range:
result += self._detect_next_stop_or_sell_point(
buy_column, sell_column, date_column, ohlc_columns, round(stoploss, 6), pair
)
return result
def _detect_next_stop_or_sell_point(self, buy_column, sell_column, date_column,
ohlc_columns, stoploss, pair):
"""
Iterate through ohlc_columns in order to find the next trade
Next trade opens from the first buy signal noticed to
The sell or stoploss signal after it.
It then cuts OHLC, buy_column, sell_column and date_column.
Cut from (the exit trade index) + 1.
Author: https://github.com/mishaker
"""
result: list = []
start_point = 0
while True:
open_trade_index = utf1st.find_1st(buy_column, 1, utf1st.cmp_equal)
# Return empty if we don't find trade entry (i.e. buy==1) or
# we find a buy but at the end of array
if open_trade_index == -1 or open_trade_index == len(buy_column) - 1:
break
else:
# When a buy signal is seen,
# 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)
# Searching for the index where stoploss is hit
stop_index = utf1st.find_1st(
ohlc_columns[open_trade_index:, 2], stop_price, utf1st.cmp_smaller)
# If we don't find it then we assume stop_index will be far in future (infinite number)
if stop_index == -1:
stop_index = float('inf')
# Searching for the index where sell is hit
sell_index = utf1st.find_1st(sell_column[open_trade_index:], 1, utf1st.cmp_equal)
# If we don't find it then we assume sell_index will be far in future (infinite number)
if sell_index == -1:
sell_index = float('inf')
# Check if we don't find any stop or sell point (in that case trade remains open)
# It is not interesting for Edge to consider it so we simply ignore the trade
# And stop iterating there is no more entry
if stop_index == sell_index == float('inf'):
break
if stop_index <= sell_index:
exit_index = open_trade_index + stop_index
exit_type = SellType.STOP_LOSS
exit_price = stop_price
elif stop_index > sell_index:
# If exit is SELL then we exit at the next candle
exit_index = open_trade_index + sell_index + 1
# Check if we have the next candle
if len(ohlc_columns) - 1 < exit_index:
break
exit_type = SellType.SELL_SIGNAL
exit_price = ohlc_columns[exit_index, 0]
trade = {'pair': pair,
'stoploss': stoploss,
'profit_percent': '',
'profit_abs': '',
'open_time': date_column[open_trade_index],
'close_time': date_column[exit_index],
'open_index': start_point + open_trade_index,
'close_index': start_point + exit_index,
'trade_duration': '',
'open_rate': round(open_price, 15),
'close_rate': round(exit_price, 15),
'exit_type': exit_type
}
result.append(trade)
# Giving a view of exit_index till the end of array
buy_column = buy_column[exit_index:]
sell_column = sell_column[exit_index:]
date_column = date_column[exit_index:]
ohlc_columns = ohlc_columns[exit_index:]
start_point += exit_index
return result
from .edge_positioning import Edge, PairInfo # noqa: F401

464
freqtrade/edge/edge_positioning.py Normal file
View File

@ -0,0 +1,464 @@
# pragma pylint: disable=W0603
""" Edge positioning package """
import logging
from typing import Any, Dict, NamedTuple
import arrow
import numpy as np
import utils_find_1st as utf1st
from pandas import DataFrame
from freqtrade import constants
from freqtrade.configuration import TimeRange
from freqtrade.data import history
from freqtrade.exceptions import OperationalException
from freqtrade.strategy.interface import SellType
logger = logging.getLogger(__name__)
class PairInfo(NamedTuple):
stoploss: float
winrate: float
risk_reward_ratio: float
required_risk_reward: float
expectancy: float
nb_trades: int
avg_trade_duration: float
class Edge:
"""
Calculates Win Rate, Risk Reward Ratio, Expectancy
against historical data for a give set of markets and a strategy
it then adjusts stoploss and position size accordingly
and force it into the strategy
Author: https://github.com/mishaker
"""
config: Dict = {}
_cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
def __init__(self, config: Dict[str, Any], exchange, strategy) -> None:
self.config = config
self.exchange = exchange
self.strategy = strategy
self.edge_config = self.config.get('edge', {})
self._cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
self._final_pairs: list = []
# checking max_open_trades. it should be -1 as with Edge
# the number of trades is determined by position size
if self.config['max_open_trades'] != float('inf'):
logger.critical('max_open_trades should be -1 in config !')
if self.config['stake_amount'] != constants.UNLIMITED_STAKE_AMOUNT:
raise OperationalException('Edge works only with unlimited stake amount')
# Deprecated capital_available_percentage. Will use tradable_balance_ratio in the future.
self._capital_percentage: float = self.edge_config.get(
'capital_available_percentage', self.config['tradable_balance_ratio'])
self._allowed_risk: float = self.edge_config.get('allowed_risk')
self._since_number_of_days: int = self.edge_config.get('calculate_since_number_of_days', 14)
self._last_updated: int = 0 # Timestamp of pairs last updated time
self._refresh_pairs = True
self._stoploss_range_min = float(self.edge_config.get('stoploss_range_min', -0.01))
self._stoploss_range_max = float(self.edge_config.get('stoploss_range_max', -0.05))
self._stoploss_range_step = float(self.edge_config.get('stoploss_range_step', -0.001))
# calculating stoploss range
self._stoploss_range = np.arange(
self._stoploss_range_min,
self._stoploss_range_max,
self._stoploss_range_step
)
self._timerange: TimeRange = TimeRange.parse_timerange("%s-" % arrow.now().shift(
days=-1 * self._since_number_of_days).format('YYYYMMDD'))
if config.get('fee'):
self.fee = config['fee']
else:
self.fee = self.exchange.get_fee(symbol=self.config['exchange']['pair_whitelist'][0])
def calculate(self) -> bool:
pairs = self.config['exchange']['pair_whitelist']
heartbeat = self.edge_config.get('process_throttle_secs')
if (self._last_updated > 0) and (
self._last_updated + heartbeat > arrow.utcnow().timestamp):
return False
data: Dict[str, Any] = {}
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
logger.info('Using local backtesting data (using whitelist in given config) ...')
if self._refresh_pairs:
history.refresh_data(
datadir=self.config['datadir'],
pairs=pairs,
exchange=self.exchange,
timeframe=self.strategy.ticker_interval,
timerange=self._timerange,
)
data = history.load_data(
datadir=self.config['datadir'],
pairs=pairs,
timeframe=self.strategy.ticker_interval,
timerange=self._timerange,
startup_candles=self.strategy.startup_candle_count,
)
if not data:
# Reinitializing cached pairs
self._cached_pairs = {}
logger.critical("No data found. Edge is stopped ...")
return False
preprocessed = self.strategy.tickerdata_to_dataframe(data)
# Print timeframe
min_date, max_date = history.get_timerange(preprocessed)
logger.info(
'Measuring data from %s up to %s (%s days) ...',
min_date.isoformat(),
max_date.isoformat(),
(max_date - min_date).days
)
headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
trades: list = []
for pair, pair_data in preprocessed.items():
# Sorting dataframe by date and reset index
pair_data = pair_data.sort_values(by=['date'])
pair_data = pair_data.reset_index(drop=True)
ticker_data = 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)
# If no trade found then exit
if len(trades) == 0:
logger.info("No trades found.")
return False
# Fill missing, calculable columns, profit, duration , abs etc.
trades_df = self._fill_calculable_fields(DataFrame(trades))
self._cached_pairs = self._process_expectancy(trades_df)
self._last_updated = arrow.utcnow().timestamp
return True
def stake_amount(self, pair: str, free_capital: float,
total_capital: float, capital_in_trade: float) -> float:
stoploss = self.stoploss(pair)
available_capital = (total_capital + capital_in_trade) * self._capital_percentage
allowed_capital_at_risk = available_capital * self._allowed_risk
max_position_size = abs(allowed_capital_at_risk / stoploss)
position_size = min(max_position_size, free_capital)
if pair in self._cached_pairs:
logger.info(
'winrate: %s, expectancy: %s, position size: %s, pair: %s,'
' capital in trade: %s, free capital: %s, total capital: %s,'
' stoploss: %s, available capital: %s.',
self._cached_pairs[pair].winrate,
self._cached_pairs[pair].expectancy,
position_size, pair,
capital_in_trade, free_capital, total_capital,
stoploss, available_capital
)
return round(position_size, 15)
def stoploss(self, pair: str) -> float:
if pair in self._cached_pairs:
return self._cached_pairs[pair].stoploss
else:
logger.warning('tried to access stoploss of a non-existing pair, '
'strategy stoploss is returned instead.')
return self.strategy.stoploss
def adjust(self, pairs) -> list:
"""
Filters out and sorts "pairs" according to Edge calculated pairs
"""
final = []
for pair, info in self._cached_pairs.items():
if info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) and \
info.winrate > float(self.edge_config.get('minimum_winrate', 0.60)) and \
pair in pairs:
final.append(pair)
if self._final_pairs != final:
self._final_pairs = final
if self._final_pairs:
logger.info(
'Minimum expectancy and minimum winrate are met only for %s,'
' so other pairs are filtered out.',
self._final_pairs
)
else:
logger.info(
'Edge removed all pairs as no pair with minimum expectancy '
'and minimum winrate was found !'
)
return self._final_pairs
def accepted_pairs(self) -> list:
"""
return a list of accepted pairs along with their winrate, expectancy and stoploss
"""
final = []
for pair, info in self._cached_pairs.items():
if info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) and \
info.winrate > float(self.edge_config.get('minimum_winrate', 0.60)):
final.append({
'Pair': pair,
'Winrate': info.winrate,
'Expectancy': info.expectancy,
'Stoploss': info.stoploss,
})
return final
def _fill_calculable_fields(self, result: DataFrame) -> DataFrame:
"""
The result frame contains a number of columns that are calculable
from other columns. These are left blank till all rows are added,
to be populated in single vector calls.
Columns to be populated are:
- Profit
- trade duration
- profit abs
:param result Dataframe
:return: result Dataframe
"""
# stake and fees
# stake = 0.015
# 0.05% is 0.0005
# fee = 0.001
# we set stake amount to an arbitrary amount.
# as it doesn't change the calculation.
# all returned values are relative. they are percentages.
stake = 0.015
fee = self.fee
open_fee = fee / 2
close_fee = fee / 2
result['trade_duration'] = result['close_time'] - result['open_time']
result['trade_duration'] = result['trade_duration'].map(
lambda x: int(x.total_seconds() / 60))
# Spends, Takes, Profit, Absolute Profit
# Buy Price
result['buy_vol'] = stake / result['open_rate'] # How many target are we buying
result['buy_fee'] = stake * open_fee
result['buy_spend'] = stake + result['buy_fee'] # How much we're spending
# Sell price
result['sell_sum'] = result['buy_vol'] * result['close_rate']
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']
# Absolute profit
result['profit_abs'] = result['sell_take'] - result['buy_spend']
return result
def _process_expectancy(self, results: DataFrame) -> Dict[str, Any]:
"""
This calculates WinRate, Required Risk Reward, Risk Reward and Expectancy of all pairs
The calulation will be done per pair and per strategy.
"""
# Removing pairs having less than min_trades_number
min_trades_number = self.edge_config.get('min_trade_number', 10)
results = results.groupby(['pair', 'stoploss']).filter(lambda x: len(x) > min_trades_number)
###################################
# Removing outliers (Only Pumps) from the dataset
# The method to detect outliers is to calculate standard deviation
# Then every value more than (standard deviation + 2*average) is out (pump)
#
# Removing Pumps
if self.edge_config.get('remove_pumps', False):
results = results.groupby(['pair', 'stoploss']).apply(
lambda x: x[x['profit_abs'] < 2 * x['profit_abs'].std() + x['profit_abs'].mean()])
##########################################################################
# Removing trades having a duration more than X minutes (set in config)
max_trade_duration = self.edge_config.get('max_trade_duration_minute', 1440)
results = results[results.trade_duration < max_trade_duration]
#######################################################################
if results.empty:
return {}
groupby_aggregator = {
'profit_abs': [
('nb_trades', 'count'), # number of all trades
('profit_sum', lambda x: x[x > 0].sum()), # cumulative profit of all winning trades
('loss_sum', lambda x: abs(x[x < 0].sum())), # cumulative loss of all losing trades
('nb_win_trades', lambda x: x[x > 0].count()) # number of winning trades
],
'trade_duration': [('avg_trade_duration', 'mean')]
}
# Group by (pair and stoploss) by applying above aggregator
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
df.columns = df.columns.droplevel(0)
# Calculating number of losing trades, average win and average loss
df['nb_loss_trades'] = df['nb_trades'] - df['nb_win_trades']
df['average_win'] = df['profit_sum'] / df['nb_win_trades']
df['average_loss'] = df['loss_sum'] / df['nb_loss_trades']
# Win rate = number of profitable trades / number of trades
df['winrate'] = df['nb_win_trades'] / df['nb_trades']
# risk_reward_ratio = average win / average loss
df['risk_reward_ratio'] = df['average_win'] / df['average_loss']
# required_risk_reward = (1 / winrate) - 1
df['required_risk_reward'] = (1 / df['winrate']) - 1
# expectancy = (risk_reward_ratio * winrate) - (lossrate)
df['expectancy'] = (df['risk_reward_ratio'] * df['winrate']) - (1 - df['winrate'])
# sort by expectancy and stoploss
df = df.sort_values(by=['expectancy', 'stoploss'], ascending=False).groupby(
'pair').first().sort_values(by=['expectancy'], ascending=False).reset_index()
final = {}
for x in df.itertuples():
final[x.pair] = PairInfo(
x.stoploss,
x.winrate,
x.risk_reward_ratio,
x.required_risk_reward,
x.expectancy,
x.nb_trades,
x.avg_trade_duration
)
# 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
result: list = []
for stoploss in stoploss_range:
result += self._detect_next_stop_or_sell_point(
buy_column, sell_column, date_column, ohlc_columns, round(stoploss, 6), pair
)
return result
def _detect_next_stop_or_sell_point(self, buy_column, sell_column, date_column,
ohlc_columns, stoploss, pair):
"""
Iterate through ohlc_columns in order to find the next trade
Next trade opens from the first buy signal noticed to
The sell or stoploss signal after it.
It then cuts OHLC, buy_column, sell_column and date_column.
Cut from (the exit trade index) + 1.
Author: https://github.com/mishaker
"""
result: list = []
start_point = 0
while True:
open_trade_index = utf1st.find_1st(buy_column, 1, utf1st.cmp_equal)
# Return empty if we don't find trade entry (i.e. buy==1) or
# we find a buy but at the end of array
if open_trade_index == -1 or open_trade_index == len(buy_column) - 1:
break
else:
# When a buy signal is seen,
# 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)
# Searching for the index where stoploss is hit
stop_index = utf1st.find_1st(
ohlc_columns[open_trade_index:, 2], stop_price, utf1st.cmp_smaller)
# If we don't find it then we assume stop_index will be far in future (infinite number)
if stop_index == -1:
stop_index = float('inf')
# Searching for the index where sell is hit
sell_index = utf1st.find_1st(sell_column[open_trade_index:], 1, utf1st.cmp_equal)
# If we don't find it then we assume sell_index will be far in future (infinite number)
if sell_index == -1:
sell_index = float('inf')
# Check if we don't find any stop or sell point (in that case trade remains open)
# It is not interesting for Edge to consider it so we simply ignore the trade
# And stop iterating there is no more entry
if stop_index == sell_index == float('inf'):
break
if stop_index <= sell_index:
exit_index = open_trade_index + stop_index
exit_type = SellType.STOP_LOSS
exit_price = stop_price
elif stop_index > sell_index:
# If exit is SELL then we exit at the next candle
exit_index = open_trade_index + sell_index + 1
# Check if we have the next candle
if len(ohlc_columns) - 1 < exit_index:
break
exit_type = SellType.SELL_SIGNAL
exit_price = ohlc_columns[exit_index, 0]
trade = {'pair': pair,
'stoploss': stoploss,
'profit_percent': '',
'profit_abs': '',
'open_time': date_column[open_trade_index],
'close_time': date_column[exit_index],
'open_index': start_point + open_trade_index,
'close_index': start_point + exit_index,
'trade_duration': '',
'open_rate': round(open_price, 15),
'close_rate': round(exit_price, 15),
'exit_type': exit_type
}
result.append(trade)
# Giving a view of exit_index till the end of array
buy_column = buy_column[exit_index:]
sell_column = sell_column[exit_index:]
date_column = date_column[exit_index:]
ohlc_columns = ohlc_columns[exit_index:]
start_point += exit_index
return result

37
freqtrade/exceptions.py Normal file
View File

@ -0,0 +1,37 @@
class FreqtradeException(Exception):
"""
Freqtrade base exception. Handled at the outermost level.
All other exception types are subclasses of this exception type.
"""
class OperationalException(FreqtradeException):
"""
Requires manual intervention and will stop the bot.
Most of the time, this is caused by an invalid Configuration.
"""
class DependencyException(FreqtradeException):
"""
Indicates that an assumed dependency is not met.
This could happen when there is currently not enough money on the account.
"""
class InvalidOrderException(FreqtradeException):
"""
This is returned when the order is not valid. Example:
If stoploss on exchange order is hit, then trying to cancel the order
should return this exception.
"""
class TemporaryError(FreqtradeException):
"""
Temporary network or exchange related error.
This could happen when an exchange is congested, unavailable, or the user
has networking problems. Usually resolves itself after a time.
"""

10
freqtrade/exchange/binance.py
View File

@ -4,8 +4,8 @@ from typing import Dict
import ccxt
from freqtrade import (DependencyException, InvalidOrderException,
OperationalException, TemporaryError)
from freqtrade.exceptions import (DependencyException, InvalidOrderException,
OperationalException, TemporaryError)
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
@ -41,7 +41,7 @@ class Binance(Exchange):
"""
ordertype = "stop_loss_limit"
stop_price = self.symbol_price_prec(pair, stop_price)
stop_price = self.price_to_precision(pair, stop_price)
# Ensure rate is less than stop price
if stop_price <= rate:
@ -57,9 +57,9 @@ class Binance(Exchange):
params = self._params.copy()
params.update({'stopPrice': stop_price})
amount = self.symbol_amount_prec(pair, amount)
amount = self.amount_to_precision(pair, amount)
rate = self.symbol_price_prec(pair, rate)
rate = self.price_to_precision(pair, rate)
order = self._api.create_order(pair, ordertype, 'sell',
amount, rate, params)

2
freqtrade/exchange/common.py
View File

@ -1,6 +1,6 @@
import logging
from freqtrade import DependencyException, TemporaryError
from freqtrade.exceptions import DependencyException, TemporaryError
logger = logging.getLogger(__name__)

106
freqtrade/exchange/exchange.py
View File

@ -7,19 +7,20 @@ import inspect
import logging
from copy import deepcopy
from datetime import datetime, timezone
from math import ceil, floor
from math import ceil
from random import randint
from typing import Any, Dict, List, Optional, Tuple
import arrow
import ccxt
import ccxt.async_support as ccxt_async
from ccxt.base.decimal_to_precision import ROUND_DOWN, ROUND_UP
from ccxt.base.decimal_to_precision import (ROUND_DOWN, ROUND_UP, TICK_SIZE,
TRUNCATE, decimal_to_precision)
from pandas import DataFrame
from freqtrade import (DependencyException, InvalidOrderException,
OperationalException, TemporaryError, constants)
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.exceptions import (DependencyException, InvalidOrderException,
OperationalException, TemporaryError)
from freqtrade.exchange.common import BAD_EXCHANGES, retrier, retrier_async
from freqtrade.misc import deep_merge_dicts
@ -116,6 +117,7 @@ class Exchange:
self._load_markets()
# Check if all pairs are available
self.validate_stakecurrency(config['stake_currency'])
self.validate_pairs(config['exchange']['pair_whitelist'])
self.validate_ordertypes(config.get('order_types', {}))
self.validate_order_time_in_force(config.get('order_time_in_force', {}))
@ -188,6 +190,11 @@ class Exchange:
self._load_markets()
return self._api.markets
@property
def precisionMode(self) -> str:
"""exchange ccxt precisionMode"""
return self._api.precisionMode
def get_markets(self, base_currencies: List[str] = None, quote_currencies: List[str] = None,
pairs_only: bool = False, active_only: bool = False) -> Dict:
"""
@ -210,6 +217,13 @@ class Exchange:
markets = {k: v for k, v in markets.items() if market_is_active(v)}
return markets
def get_quote_currencies(self) -> List[str]:
"""
Return a list of supported quote currencies
"""
markets = self.markets
return sorted(set([x['quote'] for _, x in markets.items()]))
def klines(self, pair_interval: Tuple[str, str], copy=True) -> DataFrame:
if pair_interval in self._klines:
return self._klines[pair_interval].copy() if copy else self._klines[pair_interval]
@ -259,11 +273,23 @@ class Exchange:
except ccxt.BaseError:
logger.exception("Could not reload markets.")
def validate_stakecurrency(self, stake_currency) -> None:
"""
Checks stake-currency against available currencies on the exchange.
:param stake_currency: Stake-currency to validate
:raise: OperationalException if stake-currency is not available.
"""
quote_currencies = self.get_quote_currencies()
if stake_currency not in quote_currencies:
raise OperationalException(
f"{stake_currency} is not available as stake on {self.name}. "
f"Available currencies are: {', '.join(quote_currencies)}")
def validate_pairs(self, pairs: List[str]) -> None:
"""
Checks if all given pairs are tradable on the current exchange.
Raises OperationalException if one pair is not available.
:param pairs: list of pairs
:raise: OperationalException if one pair is not available
:return: None
"""
@ -278,7 +304,15 @@ class Exchange:
raise OperationalException(
f'Pair {pair} is not available on {self.name}. '
f'Please remove {pair} from your whitelist.')
elif self.markets[pair].get('info', {}).get('IsRestricted', False):
# From ccxt Documentation:
# markets.info: An associative array of non-common market properties,
# including fees, rates, limits and other general market information.
# The internal info array is different for each particular market,
# its contents depend on the exchange.
# It can also be a string or similar ... so we need to verify that first.
elif (isinstance(self.markets[pair].get('info', None), dict)
and self.markets[pair].get('info', {}).get('IsRestricted', False)):
# Warn users about restricted pairs in whitelist.
# We cannot determine reliably if Users are affected.
logger.warning(f"Pair {pair} is restricted for some users on this exchange."
@ -311,6 +345,10 @@ class Exchange:
raise OperationalException(
f"Invalid ticker interval '{timeframe}'. This exchange supports: {self.timeframes}")
if timeframe and timeframe_to_minutes(timeframe) < 1:
raise OperationalException(
f"Timeframes < 1m are currently not supported by Freqtrade.")
def validate_ordertypes(self, order_types: Dict) -> None:
"""
Checks if order-types configured in strategy/config are supported
@ -354,40 +392,58 @@ class Exchange:
"""
return endpoint in self._api.has and self._api.has[endpoint]
def symbol_amount_prec(self, pair, amount: float):
def amount_to_precision(self, pair, amount: float) -> float:
'''
Returns the amount to buy or sell to a precision the Exchange accepts
Rounded down
Reimplementation of ccxt internal methods - ensuring we can test the result is correct
based on our definitions.
'''
if self.markets[pair]['precision']['amount']:
symbol_prec = self.markets[pair]['precision']['amount']
big_amount = amount * pow(10, symbol_prec)
amount = floor(big_amount) / pow(10, symbol_prec)
amount = float(decimal_to_precision(amount, rounding_mode=TRUNCATE,
precision=self.markets[pair]['precision']['amount'],
counting_mode=self.precisionMode,
))
return amount
def symbol_price_prec(self, pair, price: float):
def price_to_precision(self, pair, price: float) -> float:
'''
Returns the price buying or selling with to the precision the Exchange accepts
Returns the price rounded up to the precision the Exchange accepts.
Partial Reimplementation of ccxt internal method decimal_to_precision(),
which does not support rounding up
TODO: If ccxt supports ROUND_UP for decimal_to_precision(), we could remove this and
align with amount_to_precision().
Rounds up
'''
if self.markets[pair]['precision']['price']:
symbol_prec = self.markets[pair]['precision']['price']
big_price = price * pow(10, symbol_prec)
price = ceil(big_price) / pow(10, symbol_prec)
# price = float(decimal_to_precision(price, rounding_mode=ROUND,
# precision=self.markets[pair]['precision']['price'],
# counting_mode=self.precisionMode,
# ))
if self.precisionMode == TICK_SIZE:
precision = self.markets[pair]['precision']['price']
missing = price % precision
if missing != 0:
price = price - missing + precision
else:
symbol_prec = self.markets[pair]['precision']['price']
big_price = price * pow(10, symbol_prec)
price = ceil(big_price) / pow(10, symbol_prec)
return price
def dry_run_order(self, pair: str, ordertype: str, side: str, amount: float,
rate: float, params: Dict = {}) -> Dict[str, Any]:
order_id = f'dry_run_{side}_{randint(0, 10**6)}'
_amount = self.amount_to_precision(pair, amount)
dry_order = {
"id": order_id,
'pair': pair,
'price': rate,
'amount': amount,
"cost": amount * rate,
'amount': _amount,
"cost": _amount * rate,
'type': ordertype,
'side': side,
'remaining': amount,
'remaining': _amount,
'datetime': arrow.utcnow().isoformat(),
'status': "closed" if ordertype == "market" else "open",
'fee': None,
@ -413,13 +469,13 @@ class Exchange:
rate: float, params: Dict = {}) -> Dict:
try:
# Set the precision for amount and price(rate) as accepted by the exchange
amount = self.symbol_amount_prec(pair, amount)
amount = self.amount_to_precision(pair, amount)
needs_price = (ordertype != 'market'
or self._api.options.get("createMarketBuyOrderRequiresPrice", False))
rate = self.symbol_price_prec(pair, rate) if needs_price else None
rate_for_order = self.price_to_precision(pair, rate) if needs_price else None
return self._api.create_order(pair, ordertype, side,
amount, rate, params)
amount, rate_for_order, params)
except ccxt.InsufficientFunds as e:
raise DependencyException(
@ -478,7 +534,7 @@ class Exchange:
@retrier
def get_balance(self, currency: str) -> float:
if self._config['dry_run']:
return constants.DRY_RUN_WALLET
return self._config['dry_run_wallet']
# ccxt exception is already handled by get_balances
balances = self.get_balances()
@ -523,7 +579,7 @@ class Exchange:
raise OperationalException(e) from e
@retrier
def get_ticker(self, pair: str, refresh: Optional[bool] = True) -> dict:
def fetch_ticker(self, pair: str, refresh: Optional[bool] = True) -> dict:
if refresh or pair not in self._cached_ticker.keys():
try:
if pair not in self._api.markets or not self._api.markets[pair].get('active'):
@ -920,7 +976,7 @@ class Exchange:
raise OperationalException(e) from e
@retrier
def get_fee(self, symbol='ETH/BTC', type='', side='', amount=1,
def get_fee(self, symbol, type='', side='', amount=1,
price=1, taker_or_maker='maker') -> float:
try:
# validate that markets are loaded before trying to get fee

2
freqtrade/exchange/kraken.py
View File

@ -4,7 +4,7 @@ from typing import Dict
import ccxt
from freqtrade import OperationalException, TemporaryError
from freqtrade.exceptions import OperationalException, TemporaryError
from freqtrade.exchange import Exchange
from freqtrade.exchange.exchange import retrier

536
freqtrade/freqtradebot.py
View File

@ -7,22 +7,23 @@ 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 requests.exceptions import RequestException
from freqtrade import (DependencyException, InvalidOrderException, __version__,
constants, persistence)
from freqtrade import __version__, constants, persistence
from freqtrade.configuration import validate_config_consistency
from freqtrade.data.converter import order_book_to_dataframe
from freqtrade.data.dataprovider import DataProvider
from freqtrade.edge import Edge
from freqtrade.exceptions import DependencyException, InvalidOrderException
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date
from freqtrade.pairlist.pairlistmanager import PairListManager
from freqtrade.persistence import Trade
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.rpc import RPCManager, RPCMessageType
from freqtrade.pairlist.pairlistmanager import PairListManager
from freqtrade.state import State
from freqtrade.strategy.interface import IStrategy, SellType
from freqtrade.wallets import Wallets
@ -55,14 +56,17 @@ class FreqtradeBot:
self.heartbeat_interval = self.config.get('internals', {}).get('heartbeat_interval', 60)
self.strategy: IStrategy = StrategyResolver(self.config).strategy
self.strategy: IStrategy = StrategyResolver.load_strategy(self.config)
# Check config consistency here since strategies can set certain options
validate_config_consistency(config)
self.exchange = ExchangeResolver(self.config['exchange']['name'], self.config).exchange
self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
persistence.init(self.config.get('db_url', None), clean_open_orders=self.config['dry_run'])
self.wallets = Wallets(self.config, self.exchange)
self.dataprovider = DataProvider(self.config, self.exchange)
# Attach Dataprovider to Strategy baseclass
@ -78,9 +82,6 @@ class FreqtradeBot:
self.active_pair_whitelist = self._refresh_whitelist()
persistence.init(self.config.get('db_url', None),
clean_open_orders=self.config.get('dry_run', False))
# Set initial bot state from config
initial_state = self.config.get('initial_state')
self.state = State[initial_state.upper()] if initial_state else State.STOPPED
@ -91,6 +92,18 @@ class FreqtradeBot:
# the initial state of the bot.
# Keep this at the end of this initialization method.
self.rpc: RPCManager = RPCManager(self)
# Protect sell-logic from forcesell and viceversa
self._sell_lock = Lock()
def notify_status(self, msg: str) -> None:
"""
Public method for users of this class (worker, etc.) to send notifications
via RPC about changes in the bot status.
"""
self.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': msg
})
def cleanup(self) -> None:
"""
@ -131,12 +144,16 @@ class FreqtradeBot:
self.dataprovider.refresh(self._create_pair_whitelist(self.active_pair_whitelist),
self.strategy.informative_pairs())
# First process current opened trades
self.process_maybe_execute_sells(trades)
# Protect from collisions with forcesell.
# Without this, freqtrade my try to recreate stoploss_on_exchange orders
# while selling is in process, since telegram messages arrive in an different thread.
with self._sell_lock:
# First process current opened trades (positions)
self.exit_positions(trades)
# Then looking for buy opportunities
if len(trades) < self.config['max_open_trades']:
self.process_maybe_execute_buys()
if self.get_free_open_trades():
self.enter_positions()
# Check and handle any timed out open orders
self.check_handle_timedout()
@ -172,7 +189,52 @@ class FreqtradeBot:
"""
return [(pair, self.config['ticker_interval']) for pair in pairs]
def get_target_bid(self, pair: str, tick: Dict = None) -> float:
def get_free_open_trades(self):
"""
Return the number of free open trades slots or 0 if
max number of open trades reached
"""
open_trades = len(Trade.get_open_trades())
return max(0, self.config['max_open_trades'] - open_trades)
#
# BUY / enter positions / open trades logic and methods
#
def enter_positions(self) -> int:
"""
Tries to execute buy orders for new trades (positions)
"""
trades_created = 0
whitelist = copy.deepcopy(self.active_pair_whitelist)
if not whitelist:
logger.info("Active pair whitelist is empty.")
else:
# Remove pairs for currently opened trades from the whitelist
for trade in Trade.get_open_trades():
if trade.pair in whitelist:
whitelist.remove(trade.pair)
logger.debug('Ignoring %s in pair whitelist', trade.pair)
if not whitelist:
logger.info("No currency pair in active pair whitelist, "
"but checking to sell open trades.")
else:
# Create entity and execute trade for each pair from whitelist
for pair in whitelist:
try:
trades_created += self.create_trade(pair)
except DependencyException as exception:
logger.warning('Unable to create trade for %s: %s', pair, exception)
if not trades_created:
logger.debug("Found no buy signals for whitelisted currencies. "
"Trying again...")
return trades_created
def get_buy_rate(self, pair: str, tick: Dict = None) -> float:
"""
Calculates bid target between current ask price and last price
:return: float: Price
@ -191,7 +253,7 @@ class FreqtradeBot:
else:
if not tick:
logger.info('Using Last Ask / Last Price')
ticker = self.exchange.get_ticker(pair)
ticker = self.exchange.fetch_ticker(pair)
else:
ticker = tick
if ticker['ask'] < ticker['last']:
@ -203,14 +265,18 @@ class FreqtradeBot:
return used_rate
def _get_trade_stake_amount(self, pair) -> Optional[float]:
def get_trade_stake_amount(self, pair) -> float:
"""
Check if stake amount can be fulfilled with the available balance
for the stake currency
:return: float: Stake Amount
Calculate stake amount for the trade
:return: float: Stake amount
:raise: DependencyException if the available stake amount is too low
"""
stake_amount: float
# Ensure wallets are uptodate.
self.wallets.update()
if self.edge:
return self.edge.stake_amount(
stake_amount = self.edge.stake_amount(
pair,
self.wallets.get_free(self.config['stake_currency']),
self.wallets.get_total(self.config['stake_currency']),
@ -218,21 +284,60 @@ class FreqtradeBot:
)
else:
stake_amount = self.config['stake_amount']
if stake_amount == constants.UNLIMITED_STAKE_AMOUNT:
stake_amount = self._calculate_unlimited_stake_amount()
available_amount = self.wallets.get_free(self.config['stake_currency'])
return self._check_available_stake_amount(stake_amount)
if stake_amount == constants.UNLIMITED_STAKE_AMOUNT:
open_trades = len(Trade.get_open_trades())
if open_trades >= self.config['max_open_trades']:
logger.warning("Can't open a new trade: max number of trades is reached")
return None
return available_amount / (self.config['max_open_trades'] - open_trades)
def _get_available_stake_amount(self) -> float:
"""
Return the total currently available balance in stake currency,
respecting tradable_balance_ratio.
Calculated as
<open_trade stakes> + free amount ) * tradable_balance_ratio - <open_trade stakes>
"""
val_tied_up = Trade.total_open_trades_stakes()
# Ensure <tradable_balance_ratio>% is used from the overall balance
# Otherwise we'd risk lowering stakes with each open trade.
# (tied up + current free) * ratio) - tied up
available_amount = ((val_tied_up + self.wallets.get_free(self.config['stake_currency'])) *
self.config['tradable_balance_ratio']) - val_tied_up
return available_amount
def _calculate_unlimited_stake_amount(self) -> float:
"""
Calculate stake amount for "unlimited" stake amount
:return: 0 if max number of trades reached, else stake_amount to use.
"""
free_open_trades = self.get_free_open_trades()
if not free_open_trades:
return 0
available_amount = self._get_available_stake_amount()
return available_amount / free_open_trades
def _check_available_stake_amount(self, stake_amount: float) -> float:
"""
Check if stake amount can be fulfilled with the available balance
for the stake currency
:return: float: Stake amount
"""
available_amount = self._get_available_stake_amount()
if self.config['amend_last_stake_amount']:
# Remaining amount needs to be at least stake_amount * last_stake_amount_min_ratio
# Otherwise the remaining amount is too low to trade.
if available_amount > (stake_amount * self.config['last_stake_amount_min_ratio']):
stake_amount = min(stake_amount, available_amount)
else:
stake_amount = 0
# Check if stake_amount is fulfilled
if available_amount < stake_amount:
raise DependencyException(
f"Available balance({available_amount} {self.config['stake_currency']}) is "
f"lower than stake amount({stake_amount} {self.config['stake_currency']})"
f"Available balance ({available_amount} {self.config['stake_currency']}) is "
f"lower than stake amount ({stake_amount} {self.config['stake_currency']})"
)
return stake_amount
@ -272,60 +377,50 @@ class FreqtradeBot:
# See also #2575 at github.
return max(min_stake_amounts) / amount_reserve_percent
def create_trades(self) -> bool:
def create_trade(self, pair: str) -> bool:
"""
Checks the implemented trading strategy for buy-signals, using the active pair whitelist.
If a pair triggers the buy_signal a new trade record gets created.
Checks pairs as long as the open trade count is below `max_open_trades`.
:return: True if at least one trade has been created.
"""
whitelist = copy.deepcopy(self.active_pair_whitelist)
Check the implemented trading strategy for buy signals.
if not whitelist:
logger.info("Active pair whitelist is empty.")
If the pair triggers the buy signal a new trade record gets created
and the buy-order opening the trade gets issued towards the exchange.
:return: True if a trade has been created.
"""
logger.debug(f"create_trade for pair {pair}")
if self.strategy.is_pair_locked(pair):
logger.info(f"Pair {pair} is currently locked.")
return False
# Remove currently opened and latest pairs from whitelist
for trade in Trade.get_open_trades():
if trade.pair in whitelist:
whitelist.remove(trade.pair)
logger.debug('Ignoring %s in pair whitelist', trade.pair)
if not whitelist:
logger.info("No currency pair in active pair whitelist, "
"but checking to sell open trades.")
return False
buycount = 0
# running get_signal on historical data fetched
for _pair in whitelist:
if self.strategy.is_pair_locked(_pair):
logger.info(f"Pair {_pair} is currently locked.")
continue
(buy, sell) = self.strategy.get_signal(
pair, self.strategy.ticker_interval,
self.dataprovider.ohlcv(pair, self.strategy.ticker_interval))
(buy, sell) = self.strategy.get_signal(
_pair, self.strategy.ticker_interval,
self.dataprovider.ohlcv(_pair, self.strategy.ticker_interval))
if buy and not sell:
if not self.get_free_open_trades():
logger.debug("Can't open a new trade: max number of trades is reached.")
return False
if buy and not sell and len(Trade.get_open_trades()) < self.config['max_open_trades']:
stake_amount = self._get_trade_stake_amount(_pair)
if not stake_amount:
continue
stake_amount = self.get_trade_stake_amount(pair)
if not stake_amount:
logger.debug("Stake amount is 0, ignoring possible trade for {pair}.")
return False
logger.info(f"Buy signal found: about create a new trade with stake_amount: "
f"{stake_amount} ...")
logger.info(f"Buy signal found: about create a new trade with stake_amount: "
f"{stake_amount} ...")
bidstrat_check_depth_of_market = self.config.get('bid_strategy', {}).\
get('check_depth_of_market', {})
if (bidstrat_check_depth_of_market.get('enabled', False)) and\
(bidstrat_check_depth_of_market.get('bids_to_ask_delta', 0) > 0):
if self._check_depth_of_market_buy(_pair, bidstrat_check_depth_of_market):
buycount += self.execute_buy(_pair, stake_amount)
continue
bid_check_dom = self.config.get('bid_strategy', {}).get('check_depth_of_market', {})
if ((bid_check_dom.get('enabled', False)) and
(bid_check_dom.get('bids_to_ask_delta', 0) > 0)):
if self._check_depth_of_market_buy(pair, bid_check_dom):
return self.execute_buy(pair, stake_amount)
else:
return False
buycount += self.execute_buy(_pair, stake_amount)
return buycount > 0
return self.execute_buy(pair, stake_amount)
else:
return False
def _check_depth_of_market_buy(self, pair: str, conf: Dict) -> bool:
"""
@ -350,21 +445,18 @@ class FreqtradeBot:
:param pair: pair for which we want to create a LIMIT_BUY
:return: None
"""
pair_s = pair.replace('_', '/')
stake_currency = self.config['stake_currency']
fiat_currency = self.config.get('fiat_display_currency', None)
time_in_force = self.strategy.order_time_in_force['buy']
if price:
buy_limit_requested = price
else:
# Calculate amount
buy_limit_requested = self.get_target_bid(pair)
# Calculate price
buy_limit_requested = self.get_buy_rate(pair)
min_stake_amount = self._get_min_pair_stake_amount(pair_s, buy_limit_requested)
min_stake_amount = self._get_min_pair_stake_amount(pair, buy_limit_requested)
if min_stake_amount is not None and min_stake_amount > stake_amount:
logger.warning(
f"Can't open a new trade for {pair_s}: stake amount "
f"Can't open a new trade for {pair}: stake amount "
f"is too small ({stake_amount} < {min_stake_amount})"
)
return False
@ -387,7 +479,7 @@ class FreqtradeBot:
if float(order['filled']) == 0:
logger.warning('Buy %s order with time in force %s for %s is %s by %s.'
' zero amount is fulfilled.',
order_tif, order_type, pair_s, order_status, self.exchange.name)
order_tif, order_type, pair, order_status, self.exchange.name)
return False
else:
# the order is partially fulfilled
@ -395,7 +487,7 @@ class FreqtradeBot:
# if the order is fulfilled fully or partially
logger.warning('Buy %s order with time in force %s for %s is %s by %s.'
' %s amount fulfilled out of %s (%s remaining which is canceled).',
order_tif, order_type, pair_s, order_status, self.exchange.name,
order_tif, order_type, pair, order_status, self.exchange.name,
order['filled'], order['amount'], order['remaining']
)
stake_amount = order['cost']
@ -409,17 +501,6 @@ class FreqtradeBot:
amount = order['amount']
buy_limit_filled_price = order['price']
self.rpc.send_msg({
'type': RPCMessageType.BUY_NOTIFICATION,
'exchange': self.exchange.name.capitalize(),
'pair': pair_s,
'limit': buy_limit_filled_price,
'order_type': order_type,
'stake_amount': stake_amount,
'stake_currency': stake_currency,
'fiat_currency': fiat_currency
})
# Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
fee = self.exchange.get_fee(symbol=pair, taker_or_maker='maker')
trade = Trade(
@ -437,6 +518,8 @@ class FreqtradeBot:
ticker_interval=timeframe_to_minutes(self.config['ticker_interval'])
)
self._notify_buy(trade, order_type)
# Update fees if order is closed
if order_status == 'closed':
self.update_trade_state(trade, order)
@ -449,126 +532,59 @@ class FreqtradeBot:
return True
def process_maybe_execute_buys(self) -> None:
def _notify_buy(self, trade: Trade, order_type: str):
"""
Tries to execute buy orders for trades in a safe way
Sends rpc notification when a buy occured.
"""
try:
# Create entity and execute trade
if not self.create_trades():
logger.debug('Found no buy signals for whitelisted currencies. Trying again...')
except DependencyException as exception:
logger.warning('Unable to create trade: %s', exception)
msg = {
'type': RPCMessageType.BUY_NOTIFICATION,
'exchange': self.exchange.name.capitalize(),
'pair': trade.pair,
'limit': trade.open_rate,
'order_type': order_type,
'stake_amount': trade.stake_amount,
'stake_currency': self.config['stake_currency'],
'fiat_currency': self.config.get('fiat_display_currency', None),
}
def process_maybe_execute_sells(self, trades: List[Any]) -> None:
# Send the message
self.rpc.send_msg(msg)
#
# SELL / exit positions / close trades logic and methods
#
def exit_positions(self, trades: List[Any]) -> int:
"""
Tries to execute sell orders for trades in a safe way
Tries to execute sell orders for open trades (positions)
"""
result = False
trades_closed = 0
for trade in trades:
try:
self.update_trade_state(trade)
if (self.strategy.order_types.get('stoploss_on_exchange') and
self.handle_stoploss_on_exchange(trade)):
result = True
trades_closed += 1
continue
# Check if we can sell our current pair
if trade.open_order_id is None and self.handle_trade(trade):
result = True
trades_closed += 1
except DependencyException as exception:
logger.warning('Unable to sell trade: %s', exception)
# Updating wallets if any trade occured
if result:
if trades_closed:
self.wallets.update()
def get_real_amount(self, trade: Trade, order: Dict, order_amount: float = None) -> float:
"""
Get real amount for the trade
Necessary for exchanges which charge fees in base currency (e.g. binance)
"""
if order_amount is None:
order_amount = order['amount']
# Only run for closed orders
if trade.fee_open == 0 or order['status'] == 'open':
return order_amount
# 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'])):
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)
return new_amount
# Fallback to Trades
trades = self.exchange.get_trades_for_order(trade.open_order_id, trade.pair,
trade.open_date)
if len(trades) == 0:
logger.info("Applying fee on amount for %s failed: myTrade-Dict empty found", trade)
return order_amount
amount = 0
fee_abs = 0
for exectrade in trades:
amount += exectrade['amount']
if ("fee" in exectrade and exectrade['fee'] is not None and
(exectrade['fee'].keys() >= {'currency', 'cost'})):
# 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'])):
fee_abs += exectrade['fee']['cost']
if not isclose(amount, order_amount, abs_tol=constants.MATH_CLOSE_PREC):
logger.warning(f"Amount {amount} does not match amount {trade.amount}")
raise DependencyException("Half bought? Amounts don't match")
real_amount = amount - fee_abs
if fee_abs != 0:
logger.info(f"Applying fee on amount for {trade} "
f"(from {order_amount} to {real_amount}) from Trades")
return real_amount
def update_trade_state(self, trade, action_order: dict = None):
"""
Checks trades with open orders and updates the amount if necessary
"""
# Get order details for actual price per unit
if trade.open_order_id:
# Update trade with order values
logger.info('Found open order for %s', trade)
try:
order = action_order or self.exchange.get_order(trade.open_order_id, trade.pair)
except InvalidOrderException as exception:
logger.warning('Unable to fetch order %s: %s', trade.open_order_id, exception)
return
# Try update amount (binance-fix)
try:
new_amount = self.get_real_amount(trade, order)
if not isclose(order['amount'], new_amount, abs_tol=constants.MATH_CLOSE_PREC):
order['amount'] = new_amount
# Fee was applied, so set to 0
trade.fee_open = 0
except DependencyException as exception:
logger.warning("Could not update trade amount: %s", exception)
trade.update(order)
# Updating wallets when order is closed
if not trade.is_open:
self.wallets.update()
return trades_closed
def get_sell_rate(self, pair: str, refresh: bool) -> float:
"""
Get sell rate - either using get-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 get_ticker)
for BitMex (has no bid/ask in fetch_ticker)
or remain static in any other case since it's not updating.
:return: Bid rate
"""
@ -580,7 +596,7 @@ class FreqtradeBot:
rate = order_book['bids'][0][0]
else:
rate = self.exchange.get_ticker(pair, refresh)['bid']
rate = self.exchange.fetch_ticker(pair, refresh)['bid']
return rate
def handle_trade(self, trade: Trade) -> bool:
@ -748,8 +764,8 @@ class FreqtradeBot:
Check and execute sell
"""
should_sell = self.strategy.should_sell(
trade, sell_rate, datetime.utcnow(), buy, sell,
force_stoploss=self.edge.stoploss(trade.pair) if self.edge else 0
trade, sell_rate, datetime.utcnow(), buy, sell,
force_stoploss=self.edge.stoploss(trade.pair) if self.edge else 0
)
if should_sell.sell_flag:
@ -849,6 +865,7 @@ class FreqtradeBot:
trade.amount = new_amount
# Fee was applied, so set to 0
trade.fee_open = 0
trade.recalc_open_trade_price()
except DependencyException as e:
logger.warning("Could not update trade amount: %s", e)
@ -889,6 +906,31 @@ class FreqtradeBot:
# TODO: figure out how to handle partially complete sell orders
return False
def _safe_sell_amount(self, pair: str, amount: float) -> float:
"""
Get sellable amount.
Should be trade.amount - but will fall back to the available amount if necessary.
This should cover cases where get_real_amount() was not able to update the amount
for whatever reason.
:param pair: Pair we're trying to sell
:param amount: amount we expect to be available
:return: amount to sell
:raise: DependencyException: if available balance is not within 2% of the available amount.
"""
# 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])
logger.debug(f"{pair} - Wallet: {wallet_amount} - Trade-amount: {amount}")
if wallet_amount >= amount:
return amount
elif wallet_amount > amount * 0.98:
logger.info(f"{pair} - Falling back to wallet-amount.")
return wallet_amount
else:
raise DependencyException(
f"Not enough amount to sell. Trade-amount: {amount}, Wallet: {wallet_amount}")
def execute_sell(self, trade: Trade, limit: float, sell_reason: SellType) -> None:
"""
Executes a limit sell for the given trade and limit
@ -903,7 +945,7 @@ class FreqtradeBot:
# if stoploss is on exchange and we are on dry_run mode,
# we consider the sell price stop price
if self.config.get('dry_run', False) and sell_type == 'stoploss' \
if self.config['dry_run'] and sell_type == 'stoploss' \
and self.strategy.order_types['stoploss_on_exchange']:
limit = trade.stop_loss
@ -914,15 +956,17 @@ class FreqtradeBot:
except InvalidOrderException:
logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id}")
ordertype = self.strategy.order_types[sell_type]
order_type = self.strategy.order_types[sell_type]
if sell_reason == SellType.EMERGENCY_SELL:
# Emergencysells (default to market!)
ordertype = self.strategy.order_types.get("emergencysell", "market")
order_type = self.strategy.order_types.get("emergencysell", "market")
amount = self._safe_sell_amount(trade.pair, trade.amount)
# Execute sell and update trade record
order = self.exchange.sell(pair=str(trade.pair),
ordertype=ordertype,
amount=trade.amount, rate=limit,
ordertype=order_type,
amount=amount, rate=limit,
time_in_force=self.strategy.order_time_in_force['sell']
)
@ -937,7 +981,7 @@ class FreqtradeBot:
# Lock pair for one candle to prevent immediate rebuys
self.strategy.lock_pair(trade.pair, timeframe_to_next_date(self.config['ticker_interval']))
self._notify_sell(trade, ordertype)
self._notify_sell(trade, order_type)
def _notify_sell(self, trade: Trade, order_type: str):
"""
@ -947,7 +991,7 @@ class FreqtradeBot:
profit_trade = trade.calc_profit(rate=profit_rate)
# Use cached ticker here - it was updated seconds ago.
current_rate = self.get_sell_rate(trade.pair, False)
profit_percent = trade.calc_profit_percent(profit_rate)
profit_percent = trade.calc_profit_ratio(profit_rate)
gain = "profit" if profit_percent > 0 else "loss"
msg = {
@ -962,17 +1006,101 @@ class FreqtradeBot:
'current_rate': current_rate,
'profit_amount': profit_trade,
'profit_percent': profit_percent,
'sell_reason': trade.sell_reason
'sell_reason': trade.sell_reason,
'open_date': trade.open_date,
'close_date': trade.close_date or datetime.utcnow(),
'stake_currency': self.config['stake_currency'],
}
# For regular case, when the configuration exists
if 'stake_currency' in self.config and 'fiat_display_currency' in self.config:
stake_currency = self.config['stake_currency']
fiat_currency = self.config['fiat_display_currency']
if 'fiat_display_currency' in self.config:
msg.update({
'stake_currency': stake_currency,
'fiat_currency': fiat_currency,
'fiat_currency': self.config['fiat_display_currency'],
})
# Send the message
self.rpc.send_msg(msg)
#
# Common update trade state methods
#
def update_trade_state(self, trade, action_order: dict = None):
"""
Checks trades with open orders and updates the amount if necessary
"""
# Get order details for actual price per unit
if trade.open_order_id:
# Update trade with order values
logger.info('Found open order for %s', trade)
try:
order = action_order or self.exchange.get_order(trade.open_order_id, trade.pair)
except InvalidOrderException as exception:
logger.warning('Unable to fetch order %s: %s', trade.open_order_id, exception)
return
# Try update amount (binance-fix)
try:
new_amount = self.get_real_amount(trade, order)
if not isclose(order['amount'], new_amount, abs_tol=constants.MATH_CLOSE_PREC):
order['amount'] = new_amount
# Fee was applied, so set to 0
trade.fee_open = 0
trade.recalc_open_trade_price()
except DependencyException as exception:
logger.warning("Could not update trade amount: %s", exception)
trade.update(order)
# Updating wallets when order is closed
if not trade.is_open:
self.wallets.update()
def get_real_amount(self, trade: Trade, order: Dict, order_amount: float = None) -> float:
"""
Get real amount for the trade
Necessary for exchanges which charge fees in base currency (e.g. binance)
"""
if order_amount is None:
order_amount = order['amount']
# Only run for closed orders
if trade.fee_open == 0 or order['status'] == 'open':
return order_amount
# 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'])):
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)
return new_amount
# Fallback to Trades
trades = self.exchange.get_trades_for_order(trade.open_order_id, trade.pair,
trade.open_date)
if len(trades) == 0:
logger.info("Applying fee on amount for %s failed: myTrade-Dict empty found", trade)
return order_amount
amount = 0
fee_abs = 0
for exectrade in trades:
amount += exectrade['amount']
if ("fee" in exectrade and exectrade['fee'] is not None and
(exectrade['fee'].keys() >= {'currency', 'cost'})):
# 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'])):
fee_abs += exectrade['fee']['cost']
if not isclose(amount, order_amount, abs_tol=constants.MATH_CLOSE_PREC):
logger.warning(f"Amount {amount} does not match amount {trade.amount}")
raise DependencyException("Half bought? Amounts don't match")
real_amount = amount - fee_abs
if fee_abs != 0:
logger.info(f"Applying fee on amount for {trade} "
f"(from {order_amount} to {real_amount}) from Trades")
return real_amount

2
freqtrade/loggers.py
View File

@ -5,7 +5,7 @@ from logging import Formatter
from logging.handlers import RotatingFileHandler, SysLogHandler
from typing import Any, Dict, List
from freqtrade import OperationalException
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)

6
freqtrade/main.py
View File

@ -4,6 +4,7 @@ Main Freqtrade bot script.
Read the documentation to know what cli arguments you need.
"""
from freqtrade.exceptions import FreqtradeException, OperationalException
import sys
# check min. python version
if sys.version_info < (3, 6):
@ -13,8 +14,7 @@ if sys.version_info < (3, 6):
import logging
from typing import Any, List
from freqtrade import OperationalException
from freqtrade.configuration import Arguments
from freqtrade.commands import Arguments
logger = logging.getLogger('freqtrade')
@ -50,7 +50,7 @@ def main(sysargv: List[str] = None) -> None:
except KeyboardInterrupt:
logger.info('SIGINT received, aborting ...')
return_code = 0
except OperationalException as e:
except FreqtradeException as e:
logger.error(str(e))
return_code = 2
except Exception:

102
freqtrade/optimize/__init__.py
View File

@ -1,102 +0,0 @@
import logging
from typing import Any, Dict
from freqtrade import DependencyException, constants, OperationalException
from freqtrade.state import RunMode
from freqtrade.utils import setup_utils_configuration
logger = logging.getLogger(__name__)
def setup_configuration(args: Dict[str, Any], method: RunMode) -> Dict[str, Any]:
"""
Prepare the configuration for the Hyperopt module
:param args: Cli args from Arguments()
:return: Configuration
"""
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)
return config
def start_backtesting(args: Dict[str, Any]) -> None:
"""
Start Backtesting script
:param args: Cli args from Arguments()
:return: None
"""
# Import here to avoid loading backtesting module when it's not used
from freqtrade.optimize.backtesting import Backtesting
# Initialize configuration
config = setup_configuration(args, RunMode.BACKTEST)
logger.info('Starting freqtrade in Backtesting mode')
# Initialize backtesting object
backtesting = Backtesting(config)
backtesting.start()
def start_hyperopt(args: Dict[str, Any]) -> None:
"""
Start hyperopt script
:param args: Cli args from Arguments()
:return: None
"""
# Import here to avoid loading hyperopt module when it's not used
try:
from filelock import FileLock, Timeout
from freqtrade.optimize.hyperopt import Hyperopt
except ImportError as e:
raise OperationalException(
f"{e}. Please ensure that the hyperopt dependencies are installed.") from e
# Initialize configuration
config = setup_configuration(args, RunMode.HYPEROPT)
logger.info('Starting freqtrade in Hyperopt mode')
lock = FileLock(Hyperopt.get_lock_filename(config))
try:
with lock.acquire(timeout=1):
# Remove noisy log messages
logging.getLogger('hyperopt.tpe').setLevel(logging.WARNING)
logging.getLogger('filelock').setLevel(logging.WARNING)
# Initialize backtesting object
hyperopt = Hyperopt(config)
hyperopt.start()
except Timeout:
logger.info("Another running instance of freqtrade Hyperopt detected.")
logger.info("Simultaneous execution of multiple Hyperopt commands is not supported. "
"Hyperopt module is resource hungry. Please run your Hyperopt sequentially "
"or on separate machines.")
logger.info("Quitting now.")
# TODO: return False here in order to help freqtrade to exit
# with non-zero exit code...
# Same in Edge and Backtesting start() functions.
def start_edge(args: Dict[str, Any]) -> None:
"""
Start Edge script
:param args: Cli args from Arguments()
:return: None
"""
from freqtrade.optimize.edge_cli import EdgeCli
# Initialize configuration
config = setup_configuration(args, RunMode.EDGE)
logger.info('Starting freqtrade in Edge mode')
# Initialize Edge object
edge_cli = EdgeCli(config)
edge_cli.start()

247
freqtrade/optimize/backtesting.py
View File

@ -10,15 +10,17 @@ from pathlib import Path
from typing import Any, Dict, List, NamedTuple, Optional
from pandas import DataFrame
from tabulate import tabulate
from freqtrade import OperationalException
from freqtrade.configuration import (TimeRange, remove_credentials,
validate_config_consistency)
from freqtrade.data import history
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.misc import file_dump_json
from freqtrade.optimize.optimize_reports import (
generate_text_table, generate_text_table_sell_reason,
generate_text_table_strategy)
from freqtrade.persistence import Trade
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.state import RunMode
@ -60,12 +62,12 @@ class Backtesting:
# Reset keys for backtesting
remove_credentials(self.config)
self.strategylist: List[IStrategy] = []
self.exchange = ExchangeResolver(self.config['exchange']['name'], self.config).exchange
self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
if config.get('fee'):
self.fee = config['fee']
else:
self.fee = self.exchange.get_fee()
self.fee = self.exchange.get_fee(symbol=self.config['exchange']['pair_whitelist'][0])
if self.config.get('runmode') != RunMode.HYPEROPT:
self.dataprovider = DataProvider(self.config, self.exchange)
@ -75,19 +77,19 @@ class Backtesting:
for strat in list(self.config['strategy_list']):
stratconf = deepcopy(self.config)
stratconf['strategy'] = strat
self.strategylist.append(StrategyResolver(stratconf).strategy)
self.strategylist.append(StrategyResolver.load_strategy(stratconf))
validate_config_consistency(stratconf)
else:
# No strategy list specified, only one strategy
self.strategylist.append(StrategyResolver(self.config).strategy)
self.strategylist.append(StrategyResolver.load_strategy(self.config))
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`")
self.timeframe = str(self.config.get('ticker_interval'))
self.timeframe_mins = timeframe_to_minutes(self.timeframe)
self.timeframe_min = timeframe_to_minutes(self.timeframe)
# Get maximum required startup period
self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
@ -109,7 +111,7 @@ class Backtesting:
'timerange') is None else str(self.config.get('timerange')))
data = history.load_data(
datadir=Path(self.config['datadir']),
datadir=self.config['datadir'],
pairs=self.config['exchange']['pair_whitelist'],
timeframe=self.timeframe,
timerange=timerange,
@ -117,7 +119,7 @@ class Backtesting:
fail_without_data=True,
)
min_date, max_date = history.get_timeframe(data)
min_date, max_date = history.get_timerange(data)
logger.info(
'Loading data from %s up to %s (%s days)..',
@ -129,94 +131,6 @@ class Backtesting:
return data, timerange
def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame,
skip_nan: bool = False) -> str:
"""
Generates and returns a text table for the given backtest data and the results dataframe
:return: pretty printed table with tabulate as str
"""
stake_currency = str(self.config.get('stake_currency'))
max_open_trades = self.config.get('max_open_trades')
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
tabular_data = []
headers = ['pair', 'buy count', 'avg profit %', 'cum profit %',
'tot profit ' + stake_currency, 'tot profit %', 'avg duration',
'profit', 'loss']
for pair in data:
result = results[results.pair == pair]
if skip_nan and result.profit_abs.isnull().all():
continue
tabular_data.append([
pair,
len(result.index),
result.profit_percent.mean() * 100.0,
result.profit_percent.sum() * 100.0,
result.profit_abs.sum(),
result.profit_percent.sum() * 100.0 / max_open_trades,
str(timedelta(
minutes=round(result.trade_duration.mean()))) if not result.empty else '0:00',
len(result[result.profit_abs > 0]),
len(result[result.profit_abs < 0])
])
# Append Total
tabular_data.append([
'TOTAL',
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_percent.sum() * 100.0,
results.profit_abs.sum(),
results.profit_percent.sum() * 100.0 / max_open_trades,
str(timedelta(
minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
len(results[results.profit_abs > 0]),
len(results[results.profit_abs < 0])
])
# 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
def _generate_text_table_sell_reason(self, data: Dict[str, Dict], results: DataFrame) -> str:
"""
Generate small table outlining Backtest results
"""
tabular_data = []
headers = ['Sell Reason', 'Count']
for reason, count in results['sell_reason'].value_counts().iteritems():
tabular_data.append([reason.value, count])
return tabulate(tabular_data, headers=headers, tablefmt="pipe")
def _generate_text_table_strategy(self, all_results: dict) -> str:
"""
Generate summary table per strategy
"""
stake_currency = str(self.config.get('stake_currency'))
max_open_trades = self.config.get('max_open_trades')
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
tabular_data = []
headers = ['Strategy', 'buy count', 'avg profit %', 'cum profit %',
'tot profit ' + stake_currency, 'tot profit %', 'avg duration',
'profit', 'loss']
for strategy, results in all_results.items():
tabular_data.append([
strategy,
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_percent.sum() * 100.0,
results.profit_abs.sum(),
results.profit_percent.sum() * 100.0 / max_open_trades,
str(timedelta(
minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
len(results[results.profit_abs > 0]),
len(results[results.profit_abs < 0])
])
# 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
def _store_backtest_result(self, recordfilename: Path, results: DataFrame,
strategyname: Optional[str] = None) -> None:
@ -261,6 +175,45 @@ class Backtesting:
ticker[pair] = [x for x in ticker_data.itertuples()]
return ticker
def _get_close_rate(self, sell_row, trade: Trade, sell, trade_dur) -> float:
"""
Get close rate for backtesting result
"""
# Special handling if high or low hit STOP_LOSS or ROI
if sell.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
# Set close_rate to stoploss
return trade.stop_loss
elif sell.sell_type == (SellType.ROI):
roi_entry, roi = self.strategy.min_roi_reached_entry(trade_dur)
if roi is not None:
if roi == -1 and roi_entry % self.timeframe_min == 0:
# When forceselling with ROI=-1, the roi time will always be equal to trade_dur.
# If that entry is a multiple of the timeframe (so on candle open)
# - we'll use open instead of close
return sell_row.open
# - (Expected abs profit + open_rate + open_fee) / (fee_close -1)
close_rate = - (trade.open_rate * roi + trade.open_rate *
(1 + trade.fee_open)) / (trade.fee_close - 1)
if (trade_dur > 0 and trade_dur == roi_entry
and roi_entry % self.timeframe_min == 0
and sell_row.open > close_rate):
# new ROI entry came into effect.
# use Open rate if open_rate > calculated sell rate
return sell_row.open
# Use the maximum between close_rate and low as we
# cannot sell outside of a candle.
# Applies when a new ROI setting comes in place and the whole candle is above that.
return max(close_rate, sell_row.low)
else:
# This should not be reached...
return sell_row.open
else:
return sell_row.open
def _get_sell_trade_entry(
self, pair: str, buy_row: DataFrame,
partial_ticker: List, trade_count_lock: Dict,
@ -287,29 +240,10 @@ class Backtesting:
sell_row.sell, low=sell_row.low, high=sell_row.high)
if sell.sell_flag:
trade_dur = int((sell_row.date - buy_row.date).total_seconds() // 60)
# Special handling if high or low hit STOP_LOSS or ROI
if sell.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
# Set close_rate to stoploss
closerate = trade.stop_loss
elif sell.sell_type == (SellType.ROI):
roi = self.strategy.min_roi_reached_entry(trade_dur)
if roi is not None:
# - (Expected abs profit + open_rate + open_fee) / (fee_close -1)
closerate = - (trade.open_rate * roi + trade.open_rate *
(1 + trade.fee_open)) / (trade.fee_close - 1)
# Use the maximum between closerate and low as we
# cannot sell outside of a candle.
# Applies when using {"xx": -1} as roi to force sells after xx minutes
closerate = max(closerate, sell_row.low)
else:
# This should not be reached...
closerate = sell_row.open
else:
closerate = sell_row.open
closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
return BacktestResult(pair=pair,
profit_percent=trade.calc_profit_percent(rate=closerate),
profit_percent=trade.calc_profit_ratio(rate=closerate),
profit_abs=trade.calc_profit(rate=closerate),
open_time=buy_row.date,
close_time=sell_row.date,
@ -325,7 +259,7 @@ class Backtesting:
# no sell condition found - trade stil open at end of backtest period
sell_row = partial_ticker[-1]
bt_res = BacktestResult(pair=pair,
profit_percent=trade.calc_profit_percent(rate=sell_row.open),
profit_percent=trade.calc_profit_ratio(rate=sell_row.open),
profit_abs=trade.calc_profit(rate=sell_row.open),
open_time=buy_row.date,
close_time=sell_row.date,
@ -345,30 +279,28 @@ class Backtesting:
return bt_res
return None
def backtest(self, args: Dict) -> DataFrame:
def backtest(self, processed: Dict, stake_amount: float,
start_date, end_date,
max_open_trades: int = 0, position_stacking: bool = False) -> DataFrame:
"""
Implements backtesting functionality
Implement backtesting functionality
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
Of course try to not have ugly code. By some accessor are sometime slower than functions.
Avoid, logging on this method
Avoid extensive logging in this method and functions it calls.
:param args: a dict containing:
stake_amount: btc amount to use for each trade
processed: a processed dictionary with format {pair, data}
max_open_trades: maximum number of concurrent trades (default: 0, disabled)
position_stacking: do we allow position stacking? (default: False)
:return: DataFrame
:param processed: a processed dictionary with format {pair, data}
:param stake_amount: amount to use for each trade
:param start_date: backtesting timerange start datetime
:param end_date: backtesting timerange end datetime
:param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
:param position_stacking: do we allow position stacking?
:return: DataFrame with trades (results of backtesting)
"""
# Arguments are long and noisy, so this is commented out.
# Uncomment if you need to debug the backtest() method.
# logger.debug(f"Start backtest, args: {args}")
processed = args['processed']
stake_amount = args['stake_amount']
max_open_trades = args.get('max_open_trades', 0)
position_stacking = args.get('position_stacking', False)
start_date = args['start_date']
end_date = args['end_date']
logger.debug(f"Run backtest, stake_amount: {stake_amount}, "
f"start_date: {start_date}, end_date: {end_date}, "
f"max_open_trades: {max_open_trades}, position_stacking: {position_stacking}"
)
trades = []
trade_count_lock: Dict = {}
@ -378,7 +310,7 @@ class Backtesting:
lock_pair_until: Dict = {}
# Indexes per pair, so some pairs are allowed to have a missing start.
indexes: Dict = {}
tmp = start_date + timedelta(minutes=self.timeframe_mins)
tmp = start_date + timedelta(minutes=self.timeframe_min)
# Loop timerange and get candle for each pair at that point in time
while tmp < end_date:
@ -430,23 +362,26 @@ class Backtesting:
lock_pair_until[pair] = end_date.datetime
# Move time one configured time_interval ahead.
tmp += timedelta(minutes=self.timeframe_mins)
tmp += timedelta(minutes=self.timeframe_min)
return DataFrame.from_records(trades, columns=BacktestResult._fields)
def start(self) -> None:
"""
Run a backtesting end-to-end
Run backtesting end-to-end
:return: None
"""
data: Dict[str, Any] = {}
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
max_open_trades = self.config['max_open_trades']
else:
logger.info('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
max_open_trades = 0
position_stacking = self.config.get('position_stacking', False)
data, timerange = self.load_bt_data()
@ -461,7 +396,7 @@ class Backtesting:
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
preprocessed[pair] = history.trim_dataframe(df, timerange)
min_date, max_date = history.get_timeframe(preprocessed)
min_date, max_date = history.get_timerange(preprocessed)
logger.info(
'Backtesting with data from %s up to %s (%s days)..',
@ -469,14 +404,12 @@ class Backtesting:
)
# Execute backtest and print results
all_results[self.strategy.get_strategy_name()] = self.backtest(
{
'stake_amount': self.config.get('stake_amount'),
'processed': preprocessed,
'max_open_trades': max_open_trades,
'position_stacking': self.config.get('position_stacking', False),
'start_date': min_date,
'end_date': max_date,
}
processed=preprocessed,
stake_amount=self.config['stake_amount'],
start_date=min_date,
end_date=max_date,
max_open_trades=max_open_trades,
position_stacking=position_stacking,
)
for strategy, results in all_results.items():
@ -487,16 +420,24 @@ class Backtesting:
print(f"Result for strategy {strategy}")
print(' BACKTESTING REPORT '.center(133, '='))
print(self._generate_text_table(data, results))
print(generate_text_table(data,
stake_currency=self.config['stake_currency'],
max_open_trades=self.config['max_open_trades'],
results=results))
print(' SELL REASON STATS '.center(133, '='))
print(self._generate_text_table_sell_reason(data, results))
print(generate_text_table_sell_reason(data, results))
print(' LEFT OPEN TRADES REPORT '.center(133, '='))
print(self._generate_text_table(data, results.loc[results.open_at_end], True))
print(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))
print()
if len(all_results) > 1:
# Print Strategy summary table
print(' Strategy Summary '.center(133, '='))
print(self._generate_text_table_strategy(all_results))
print(generate_text_table_strategy(self.config['stake_currency'],
self.config['max_open_trades'],
all_results=all_results))
print('\nFor more details, please look at the detail tables above')

41
freqtrade/optimize/edge_cli.py
View File

@ -6,14 +6,12 @@ This module contains the edge backtesting interface
import logging
from typing import Any, Dict
from tabulate import tabulate
from freqtrade import constants
from freqtrade.configuration import (TimeRange, remove_credentials,
validate_config_consistency)
from freqtrade.edge import Edge
from freqtrade.exchange import Exchange
from freqtrade.resolvers import StrategyResolver
from freqtrade.optimize.optimize_reports import generate_edge_table
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
logger = logging.getLogger(__name__)
@ -33,8 +31,8 @@ class EdgeCli:
# Reset keys for edge
remove_credentials(self.config)
self.config['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
self.exchange = Exchange(self.config)
self.strategy = StrategyResolver(self.config).strategy
self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
self.strategy = StrategyResolver.load_strategy(self.config)
validate_config_consistency(self.config)
@ -42,38 +40,11 @@ class EdgeCli:
# Set refresh_pairs to false for edge-cli (it must be true for edge)
self.edge._refresh_pairs = False
self.timerange = TimeRange.parse_timerange(None if self.config.get(
self.edge._timerange = TimeRange.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
self.edge._timerange = self.timerange
def _generate_edge_table(self, results: dict) -> str:
floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', '.d')
tabular_data = []
headers = ['pair', 'stoploss', 'win rate', 'risk reward ratio',
'required risk reward', 'expectancy', 'total number of trades',
'average duration (min)']
for result in results.items():
if result[1].nb_trades > 0:
tabular_data.append([
result[0],
result[1].stoploss,
result[1].winrate,
result[1].risk_reward_ratio,
result[1].required_risk_reward,
result[1].expectancy,
result[1].nb_trades,
round(result[1].avg_trade_duration)
])
# 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
def start(self) -> None:
result = self.edge.calculate()
if result:
print('') # blank line for readability
print(self._generate_edge_table(self.edge._cached_pairs))
print(generate_edge_table(self.edge._cached_pairs))

64
freqtrade/optimize/hyperopt.py
View File

@ -6,7 +6,9 @@ This module contains the hyperopt logic
import locale
import logging
import random
import sys
import warnings
from collections import OrderedDict
from operator import itemgetter
from pathlib import Path
@ -19,19 +21,24 @@ 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 skopt import Optimizer
from skopt.space import Dimension
from freqtrade import OperationalException
from freqtrade.data.history import get_timeframe, trim_dataframe
from freqtrade.data.history import get_timerange, trim_dataframe
from freqtrade.exceptions import OperationalException
from freqtrade.misc import plural, round_dict
from freqtrade.optimize.backtesting import Backtesting
# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F4
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F4
from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
from freqtrade.resolvers.hyperopt_resolver import (HyperOptLossResolver,
HyperOptResolver)
# Suppress scikit-learn FutureWarnings from skopt
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=FutureWarning)
from skopt import Optimizer
from skopt.space import Dimension
logger = logging.getLogger(__name__)
@ -57,9 +64,9 @@ class Hyperopt:
self.backtesting = Backtesting(self.config)
self.custom_hyperopt = HyperOptResolver(self.config).hyperopt
self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
self.custom_hyperoptloss = HyperOptLossResolver(self.config).hyperoptloss
self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config)
self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
self.trials_file = (self.config['user_data_dir'] /
@ -177,8 +184,7 @@ class Hyperopt:
result['stoploss'] = {p.name: params.get(p.name)
for p in self.hyperopt_space('stoploss')}
if self.has_space('trailing'):
result['trailing'] = {p.name: params.get(p.name)
for p in self.hyperopt_space('trailing')}
result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)
return result
@ -353,27 +359,25 @@ class Hyperopt:
self.backtesting.strategy.stoploss = params_dict['stoploss']
if self.has_space('trailing'):
self.backtesting.strategy.trailing_stop = params_dict['trailing_stop']
self.backtesting.strategy.trailing_stop_positive = \
params_dict['trailing_stop_positive']
d = self.custom_hyperopt.generate_trailing_params(params_dict)
self.backtesting.strategy.trailing_stop = d['trailing_stop']
self.backtesting.strategy.trailing_stop_positive = d['trailing_stop_positive']
self.backtesting.strategy.trailing_stop_positive_offset = \
params_dict['trailing_stop_positive_offset']
d['trailing_stop_positive_offset']
self.backtesting.strategy.trailing_only_offset_is_reached = \
params_dict['trailing_only_offset_is_reached']
d['trailing_only_offset_is_reached']
processed = load(self.tickerdata_pickle)
min_date, max_date = get_timeframe(processed)
min_date, max_date = get_timerange(processed)
backtesting_results = self.backtesting.backtest(
{
'stake_amount': self.config['stake_amount'],
'processed': processed,
'max_open_trades': self.max_open_trades,
'position_stacking': self.position_stacking,
'start_date': min_date,
'end_date': max_date,
}
processed=processed,
stake_amount=self.config['stake_amount'],
start_date=min_date,
end_date=max_date,
max_open_trades=self.max_open_trades,
position_stacking=self.position_stacking,
)
return self._get_results_dict(backtesting_results, min_date, max_date,
params_dict, params_details)
@ -421,7 +425,7 @@ class Hyperopt:
f"Avg profit {results_metrics['avg_profit']: 6.2f}%. "
f"Total profit {results_metrics['total_profit']: 11.8f} {stake_cur} "
f"({results_metrics['profit']: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). "
f"Avg duration {results_metrics['duration']:5.1f} mins."
f"Avg duration {results_metrics['duration']:5.1f} min."
).encode(locale.getpreferredencoding(), 'replace').decode('utf-8')
def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
@ -431,7 +435,7 @@ class Hyperopt:
acq_optimizer="auto",
n_initial_points=INITIAL_POINTS,
acq_optimizer_kwargs={'n_jobs': cpu_count},
random_state=self.config.get('hyperopt_random_state', None),
random_state=self.random_state,
)
def fix_optimizer_models_list(self):
@ -470,7 +474,13 @@ class Hyperopt:
logger.info(f"Loaded {len(trials)} previous evaluations from disk.")
return trials
def _set_random_state(self, random_state: Optional[int]) -> int:
return random_state or random.randint(1, 2**16 - 1)
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}")
data, timerange = self.backtesting.load_bt_data()
preprocessed = self.backtesting.strategy.tickerdata_to_dataframe(data)
@ -478,7 +488,7 @@ class Hyperopt:
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
preprocessed[pair] = trim_dataframe(df, timerange)
min_date, max_date = get_timeframe(data)
min_date, max_date = get_timerange(data)
logger.info(
'Hyperopting with data from %s up to %s (%s days)..',

36
freqtrade/optimize/hyperopt_interface.py
View File

@ -4,17 +4,15 @@ This module defines the interface to apply for hyperopt
"""
import logging
import math
from abc import ABC
from typing import Dict, Any, Callable, List
from typing import Any, Callable, Dict, List
from skopt.space import Categorical, Dimension, Integer, Real
from freqtrade import OperationalException
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.misc import round_dict
logger = logging.getLogger(__name__)
@ -106,7 +104,7 @@ class IHyperOpt(ABC):
roi_t_alpha = 1.0
roi_p_alpha = 1.0
timeframe_mins = timeframe_to_minutes(IHyperOpt.ticker_interval)
timeframe_min = timeframe_to_minutes(IHyperOpt.ticker_interval)
# We define here limits for the ROI space parameters automagically adapted to the
# timeframe used by the bot:
@ -117,8 +115,8 @@ class IHyperOpt(ABC):
#
# The scaling is designed so that it maps exactly to the legacy Freqtrade roi_space()
# method for the 5m ticker interval.
roi_t_scale = timeframe_mins / 5
roi_p_scale = math.log1p(timeframe_mins) / math.log1p(5)
roi_t_scale = timeframe_min / 5
roi_p_scale = math.log1p(timeframe_min) / math.log1p(5)
roi_limits = {
'roi_t1_min': int(10 * roi_t_scale * roi_t_alpha),
'roi_t1_max': int(120 * roi_t_scale * roi_t_alpha),
@ -174,6 +172,19 @@ class IHyperOpt(ABC):
Real(-0.35, -0.02, name='stoploss'),
]
@staticmethod
def generate_trailing_params(params: Dict) -> Dict:
"""
Create dict with trailing stop parameters.
"""
return {
'trailing_stop': params['trailing_stop'],
'trailing_stop_positive': params['trailing_stop_positive'],
'trailing_stop_positive_offset': (params['trailing_stop_positive'] +
params['trailing_stop_positive_offset_p1']),
'trailing_only_offset_is_reached': params['trailing_only_offset_is_reached'],
}
@staticmethod
def trailing_space() -> List[Dimension]:
"""
@ -190,8 +201,15 @@ class IHyperOpt(ABC):
# other 'trailing' hyperspace parameters.
Categorical([True], name='trailing_stop'),
Real(0.02, 0.35, name='trailing_stop_positive'),
Real(0.01, 0.1, name='trailing_stop_positive_offset'),
Real(0.01, 0.35, name='trailing_stop_positive'),
# 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
# so this intermediate parameter is used as the value of the difference between
# them. The value of the 'trailing_stop_positive_offset' is constructed in the
# generate_trailing_params() method.
# # This is similar to the hyperspace dimensions used for constructing the ROI tables.
Real(0.001, 0.1, name='trailing_stop_positive_offset_p1'),
Categorical([True, False], name='trailing_only_offset_is_reached'),
]

135
freqtrade/optimize/optimize_reports.py Normal file
View File

@ -0,0 +1,135 @@
from datetime import timedelta
from typing import Dict
from pandas import DataFrame
from tabulate import tabulate
def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_trades: int,
results: DataFrame, skip_nan: bool = False) -> str:
"""
Generates and returns a text table for the given backtest data and the results dataframe
:param data: Dict of <pair: dataframe> containing data that was used during backtesting.
:param stake_currency: stake-currency - used to correctly name headers
:param max_open_trades: Maximum allowed open trades
:param results: Dataframe containing the backtest results
:param skip_nan: Print "left open" open trades
:return: pretty printed table with tabulate as string
"""
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
tabular_data = []
headers = ['pair', 'buy count', 'avg profit %', 'cum profit %',
f'tot profit {stake_currency}', 'tot profit %', 'avg duration',
'profit', 'loss']
for pair in data:
result = results[results.pair == pair]
if skip_nan and result.profit_abs.isnull().all():
continue
tabular_data.append([
pair,
len(result.index),
result.profit_percent.mean() * 100.0,
result.profit_percent.sum() * 100.0,
result.profit_abs.sum(),
result.profit_percent.sum() * 100.0 / max_open_trades,
str(timedelta(
minutes=round(result.trade_duration.mean()))) if not result.empty else '0:00',
len(result[result.profit_abs > 0]),
len(result[result.profit_abs < 0])
])
# Append Total
tabular_data.append([
'TOTAL',
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_percent.sum() * 100.0,
results.profit_abs.sum(),
results.profit_percent.sum() * 100.0 / max_open_trades,
str(timedelta(
minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
len(results[results.profit_abs > 0]),
len(results[results.profit_abs < 0])
])
# 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
def generate_text_table_sell_reason(data: Dict[str, Dict], results: DataFrame) -> str:
"""
Generate small table outlining Backtest results
:param data: Dict of <pair: dataframe> containing data that was used during backtesting.
:param results: Dataframe containing the backtest results
:return: pretty printed table with tabulate as string
"""
tabular_data = []
headers = ['Sell Reason', 'Count', 'Profit', 'Loss', 'Profit %']
for reason, count in results['sell_reason'].value_counts().iteritems():
result = results.loc[results['sell_reason'] == reason]
profit = len(result[result['profit_abs'] >= 0])
loss = len(result[result['profit_abs'] < 0])
profit_mean = round(result['profit_percent'].mean() * 100.0, 2)
tabular_data.append([reason.value, count, profit, loss, profit_mean])
return tabulate(tabular_data, headers=headers, tablefmt="pipe")
def generate_text_table_strategy(stake_currency: str, max_open_trades: str,
all_results: Dict) -> str:
"""
Generate summary table per strategy
:param stake_currency: stake-currency - used to correctly name headers
:param max_open_trades: Maximum allowed open trades used for backtest
:param all_results: Dict of <Strategyname: BacktestResult> containing results for all strategies
:return: pretty printed table with tabulate as string
"""
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
tabular_data = []
headers = ['Strategy', 'buy count', 'avg profit %', 'cum profit %',
f'tot profit {stake_currency}', 'tot profit %', 'avg duration',
'profit', 'loss']
for strategy, results in all_results.items():
tabular_data.append([
strategy,
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_percent.sum() * 100.0,
results.profit_abs.sum(),
results.profit_percent.sum() * 100.0 / max_open_trades,
str(timedelta(
minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
len(results[results.profit_abs > 0]),
len(results[results.profit_abs < 0])
])
# 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
def generate_edge_table(results: dict) -> str:
floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', '.d')
tabular_data = []
headers = ['pair', 'stoploss', 'win rate', 'risk reward ratio',
'required risk reward', 'expectancy', 'total number of trades',
'average duration (min)']
for result in results.items():
if result[1].nb_trades > 0:
tabular_data.append([
result[0],
result[1].stoploss,
result[1].winrate,
result[1].risk_reward_ratio,
result[1].required_risk_reward,
result[1].expectancy,
result[1].nb_trades,
round(result[1].avg_trade_duration)
])
# 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

4
freqtrade/pairlist/PrecisionFilter.py
View File

@ -35,8 +35,8 @@ class PrecisionFilter(IPairList):
"""
stop_price = ticker['ask'] * stoploss
# Adjust stop-prices to precision
sp = self._exchange.symbol_price_prec(ticker["symbol"], stop_price)
stop_gap_price = self._exchange.symbol_price_prec(ticker["symbol"], stop_price * 0.99)
sp = self._exchange.price_to_precision(ticker["symbol"], stop_price)
stop_gap_price = self._exchange.price_to_precision(ticker["symbol"], stop_price * 0.99)
logger.debug(f"{ticker['symbol']} - {sp} : {stop_gap_price}")
if sp <= stop_gap_price:
logger.info(f"Removed {ticker['symbol']} from whitelist, "

2
freqtrade/pairlist/VolumePairList.py
View File

@ -8,7 +8,7 @@ import logging
from datetime import datetime
from typing import Dict, List
from freqtrade import OperationalException
from freqtrade.exceptions import OperationalException
from freqtrade.pairlist.IPairList import IPairList
logger = logging.getLogger(__name__)

19
freqtrade/pairlist/pairlistmanager.py
View File

@ -4,11 +4,12 @@ Static List provider
Provides lists as configured in config.json
"""
from cachetools import TTLCache, cached
import logging
from typing import Dict, List
from freqtrade import OperationalException
from cachetools import TTLCache, cached
from freqtrade.exceptions import OperationalException
from freqtrade.pairlist.IPairList import IPairList
from freqtrade.resolvers import PairListResolver
@ -28,13 +29,13 @@ class PairListManager():
if 'method' not in pl:
logger.warning(f"No method in {pl}")
continue
pairl = PairListResolver(pl.get('method'),
exchange=exchange,
pairlistmanager=self,
config=config,
pairlistconfig=pl,
pairlist_pos=len(self._pairlists)
).pairlist
pairl = PairListResolver.load_pairlist(pl.get('method'),
exchange=exchange,
pairlistmanager=self,
config=config,
pairlistconfig=pl,
pairlist_pos=len(self._pairlists)
)
self._tickers_needed = pairl.needstickers or self._tickers_needed
self._pairlists.append(pairl)

63
freqtrade/persistence.py
View File

@ -16,7 +16,7 @@ from sqlalchemy.orm.scoping import scoped_session
from sqlalchemy.orm.session import sessionmaker
from sqlalchemy.pool import StaticPool
from freqtrade import OperationalException
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
@ -86,7 +86,7 @@ def check_migrate(engine) -> None:
logger.debug(f'trying {table_back_name}')
# Check for latest column
if not has_column(cols, 'stop_loss_pct'):
if not has_column(cols, 'open_trade_price'):
logger.info(f'Running database migration - backup available as {table_back_name}')
fee_open = get_column_def(cols, 'fee_open', 'fee')
@ -104,6 +104,8 @@ def check_migrate(engine) -> None:
sell_reason = get_column_def(cols, 'sell_reason', 'null')
strategy = get_column_def(cols, 'strategy', 'null')
ticker_interval = get_column_def(cols, 'ticker_interval', 'null')
open_trade_price = get_column_def(cols, 'open_trade_price',
f'amount * open_rate * (1 + {fee_open})')
# Schema migration necessary
engine.execute(f"alter table trades rename to {table_back_name}")
@ -121,7 +123,7 @@ def check_migrate(engine) -> None:
stop_loss, stop_loss_pct, initial_stop_loss, initial_stop_loss_pct,
stoploss_order_id, stoploss_last_update,
max_rate, min_rate, sell_reason, strategy,
ticker_interval
ticker_interval, open_trade_price
)
select id, lower(exchange),
case
@ -140,7 +142,8 @@ def check_migrate(engine) -> None:
{initial_stop_loss_pct} initial_stop_loss_pct,
{stoploss_order_id} stoploss_order_id, {stoploss_last_update} stoploss_last_update,
{max_rate} max_rate, {min_rate} min_rate, {sell_reason} sell_reason,
{strategy} strategy, {ticker_interval} ticker_interval
{strategy} strategy, {ticker_interval} ticker_interval,
{open_trade_price} open_trade_price
from {table_back_name}
""")
@ -182,6 +185,8 @@ class Trade(_DECL_BASE):
fee_close = Column(Float, nullable=False, default=0.0)
open_rate = Column(Float)
open_rate_requested = Column(Float)
# open_trade_price - calcuated via _calc_open_trade_price
open_trade_price = Column(Float)
close_rate = Column(Float)
close_rate_requested = Column(Float)
close_profit = Column(Float)
@ -210,6 +215,10 @@ class Trade(_DECL_BASE):
strategy = Column(String, nullable=True)
ticker_interval = Column(Integer, nullable=True)
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.recalc_open_trade_price()
def __repr__(self):
open_since = self.open_date.strftime('%Y-%m-%d %H:%M:%S') if self.is_open else 'closed'
@ -302,6 +311,7 @@ class Trade(_DECL_BASE):
# Update open rate and actual amount
self.open_rate = Decimal(order['price'])
self.amount = Decimal(order['amount'])
self.recalc_open_trade_price()
logger.info('%s_BUY has been fulfilled for %s.', order_type.upper(), self)
self.open_order_id = None
elif order_type in ('market', 'limit') and order['side'] == 'sell':
@ -322,7 +332,7 @@ class Trade(_DECL_BASE):
and marks trade as closed
"""
self.close_rate = Decimal(rate)
self.close_profit = self.calc_profit_percent()
self.close_profit = self.calc_profit_ratio()
self.close_date = datetime.utcnow()
self.is_open = False
self.open_order_id = None
@ -331,31 +341,36 @@ class Trade(_DECL_BASE):
self
)
def calc_open_trade_price(self, fee: Optional[float] = None) -> float:
def _calc_open_trade_price(self) -> float:
"""
Calculate the open_rate including fee.
:param fee: fee to use on the open rate (optional).
If rate is not set self.fee will be used
Calculate the open_rate including open_fee.
:return: Price in of the open trade incl. Fees
"""
buy_trade = (Decimal(self.amount) * Decimal(self.open_rate))
fees = buy_trade * Decimal(fee or self.fee_open)
buy_trade = Decimal(self.amount) * Decimal(self.open_rate)
fees = buy_trade * Decimal(self.fee_open)
return float(buy_trade + fees)
def recalc_open_trade_price(self) -> None:
"""
Recalculate open_trade_price.
Must be called whenever open_rate or fee_open is changed.
"""
self.open_trade_price = self._calc_open_trade_price()
def calc_close_trade_price(self, rate: Optional[float] = None,
fee: Optional[float] = None) -> float:
"""
Calculate the close_rate including fee
:param fee: fee to use on the close rate (optional).
If rate is not set self.fee will be used
If rate is not set self.fee will be used
:param rate: rate to compare with (optional).
If rate is not set self.close_rate will be used
If rate is not set self.close_rate will be used
:return: Price in BTC of the open trade
"""
if rate is None and not self.close_rate:
return 0.0
sell_trade = (Decimal(self.amount) * Decimal(rate or self.close_rate))
sell_trade = Decimal(self.amount) * Decimal(rate or self.close_rate)
fees = sell_trade * Decimal(fee or self.fee_close)
return float(sell_trade - fees)
@ -364,34 +379,32 @@ class Trade(_DECL_BASE):
"""
Calculate the absolute profit in stake currency between Close and Open trade
:param fee: fee to use on the close rate (optional).
If rate is not set self.fee will be used
If rate is not set self.fee will be used
:param rate: close rate to compare with (optional).
If rate is not set self.close_rate will be used
If rate is not set self.close_rate will be used
:return: profit in stake currency as float
"""
open_trade_price = self.calc_open_trade_price()
close_trade_price = self.calc_close_trade_price(
rate=(rate or self.close_rate),
fee=(fee or self.fee_close)
)
profit = close_trade_price - open_trade_price
profit = close_trade_price - self.open_trade_price
return float(f"{profit:.8f}")
def calc_profit_percent(self, rate: Optional[float] = None,
fee: Optional[float] = None) -> float:
def calc_profit_ratio(self, rate: Optional[float] = None,
fee: Optional[float] = None) -> float:
"""
Calculates the profit in percentage (including fee).
Calculates the profit as ratio (including fee).
:param rate: rate to compare with (optional).
If rate is not set self.close_rate will be used
If rate is not set self.close_rate will be used
:param fee: fee to use on the close rate (optional).
:return: profit in percentage as float
:return: profit ratio as float
"""
open_trade_price = self.calc_open_trade_price()
close_trade_price = self.calc_close_trade_price(
rate=(rate or self.close_rate),
fee=(fee or self.fee_close)
)
profit_percent = (close_trade_price / open_trade_price) - 1
profit_percent = (close_trade_price / self.open_trade_price) - 1
return float(f"{profit_percent:.8f}")
@staticmethod

139
freqtrade/plot/plotting.py
View File

@ -37,7 +37,7 @@ def init_plotscript(config):
timerange = TimeRange.parse_timerange(config.get("timerange"))
tickers = history.load_data(
datadir=Path(str(config.get("datadir"))),
datadir=config.get("datadir"),
pairs=pairs,
timeframe=config.get('ticker_interval', '5m'),
timerange=timerange,
@ -54,21 +54,27 @@ def init_plotscript(config):
}
def add_indicators(fig, row, indicators: List[str], data: pd.DataFrame) -> make_subplots:
def add_indicators(fig, row, indicators: Dict[str, Dict], data: pd.DataFrame) -> make_subplots:
"""
Generator all the indicator selected by the user for a specific row
Generate all the indicators selected by the user for a specific row, based on the configuration
:param fig: Plot figure to append to
:param row: row number for this plot
:param indicators: List of indicators present in the dataframe
:param indicators: Dict of Indicators with configuration options.
Dict key must correspond to dataframe column.
:param data: candlestick DataFrame
"""
for indicator in indicators:
for indicator, conf in indicators.items():
logger.debug(f"indicator {indicator} with config {conf}")
if indicator in data:
kwargs = {'x': data['date'],
'y': data[indicator].values,
'mode': 'lines',
'name': indicator
}
if 'color' in conf:
kwargs.update({'line': {'color': conf['color']}})
scatter = go.Scatter(
x=data['date'],
y=data[indicator].values,
mode='lines',
name=indicator
**kwargs
)
fig.add_trace(scatter, row, 1)
else:
@ -107,11 +113,31 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
"""
# Trades can be empty
if trades is not None and len(trades) > 0:
# Create description for sell summarizing the trade
trades['desc'] = trades.apply(lambda row: f"{round(row['profitperc'] * 100, 1)}%, "
f"{row['sell_reason']}, {row['duration']} min",
axis=1)
trade_buys = go.Scatter(
x=trades["open_time"],
y=trades["open_rate"],
mode='markers',
name='trade_buy',
name='Trade buy',
text=trades["desc"],
marker=dict(
symbol='circle-open',
size=11,
line=dict(width=2),
color='cyan'
)
)
trade_sells = go.Scatter(
x=trades.loc[trades['profitperc'] > 0, "close_time"],
y=trades.loc[trades['profitperc'] > 0, "close_rate"],
text=trades.loc[trades['profitperc'] > 0, "desc"],
mode='markers',
name='Sell - Profit',
marker=dict(
symbol='square-open',
size=11,
@ -119,16 +145,12 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
color='green'
)
)
# Create description for sell summarizing the trade
desc = trades.apply(lambda row: f"{round(row['profitperc'], 3)}%, {row['sell_reason']}, "
f"{row['duration']}min",
axis=1)
trade_sells = go.Scatter(
x=trades["close_time"],
y=trades["close_rate"],
text=desc,
trade_sells_loss = go.Scatter(
x=trades.loc[trades['profitperc'] <= 0, "close_time"],
y=trades.loc[trades['profitperc'] <= 0, "close_rate"],
text=trades.loc[trades['profitperc'] <= 0, "desc"],
mode='markers',
name='trade_sell',
name='Sell - Loss',
marker=dict(
symbol='square-open',
size=11,
@ -138,14 +160,53 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
)
fig.add_trace(trade_buys, 1, 1)
fig.add_trace(trade_sells, 1, 1)
fig.add_trace(trade_sells_loss, 1, 1)
else:
logger.warning("No trades found.")
return fig
def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFrame = None,
def create_plotconfig(indicators1: List[str], indicators2: List[str],
plot_config: Dict[str, Dict]) -> Dict[str, Dict]:
"""
Combines indicators 1 and indicators 2 into plot_config if necessary
:param indicators1: List containing Main plot indicators
:param indicators2: List containing Sub plot indicators
:param plot_config: Dict of Dicts containing advanced plot configuration
:return: plot_config - eventually with indicators 1 and 2
"""
if plot_config:
if indicators1:
plot_config['main_plot'] = {ind: {} for ind in indicators1}
if indicators2:
plot_config['subplots'] = {'Other': {ind: {} for ind in indicators2}}
if not plot_config:
# If no indicators and no plot-config given, use defaults.
if not indicators1:
indicators1 = ['sma', 'ema3', 'ema5']
if not indicators2:
indicators2 = ['macd', 'macdsignal']
# Create subplot configuration if plot_config is not available.
plot_config = {
'main_plot': {ind: {} for ind in indicators1},
'subplots': {'Other': {ind: {} for ind in indicators2}},
}
if 'main_plot' not in plot_config:
plot_config['main_plot'] = {}
if 'subplots' not in plot_config:
plot_config['subplots'] = {}
return plot_config
def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFrame = None, *,
indicators1: List[str] = [],
indicators2: List[str] = [],) -> go.Figure:
indicators2: List[str] = [],
plot_config: Dict[str, Dict] = {},
) -> go.Figure:
"""
Generate the graph from the data generated by Backtesting or from DB
Volume will always be ploted in row2, so Row 1 and 3 are to our disposal for custom indicators
@ -154,21 +215,26 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
:param trades: All trades created
:param indicators1: List containing Main plot indicators
:param indicators2: List containing Sub plot indicators
:return: None
:param plot_config: Dict of Dicts containing advanced plot configuration
:return: Plotly figure
"""
plot_config = create_plotconfig(indicators1, indicators2, plot_config)
rows = 2 + len(plot_config['subplots'])
row_widths = [1 for _ in plot_config['subplots']]
# Define the graph
fig = make_subplots(
rows=3,
rows=rows,
cols=1,
shared_xaxes=True,
row_width=[1, 1, 4],
row_width=row_widths + [1, 4],
vertical_spacing=0.0001,
)
fig['layout'].update(title=pair)
fig['layout']['yaxis1'].update(title='Price')
fig['layout']['yaxis2'].update(title='Volume')
fig['layout']['yaxis3'].update(title='Other')
for i, name in enumerate(plot_config['subplots']):
fig['layout'][f'yaxis{3 + i}'].update(title=name)
fig['layout']['xaxis']['rangeslider'].update(visible=False)
# Common information
@ -238,12 +304,13 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
)
fig.add_trace(bb_lower, 1, 1)
fig.add_trace(bb_upper, 1, 1)
if 'bb_upperband' in indicators1 and 'bb_lowerband' in indicators1:
indicators1.remove('bb_upperband')
indicators1.remove('bb_lowerband')
if ('bb_upperband' in plot_config['main_plot']
and 'bb_lowerband' in plot_config['main_plot']):
del plot_config['main_plot']['bb_upperband']
del plot_config['main_plot']['bb_lowerband']
# Add indicators to main plot
fig = add_indicators(fig=fig, row=1, indicators=indicators1, data=data)
fig = add_indicators(fig=fig, row=1, indicators=plot_config['main_plot'], data=data)
fig = plot_trades(fig, trades)
@ -254,11 +321,14 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
name='Volume',
marker_color='DarkSlateGrey',
marker_line_color='DarkSlateGrey'
)
)
fig.add_trace(volume, 2, 1)
# Add indicators to separate row
fig = add_indicators(fig=fig, row=3, indicators=indicators2, data=data)
for i, name in enumerate(plot_config['subplots']):
fig = add_indicators(fig=fig, row=3 + i,
indicators=plot_config['subplots'][name],
data=data)
return fig
@ -340,7 +410,7 @@ def load_and_plot_trades(config: Dict[str, Any]):
- Generate plot files
:return: None
"""
strategy = StrategyResolver(config).strategy
strategy = StrategyResolver.load_strategy(config)
plot_elements = init_plotscript(config)
trades = plot_elements['trades']
@ -359,8 +429,9 @@ def load_and_plot_trades(config: Dict[str, Any]):
pair=pair,
data=dataframe,
trades=trades_pair,
indicators1=config["indicators1"],
indicators2=config["indicators2"],
indicators1=config.get("indicators1", []),
indicators2=config.get("indicators2", []),
plot_config=strategy.plot_config if hasattr(strategy, 'plot_config') else {}
)
store_plot_file(fig, filename=generate_plot_filename(pair, config['ticker_interval']),

21
freqtrade/resolvers/exchange_resolver.py
View File

@ -14,10 +14,10 @@ class ExchangeResolver(IResolver):
"""
This class contains all the logic to load a custom exchange class
"""
object_type = Exchange
__slots__ = ['exchange']
def __init__(self, exchange_name: str, config: dict, validate: bool = True) -> None:
@staticmethod
def load_exchange(exchange_name: str, config: dict, validate: bool = True) -> Exchange:
"""
Load the custom class from config parameter
:param config: configuration dictionary
@ -25,17 +25,20 @@ class ExchangeResolver(IResolver):
# Map exchange name to avoid duplicate classes for identical exchanges
exchange_name = MAP_EXCHANGE_CHILDCLASS.get(exchange_name, exchange_name)
exchange_name = exchange_name.title()
exchange = None
try:
self.exchange = self._load_exchange(exchange_name, kwargs={'config': config,
'validate': validate})
exchange = ExchangeResolver._load_exchange(exchange_name,
kwargs={'config': config,
'validate': validate})
except ImportError:
logger.info(
f"No {exchange_name} specific subclass found. Using the generic class instead.")
if not hasattr(self, "exchange"):
self.exchange = Exchange(config, validate=validate)
if not exchange:
exchange = Exchange(config, validate=validate)
return exchange
def _load_exchange(
self, exchange_name: str, kwargs: dict) -> Exchange:
@staticmethod
def _load_exchange(exchange_name: str, kwargs: dict) -> Exchange:
"""
Loads the specified exchange.
Only checks for exchanges exported in freqtrade.exchanges

92
freqtrade/resolvers/hyperopt_resolver.py
View File

@ -5,10 +5,10 @@ This module load custom hyperopt
"""
import logging
from pathlib import Path
from typing import Optional, Dict
from typing import Dict
from freqtrade import OperationalException
from freqtrade.constants import DEFAULT_HYPEROPT_LOSS, USERPATH_HYPEROPTS
from freqtrade.exceptions import OperationalException
from freqtrade.optimize.hyperopt_interface import IHyperOpt
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss
from freqtrade.resolvers import IResolver
@ -20,11 +20,15 @@ class HyperOptResolver(IResolver):
"""
This class contains all the logic to load custom hyperopt class
"""
__slots__ = ['hyperopt']
object_type = IHyperOpt
object_type_str = "Hyperopt"
user_subdir = USERPATH_HYPEROPTS
initial_search_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
def __init__(self, config: Dict) -> None:
@staticmethod
def load_hyperopt(config: Dict) -> IHyperOpt:
"""
Load the custom class from config parameter
Load the custom hyperopt class from config parameter
:param config: configuration dictionary
"""
if not config.get('hyperopt'):
@ -33,50 +37,33 @@ class HyperOptResolver(IResolver):
hyperopt_name = config['hyperopt']
self.hyperopt = self._load_hyperopt(hyperopt_name, config,
extra_dir=config.get('hyperopt_path'))
hyperopt = HyperOptResolver.load_object(hyperopt_name, config,
kwargs={'config': config},
extra_dir=config.get('hyperopt_path'))
if not hasattr(self.hyperopt, 'populate_indicators'):
if not hasattr(hyperopt, 'populate_indicators'):
logger.warning("Hyperopt class does not provide populate_indicators() method. "
"Using populate_indicators from the strategy.")
if not hasattr(self.hyperopt, 'populate_buy_trend'):
if not hasattr(hyperopt, 'populate_buy_trend'):
logger.warning("Hyperopt class does not provide populate_buy_trend() method. "
"Using populate_buy_trend from the strategy.")
if not hasattr(self.hyperopt, 'populate_sell_trend'):
if not hasattr(hyperopt, 'populate_sell_trend'):
logger.warning("Hyperopt class does not provide populate_sell_trend() method. "
"Using populate_sell_trend from the strategy.")
def _load_hyperopt(
self, hyperopt_name: str, config: Dict, extra_dir: Optional[str] = None) -> IHyperOpt:
"""
Search and loads the specified hyperopt.
:param hyperopt_name: name of the module to import
:param config: configuration dictionary
:param extra_dir: additional directory to search for the given hyperopt
:return: HyperOpt instance or None
"""
current_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
abs_paths = self.build_search_paths(config, current_path=current_path,
user_subdir=USERPATH_HYPEROPTS, extra_dir=extra_dir)
hyperopt = self._load_object(paths=abs_paths, object_type=IHyperOpt,
object_name=hyperopt_name, kwargs={'config': config})
if hyperopt:
return hyperopt
raise OperationalException(
f"Impossible to load Hyperopt '{hyperopt_name}'. This class does not exist "
"or contains Python code errors."
)
return hyperopt
class HyperOptLossResolver(IResolver):
"""
This class contains all the logic to load custom hyperopt loss class
"""
__slots__ = ['hyperoptloss']
object_type = IHyperOptLoss
object_type_str = "HyperoptLoss"
user_subdir = USERPATH_HYPEROPTS
initial_search_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
def __init__(self, config: Dict) -> None:
@staticmethod
def load_hyperoptloss(config: Dict) -> IHyperOptLoss:
"""
Load the custom class from config parameter
:param config: configuration dictionary
@ -86,38 +73,15 @@ class HyperOptLossResolver(IResolver):
# default hyperopt loss
hyperoptloss_name = config.get('hyperopt_loss') or DEFAULT_HYPEROPT_LOSS
self.hyperoptloss = self._load_hyperoptloss(
hyperoptloss_name, config, extra_dir=config.get('hyperopt_path'))
hyperoptloss = HyperOptLossResolver.load_object(hyperoptloss_name,
config, kwargs={},
extra_dir=config.get('hyperopt_path'))
# Assign ticker_interval to be used in hyperopt
self.hyperoptloss.__class__.ticker_interval = str(config['ticker_interval'])
hyperoptloss.__class__.ticker_interval = str(config['ticker_interval'])
if not hasattr(self.hyperoptloss, 'hyperopt_loss_function'):
if not hasattr(hyperoptloss, 'hyperopt_loss_function'):
raise OperationalException(
f"Found HyperoptLoss class {hyperoptloss_name} does not "
"implement `hyperopt_loss_function`.")
def _load_hyperoptloss(
self, hyper_loss_name: str, config: Dict,
extra_dir: Optional[str] = None) -> IHyperOptLoss:
"""
Search and loads the specified hyperopt loss class.
:param hyper_loss_name: name of the module to import
:param config: configuration dictionary
:param extra_dir: additional directory to search for the given hyperopt
:return: HyperOptLoss instance or None
"""
current_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
abs_paths = self.build_search_paths(config, current_path=current_path,
user_subdir=USERPATH_HYPEROPTS, extra_dir=extra_dir)
hyperoptloss = self._load_object(paths=abs_paths, object_type=IHyperOptLoss,
object_name=hyper_loss_name)
if hyperoptloss:
return hyperoptloss
raise OperationalException(
f"Impossible to load HyperoptLoss '{hyper_loss_name}'. This class does not exist "
"or contains Python code errors."
)
return hyperoptloss

103
freqtrade/resolvers/iresolver.py
View File

@ -7,7 +7,9 @@ import importlib.util
import inspect
import logging
from pathlib import Path
from typing import Any, List, Optional, Tuple, Union, Generator
from typing import Any, Dict, Generator, List, Optional, Tuple, Type, Union
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
@ -16,11 +18,17 @@ class IResolver:
"""
This class contains all the logic to load custom classes
"""
# Childclasses need to override this
object_type: Type[Any]
object_type_str: str
user_subdir: Optional[str] = None
initial_search_path: Path
def build_search_paths(self, config, current_path: Path, user_subdir: Optional[str] = None,
@classmethod
def build_search_paths(cls, config, user_subdir: Optional[str] = None,
extra_dir: Optional[str] = None) -> List[Path]:
abs_paths: List[Path] = [current_path]
abs_paths: List[Path] = [cls.initial_search_path]
if user_subdir:
abs_paths.insert(0, config['user_data_dir'].joinpath(user_subdir))
@ -31,12 +39,11 @@ class IResolver:
return abs_paths
@staticmethod
def _get_valid_object(object_type, module_path: Path,
object_name: str) -> Generator[Any, None, None]:
@classmethod
def _get_valid_object(cls, module_path: Path,
object_name: Optional[str]) -> Generator[Any, None, None]:
"""
Generator returning objects with matching object_type and object_name in the path given.
:param object_type: object_type (class)
:param module_path: absolute path to the module
:param object_name: Class name of the object
:return: generator containing matching objects
@ -44,7 +51,7 @@ class IResolver:
# Generate spec based on absolute path
# Pass object_name as first argument to have logging print a reasonable name.
spec = importlib.util.spec_from_file_location(object_name, str(module_path))
spec = importlib.util.spec_from_file_location(object_name or "", str(module_path))
module = importlib.util.module_from_spec(spec)
try:
spec.loader.exec_module(module) # type: ignore # importlib does not use typehints
@ -54,19 +61,20 @@ class IResolver:
valid_objects_gen = (
obj for name, obj in inspect.getmembers(module, inspect.isclass)
if object_name == name and object_type in obj.__bases__
if (object_name is None or object_name == name) and cls.object_type in obj.__bases__
)
return valid_objects_gen
@staticmethod
def _search_object(directory: Path, object_type, object_name: str,
kwargs: dict = {}) -> Union[Tuple[Any, Path], Tuple[None, None]]:
@classmethod
def _search_object(cls, directory: Path, object_name: str
) -> Union[Tuple[Any, Path], Tuple[None, None]]:
"""
Search for the objectname in the given directory
:param directory: relative or absolute directory path
:return: object instance
:param object_name: ClassName of the object to load
:return: object class
"""
logger.debug("Searching for %s %s in '%s'", object_type.__name__, object_name, directory)
logger.debug(f"Searching for {cls.object_type.__name__} {object_name} in '{directory}'")
for entry in directory.iterdir():
# Only consider python files
if not str(entry).endswith('.py'):
@ -74,14 +82,14 @@ class IResolver:
continue
module_path = entry.resolve()
obj = next(IResolver._get_valid_object(object_type, module_path, object_name), None)
obj = next(cls._get_valid_object(module_path, object_name), None)
if obj:
return (obj(**kwargs), module_path)
return (obj, module_path)
return (None, None)
@staticmethod
def _load_object(paths: List[Path], object_type, object_name: str,
@classmethod
def _load_object(cls, paths: List[Path], object_name: str,
kwargs: dict = {}) -> Optional[Any]:
"""
Try to load object from path list.
@ -89,16 +97,63 @@ class IResolver:
for _path in paths:
try:
(module, module_path) = IResolver._search_object(directory=_path,
object_type=object_type,
object_name=object_name,
kwargs=kwargs)
(module, module_path) = cls._search_object(directory=_path,
object_name=object_name)
if module:
logger.info(
f"Using resolved {object_type.__name__.lower()[1:]} {object_name} "
f"Using resolved {cls.object_type.__name__.lower()[1:]} {object_name} "
f"from '{module_path}'...")
return module
return module(**kwargs)
except FileNotFoundError:
logger.warning('Path "%s" does not exist.', _path.resolve())
return None
@classmethod
def load_object(cls, object_name: str, config: dict, kwargs: dict,
extra_dir: Optional[str] = None) -> Any:
"""
Search and loads the specified object as configured in hte child class.
:param objectname: name of the module to import
:param config: configuration dictionary
:param extra_dir: additional directory to search for the given pairlist
:raises: OperationalException if the class is invalid or does not exist.
:return: Object instance or None
"""
abs_paths = cls.build_search_paths(config,
user_subdir=cls.user_subdir,
extra_dir=extra_dir)
pairlist = cls._load_object(paths=abs_paths, object_name=object_name,
kwargs=kwargs)
if pairlist:
return pairlist
raise OperationalException(
f"Impossible to load {cls.object_type_str} '{object_name}'. This class does not exist "
"or contains Python code errors."
)
@classmethod
def search_all_objects(cls, directory: Path) -> List[Dict[str, Any]]:
"""
Searches a directory for valid objects
:param directory: Path to search
:return: List of dicts containing 'name', 'class' and 'location' entires
"""
logger.debug(f"Searching for {cls.object_type.__name__} '{directory}'")
objects = []
for entry in directory.iterdir():
# Only consider python files
if not str(entry).endswith('.py'):
logger.debug('Ignoring %s', entry)
continue
module_path = entry.resolve()
logger.debug(f"Path {module_path}")
for obj in cls._get_valid_object(module_path, object_name=None):
objects.append(
{'name': obj.__name__,
'class': obj,
'location': entry,
})
return objects

50
freqtrade/resolvers/pairlist_resolver.py
View File

@ -6,7 +6,6 @@ This module load custom pairlists
import logging
from pathlib import Path
from freqtrade import OperationalException
from freqtrade.pairlist.IPairList import IPairList
from freqtrade.resolvers import IResolver
@ -17,41 +16,28 @@ class PairListResolver(IResolver):
"""
This class contains all the logic to load custom PairList class
"""
object_type = IPairList
object_type_str = "Pairlist"
user_subdir = None
initial_search_path = Path(__file__).parent.parent.joinpath('pairlist').resolve()
__slots__ = ['pairlist']
def __init__(self, pairlist_name: str, exchange, pairlistmanager,
config: dict, pairlistconfig: dict, pairlist_pos: int) -> None:
@staticmethod
def load_pairlist(pairlist_name: str, exchange, pairlistmanager,
config: dict, pairlistconfig: dict, pairlist_pos: int) -> IPairList:
"""
Load the custom class from config parameter
:param config: configuration dictionary or None
Load the pairlist with pairlist_name
:param pairlist_name: Classname of the pairlist
:param exchange: Initialized exchange class
:param pairlistmanager: Initialized pairlist manager
:param config: configuration dictionary
:param pairlistconfig: Configuration dedicated to this pairlist
:param pairlist_pos: Position of the pairlist in the list of pairlists
:return: initialized Pairlist class
"""
self.pairlist = self._load_pairlist(pairlist_name, config,
return PairListResolver.load_object(pairlist_name, config,
kwargs={'exchange': exchange,
'pairlistmanager': pairlistmanager,
'config': config,
'pairlistconfig': pairlistconfig,
'pairlist_pos': pairlist_pos})
def _load_pairlist(
self, pairlist_name: str, config: dict, kwargs: dict) -> IPairList:
"""
Search and loads the specified pairlist.
:param pairlist_name: name of the module to import
:param config: configuration dictionary
:param extra_dir: additional directory to search for the given pairlist
:return: PairList instance or None
"""
current_path = Path(__file__).parent.parent.joinpath('pairlist').resolve()
abs_paths = self.build_search_paths(config, current_path=current_path,
user_subdir=None, extra_dir=None)
pairlist = self._load_object(paths=abs_paths, object_type=IPairList,
object_name=pairlist_name, kwargs=kwargs)
if pairlist:
return pairlist
raise OperationalException(
f"Impossible to load Pairlist '{pairlist_name}'. This class does not exist "
"or contains Python code errors."
)
'pairlist_pos': pairlist_pos},
)

76
freqtrade/resolvers/strategy_resolver.py
View File

@ -11,7 +11,9 @@ from inspect import getfullargspec
from pathlib import Path
from typing import Dict, Optional
from freqtrade import constants, OperationalException
from freqtrade.constants import (REQUIRED_ORDERTIF, REQUIRED_ORDERTYPES,
USERPATH_STRATEGY)
from freqtrade.exceptions import OperationalException
from freqtrade.resolvers import IResolver
from freqtrade.strategy.interface import IStrategy
@ -20,12 +22,15 @@ logger = logging.getLogger(__name__)
class StrategyResolver(IResolver):
"""
This class contains all the logic to load custom strategy class
This class contains the logic to load custom strategy class
"""
object_type = IStrategy
object_type_str = "Strategy"
user_subdir = USERPATH_STRATEGY
initial_search_path = Path(__file__).parent.parent.joinpath('strategy').resolve()
__slots__ = ['strategy']
def __init__(self, config: Optional[Dict] = None) -> None:
@staticmethod
def load_strategy(config: Optional[Dict] = None) -> IStrategy:
"""
Load the custom class from config parameter
:param config: configuration dictionary or None
@ -37,9 +42,9 @@ class StrategyResolver(IResolver):
"the strategy class to use.")
strategy_name = config['strategy']
self.strategy: IStrategy = self._load_strategy(strategy_name,
config=config,
extra_dir=config.get('strategy_path'))
strategy: IStrategy = StrategyResolver._load_strategy(
strategy_name, config=config,
extra_dir=config.get('strategy_path'))
# make sure ask_strategy dict is available
if 'ask_strategy' not in config:
@ -61,15 +66,18 @@ class StrategyResolver(IResolver):
("stake_currency", None, False),
("stake_amount", None, False),
("startup_candle_count", None, False),
("unfilledtimeout", None, False),
("use_sell_signal", True, True),
("sell_profit_only", False, True),
("ignore_roi_if_buy_signal", False, True),
]
for attribute, default, ask_strategy in attributes:
if ask_strategy:
self._override_attribute_helper(config['ask_strategy'], attribute, default)
StrategyResolver._override_attribute_helper(strategy, config['ask_strategy'],
attribute, default)
else:
self._override_attribute_helper(config, attribute, default)
StrategyResolver._override_attribute_helper(strategy, config,
attribute, default)
# Loop this list again to have output combined
for attribute, _, exp in attributes:
@ -79,14 +87,16 @@ class StrategyResolver(IResolver):
logger.info("Strategy using %s: %s", attribute, config[attribute])
# Sort and apply type conversions
self.strategy.minimal_roi = OrderedDict(sorted(
{int(key): value for (key, value) in self.strategy.minimal_roi.items()}.items(),
strategy.minimal_roi = OrderedDict(sorted(
{int(key): value for (key, value) in strategy.minimal_roi.items()}.items(),
key=lambda t: t[0]))
self.strategy.stoploss = float(self.strategy.stoploss)
strategy.stoploss = float(strategy.stoploss)
self._strategy_sanity_validations()
StrategyResolver._strategy_sanity_validations(strategy)
return strategy
def _override_attribute_helper(self, config, attribute: str, default):
@staticmethod
def _override_attribute_helper(strategy, config, attribute: str, default):
"""
Override attributes in the strategy.
Prevalence:
@ -95,30 +105,32 @@ class StrategyResolver(IResolver):
- default (if not None)
"""
if attribute in config:
setattr(self.strategy, attribute, config[attribute])
setattr(strategy, attribute, config[attribute])
logger.info("Override strategy '%s' with value in config file: %s.",
attribute, config[attribute])
elif hasattr(self.strategy, attribute):
val = getattr(self.strategy, attribute)
elif hasattr(strategy, attribute):
val = getattr(strategy, attribute)
# None's cannot exist in the config, so do not copy them
if val is not None:
config[attribute] = val
# Explicitly check for None here as other "falsy" values are possible
elif default is not None:
setattr(self.strategy, attribute, default)
setattr(strategy, attribute, default)
config[attribute] = default
def _strategy_sanity_validations(self):
if not all(k in self.strategy.order_types for k in constants.REQUIRED_ORDERTYPES):
raise ImportError(f"Impossible to load Strategy '{self.strategy.__class__.__name__}'. "
@staticmethod
def _strategy_sanity_validations(strategy):
if not all(k in strategy.order_types for k in REQUIRED_ORDERTYPES):
raise ImportError(f"Impossible to load Strategy '{strategy.__class__.__name__}'. "
f"Order-types mapping is incomplete.")
if not all(k in self.strategy.order_time_in_force for k in constants.REQUIRED_ORDERTIF):
raise ImportError(f"Impossible to load Strategy '{self.strategy.__class__.__name__}'. "
if not all(k in strategy.order_time_in_force for k in REQUIRED_ORDERTIF):
raise ImportError(f"Impossible to load Strategy '{strategy.__class__.__name__}'. "
f"Order-time-in-force mapping is incomplete.")
def _load_strategy(
self, strategy_name: str, config: dict, extra_dir: Optional[str] = None) -> IStrategy:
@staticmethod
def _load_strategy(strategy_name: str,
config: dict, extra_dir: Optional[str] = None) -> IStrategy:
"""
Search and loads the specified strategy.
:param strategy_name: name of the module to import
@ -126,11 +138,10 @@ class StrategyResolver(IResolver):
:param extra_dir: additional directory to search for the given strategy
:return: Strategy instance or None
"""
current_path = Path(__file__).parent.parent.joinpath('strategy').resolve()
abs_paths = self.build_search_paths(config, current_path=current_path,
user_subdir=constants.USERPATH_STRATEGY,
extra_dir=extra_dir)
abs_paths = StrategyResolver.build_search_paths(config,
user_subdir=USERPATH_STRATEGY,
extra_dir=extra_dir)
if ":" in strategy_name:
logger.info("loading base64 encoded strategy")
@ -148,8 +159,9 @@ class StrategyResolver(IResolver):
# register temp path with the bot
abs_paths.insert(0, temp.resolve())
strategy = self._load_object(paths=abs_paths, object_type=IStrategy,
object_name=strategy_name, kwargs={'config': config})
strategy = StrategyResolver._load_object(paths=abs_paths,
object_name=strategy_name,
kwargs={'config': config})
if strategy:
strategy._populate_fun_len = len(getfullargspec(strategy.populate_indicators).args)
strategy._buy_fun_len = len(getfullargspec(strategy.populate_buy_trend).args)

61
freqtrade/rpc/rpc.py
View File

@ -11,7 +11,7 @@ from typing import Any, Dict, List, Optional, Tuple
import arrow
from numpy import NAN, mean
from freqtrade import DependencyException, TemporaryError
from freqtrade.exceptions import DependencyException, TemporaryError
from freqtrade.misc import shorten_date
from freqtrade.persistence import Trade
from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
@ -88,7 +88,7 @@ class RPC:
"""
config = self._freqtrade.config
val = {
'dry_run': config.get('dry_run', False),
'dry_run': config['dry_run'],
'stake_currency': config['stake_currency'],
'stake_amount': config['stake_amount'],
'minimal_roi': config['minimal_roi'].copy(),
@ -123,7 +123,7 @@ class RPC:
current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
except DependencyException:
current_rate = NAN
current_profit = trade.calc_profit_percent(current_rate)
current_profit = trade.calc_profit_ratio(current_rate)
fmt_close_profit = (f'{round(trade.close_profit * 100, 2):.2f}%'
if trade.close_profit else None)
trade_dict = trade.to_json()
@ -142,7 +142,7 @@ class RPC:
def _rpc_status_table(self, stake_currency, fiat_display_currency: str) -> Tuple[List, List]:
trades = Trade.get_open_trades()
if not trades:
raise RPCException('no active order')
raise RPCException('no active trade')
else:
trades_list = []
for trade in trades:
@ -151,7 +151,7 @@ class RPC:
current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
except DependencyException:
current_rate = NAN
trade_perc = (100 * trade.calc_profit_percent(current_rate))
trade_perc = (100 * trade.calc_profit_ratio(current_rate))
trade_profit = trade.calc_profit(current_rate)
profit_str = f'{trade_perc:.2f}%'
if self._fiat_converter:
@ -240,7 +240,7 @@ class RPC:
durations.append((trade.close_date - trade.open_date).total_seconds())
if not trade.is_open:
profit_percent = trade.calc_profit_percent()
profit_percent = trade.calc_profit_ratio()
profit_closed_coin.append(trade.calc_profit())
profit_closed_perc.append(profit_percent)
else:
@ -249,7 +249,7 @@ class RPC:
current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
except DependencyException:
current_rate = NAN
profit_percent = trade.calc_profit_percent(rate=current_rate)
profit_percent = trade.calc_profit_ratio(rate=current_rate)
profit_all_coin.append(
trade.calc_profit(rate=trade.close_rate or current_rate)
@ -306,6 +306,8 @@ class RPC:
except (TemporaryError, DependencyException):
raise RPCException('Error getting current tickers.')
self._freqtrade.wallets.update(require_update=False)
for coin, balance in self._freqtrade.wallets.get_all_balances().items():
if not balance.total:
continue
@ -335,19 +337,21 @@ class RPC:
'stake': stake_currency,
})
if total == 0.0:
if self._freqtrade.config.get('dry_run', False):
if self._freqtrade.config['dry_run']:
raise RPCException('Running in Dry Run, balances are not available.')
else:
raise RPCException('All balances are zero.')
symbol = fiat_display_currency
value = self._fiat_converter.convert_amount(total, 'BTC',
value = self._fiat_converter.convert_amount(total, stake_currency,
symbol) if self._fiat_converter else 0
return {
'currencies': output,
'total': total,
'symbol': symbol,
'value': value,
'stake': stake_currency,
'note': 'Simulated balances' if self._freqtrade.config['dry_run'] else ''
}
def _rpc_start(self) -> Dict[str, str]:
@ -416,24 +420,27 @@ class RPC:
if self._freqtrade.state != State.RUNNING:
raise RPCException('trader is not running')
if trade_id == 'all':
# Execute sell for all open orders
for trade in Trade.get_open_trades():
_exec_forcesell(trade)
with self._freqtrade._sell_lock:
if trade_id == 'all':
# Execute sell for all open orders
for trade in Trade.get_open_trades():
_exec_forcesell(trade)
Trade.session.flush()
self._freqtrade.wallets.update()
return {'result': 'Created sell orders for all open trades.'}
# Query for trade
trade = Trade.get_trades(
trade_filter=[Trade.id == trade_id, Trade.is_open.is_(True), ]
).first()
if not trade:
logger.warning('forcesell: Invalid argument received')
raise RPCException('invalid argument')
_exec_forcesell(trade)
Trade.session.flush()
return {'result': 'Created sell orders for all open trades.'}
# Query for trade
trade = Trade.get_trades(
trade_filter=[Trade.id == trade_id, Trade.is_open.is_(True), ]
).first()
if not trade:
logger.warning('forcesell: Invalid argument received')
raise RPCException('invalid argument')
_exec_forcesell(trade)
Trade.session.flush()
return {'result': f'Created sell order for trade {trade_id}.'}
self._freqtrade.wallets.update()
return {'result': f'Created sell order for trade {trade_id}.'}
def _rpc_forcebuy(self, pair: str, price: Optional[float]) -> Optional[Trade]:
"""
@ -460,7 +467,7 @@ class RPC:
raise RPCException(f'position for {pair} already open - id: {trade.id}')
# gen stake amount
stakeamount = self._freqtrade._get_trade_stake_amount(pair)
stakeamount = self._freqtrade.get_trade_stake_amount(pair)
# execute buy
if self._freqtrade.execute_buy(pair, stakeamount, price):

2
freqtrade/rpc/rpc_manager.py
View File

@ -62,7 +62,7 @@ class RPCManager:
logger.error(f"Message type {msg['type']} not implemented by handler {mod.name}.")
def startup_messages(self, config, pairlist) -> None:
if config.get('dry_run', False):
if config['dry_run']:
self.send_msg({
'type': RPCMessageType.WARNING_NOTIFICATION,
'status': 'Dry run is enabled. All trades are simulated.'

31
freqtrade/rpc/telegram.py
View File

@ -9,7 +9,7 @@ from typing import Any, Callable, Dict
from tabulate import tabulate
from telegram import ParseMode, ReplyKeyboardMarkup, Update
from telegram.error import NetworkError, TelegramError
from telegram.ext import CommandHandler, Updater, CallbackContext
from telegram.ext import CallbackContext, CommandHandler, Updater
from freqtrade.__init__ import __version__
from freqtrade.rpc import RPC, RPCException, RPCMessageType
@ -144,6 +144,9 @@ 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['duration'] = msg['close_date'].replace(
microsecond=0) - msg['open_date'].replace(microsecond=0)
msg['duration_min'] = msg['duration'].total_seconds() / 60
message = ("*{exchange}:* Selling {pair}\n"
"*Rate:* `{limit:.8f}`\n"
@ -151,6 +154,7 @@ class Telegram(RPC):
"*Open Rate:* `{open_rate:.8f}`\n"
"*Current Rate:* `{current_rate:.8f}`\n"
"*Sell Reason:* `{sell_reason}`\n"
"*Duration:* `{duration} ({duration_min:.1f} min)`\n"
"*Profit:* `{profit_percent:.2f}%`").format(**msg)
# Check if all sell properties are available.
@ -327,7 +331,15 @@ class Telegram(RPC):
try:
result = self._rpc_balance(self._config['stake_currency'],
self._config.get('fiat_display_currency', ''))
output = ''
if self._config['dry_run']:
output += (
f"*Warning:* Simulated balances in Dry Mode.\n"
"This mode is still experimental!\n"
"Starting capital: "
f"`{self._config['dry_run_wallet']}` {self._config['stake_currency']}.\n"
)
for currency in result['currencies']:
if currency['est_stake'] > 0.0001:
curr_output = "*{currency}:*\n" \
@ -346,7 +358,7 @@ class Telegram(RPC):
output += curr_output
output += "\n*Estimated Value*:\n" \
"\t`BTC: {total: .8f}`\n" \
"\t`{stake}: {total: .8f}`\n" \
"\t`{symbol}: {value: .2f}`\n".format(**result)
self._send_msg(output)
except RPCException as e:
@ -583,14 +595,25 @@ class Telegram(RPC):
:return: None
"""
val = self._rpc_show_config()
if val['trailing_stop']:
sl_info = (
f"*Initial Stoploss:* `{val['stoploss']}`\n"
f"*Trailing stop positive:* `{val['trailing_stop_positive']}`\n"
f"*Trailing stop offset:* `{val['trailing_stop_positive_offset']}`\n"
f"*Only trail above offset:* `{val['trailing_only_offset_is_reached']}`\n"
)
else:
sl_info = f"*Stoploss:* `{val['stoploss']}`\n"
self._send_msg(
f"*Mode:* `{'Dry-run' if val['dry_run'] else 'Live'}`\n"
f"*Exchange:* `{val['exchange']}`\n"
f"*Stake per trade:* `{val['stake_amount']} {val['stake_currency']}`\n"
f"*Minimum ROI:* `{val['minimal_roi']}`\n"
f"*{'Trailing ' if val['trailing_stop'] else ''}Stoploss:* `{val['stoploss']}`\n"
f"{sl_info}"
f"*Ticker Interval:* `{val['ticker_interval']}`\n"
f"*Strategy:* `{val['strategy']}`'"
f"*Strategy:* `{val['strategy']}`"
)
def _send_msg(self, msg: str, parse_mode: ParseMode = ParseMode.MARKDOWN) -> None:

36
freqtrade/strategy/interface.py
View File

@ -112,6 +112,9 @@ class IStrategy(ABC):
dp: Optional[DataProvider] = None
wallets: Optional[Wallets] = None
# Definition of plot_config. See plotting documentation for more details.
plot_config: Dict = {}
def __init__(self, config: dict) -> None:
self.config = config
# Dict to determine if analysis is necessary
@ -168,11 +171,24 @@ class IStrategy(ABC):
"""
Locks pair until a given timestamp happens.
Locked pairs are not analyzed, and are prevented from opening new trades.
Locks can only count up (allowing users to lock pairs for a longer period of time).
To remove a lock from a pair, use `unlock_pair()`
:param pair: Pair to lock
:param until: datetime in UTC until the pair should be blocked from opening new trades.
Needs to be timezone aware `datetime.now(timezone.utc)`
"""
self._pair_locked_until[pair] = until
if pair not in self._pair_locked_until or self._pair_locked_until[pair] < until:
self._pair_locked_until[pair] = until
def unlock_pair(self, pair) -> None:
"""
Unlocks a pair previously locked using lock_pair.
Not used by freqtrade itself, but intended to be used if users lock pairs
manually from within the strategy, to allow an easy way to unlock pairs.
:param pair: Unlock pair to allow trading again
"""
if pair in self._pair_locked_until:
del self._pair_locked_until[pair]
def is_pair_locked(self, pair: str) -> bool:
"""
@ -296,7 +312,7 @@ class IStrategy(ABC):
"""
# Set current rate to low for backtesting sell
current_rate = low or rate
current_profit = trade.calc_profit_percent(current_rate)
current_profit = trade.calc_profit_ratio(current_rate)
trade.adjust_min_max_rates(high or current_rate)
@ -311,7 +327,7 @@ class IStrategy(ABC):
# Set current rate to high for backtesting sell
current_rate = high or rate
current_profit = trade.calc_profit_percent(current_rate)
current_profit = trade.calc_profit_ratio(current_rate)
config_ask_strategy = self.config.get('ask_strategy', {})
if buy and config_ask_strategy.get('ignore_roi_if_buy_signal', False):
@ -360,7 +376,7 @@ class IStrategy(ABC):
sl_offset = self.trailing_stop_positive_offset
# Make sure current_profit is calculated using high for backtesting.
high_profit = current_profit if not high else trade.calc_profit_percent(high)
high_profit = current_profit if not high else trade.calc_profit_ratio(high)
# Don't update stoploss if trailing_only_offset_is_reached is true.
if not (self.trailing_only_offset_is_reached and high_profit < sl_offset):
@ -373,9 +389,11 @@ class IStrategy(ABC):
trade.adjust_stop_loss(high or current_rate, stop_loss_value)
# evaluate if the stoploss was hit if stoploss is not on exchange
# in Dry-Run, this handles stoploss logic as well, as the logic will not be different to
# regular stoploss handling.
if ((self.stoploss is not None) and
(trade.stop_loss >= current_rate) and
(not self.order_types.get('stoploss_on_exchange'))):
(not self.order_types.get('stoploss_on_exchange') or self.config['dry_run'])):
sell_type = SellType.STOP_LOSS
@ -394,7 +412,7 @@ class IStrategy(ABC):
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
def min_roi_reached_entry(self, trade_dur: int) -> Optional[float]:
def min_roi_reached_entry(self, trade_dur: int) -> Tuple[Optional[int], Optional[float]]:
"""
Based on trade duration defines the ROI entry that may have been reached.
:param trade_dur: trade duration in minutes
@ -403,9 +421,9 @@ class IStrategy(ABC):
# Get highest entry in ROI dict where key <= trade-duration
roi_list = list(filter(lambda x: x <= trade_dur, self.minimal_roi.keys()))
if not roi_list:
return None
return None, None
roi_entry = max(roi_list)
return self.minimal_roi[roi_entry]
return roi_entry, self.minimal_roi[roi_entry]
def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
"""
@ -415,7 +433,7 @@ class IStrategy(ABC):
"""
# Check if time matches and current rate is above threshold
trade_dur = int((current_time.timestamp() - trade.open_date.timestamp()) // 60)
roi = self.min_roi_reached_entry(trade_dur)
_, roi = self.min_roi_reached_entry(trade_dur)
if roi is None:
return False
else:

3
freqtrade/templates/base_strategy.py.j2
View File

@ -47,6 +47,7 @@ class {{ strategy }}(IStrategy):
# Trailing stoploss
trailing_stop = False
# trailing_only_offset_is_reached = False
# trailing_stop_positive = 0.01
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
@ -77,7 +78,7 @@ class {{ strategy }}(IStrategy):
'buy': 'gtc',
'sell': 'gtc'
}
{{ plot_config | indent(4) }}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.

3
freqtrade/templates/sample_hyperopt_loss.py
View File

@ -27,7 +27,8 @@ class SampleHyperOptLoss(IHyperOptLoss):
Defines the default loss function for hyperopt
This is intended to give you some inspiration for your own loss function.
The Function needs to return a number (float) - which becomes for better backtest results.
The Function needs to return a number (float) - which becomes smaller for better backtest
results.
"""
@staticmethod

17
freqtrade/templates/sample_strategy.py
View File

@ -48,6 +48,7 @@ class SampleStrategy(IStrategy):
# Trailing stoploss
trailing_stop = False
# trailing_only_offset_is_reached = False
# trailing_stop_positive = 0.01
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
@ -79,6 +80,22 @@ class SampleStrategy(IStrategy):
'sell': 'gtc'
}
plot_config = {
'main_plot': {
'tema': {},
'sar': {'color': 'white'},
},
'subplots': {
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
"RSI": {
'rsi': {'color': 'red'},
}
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.

6
freqtrade/templates/strategy_analysis_example.ipynb
View File

@ -73,9 +73,9 @@
"source": [
"# Load strategy using values set above\n",
"from freqtrade.resolvers import StrategyResolver\n",
"strategy = StrategyResolver({'strategy': strategy_name,\n",
" 'user_data_dir': user_data_dir,\n",
" 'strategy_path': strategy_location}).strategy\n",
"strategy = StrategyResolver.load_strategy({'strategy': strategy_name,\n",
" 'user_data_dir': user_data_dir,\n",
" 'strategy_path': strategy_location})\n",
"\n",
"# Generate buy/sell signals using strategy\n",
"df = strategy.analyze_ticker(candles, {'pair': pair})\n",

18
freqtrade/templates/subtemplates/plot_config_full.j2 Normal file
View File

@ -0,0 +1,18 @@
plot_config = {
# Main plot indicators (Moving averages, ...)
'main_plot': {
'tema': {},
'sar': {'color': 'white'},
},
'subplots': {
# Subplots - each dict defines one additional plot
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
"RSI": {
'rsi': {'color': 'red'},
}
}
}

0
user_data/backtest_data/.gitkeep → freqtrade/templates/subtemplates/plot_config_minimal.j2
View File

461
freqtrade/utils.py
View File

@ -1,461 +0,0 @@
import csv
import logging
import sys
from collections import OrderedDict
from operator import itemgetter
from pathlib import Path
from typing import Any, Dict, List
import arrow
import rapidjson
from colorama import init as colorama_init
from tabulate import tabulate
from freqtrade import OperationalException
from freqtrade.configuration import (Configuration, TimeRange,
remove_credentials)
from freqtrade.configuration.directory_operations import (copy_sample_files,
create_userdata_dir)
from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGY
from freqtrade.data.history import (convert_trades_to_ohlcv,
refresh_backtest_ohlcv_data,
refresh_backtest_trades_data)
from freqtrade.exchange import (available_exchanges, ccxt_exchanges,
market_is_active, symbol_is_pair)
from freqtrade.misc import plural, render_template
from freqtrade.resolvers import ExchangeResolver
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
def setup_utils_configuration(args: Dict[str, Any], method: RunMode) -> Dict[str, Any]:
"""
Prepare the configuration for utils subcommands
:param args: Cli args from Arguments()
:return: Configuration
"""
configuration = Configuration(args, method)
config = configuration.get_config()
# Ensure we do not use Exchange credentials
remove_credentials(config)
return config
def start_trading(args: Dict[str, Any]) -> int:
"""
Main entry point for trading mode
"""
from freqtrade.worker import Worker
# Load and run worker
worker = None
try:
worker = Worker(args)
worker.run()
except KeyboardInterrupt:
logger.info('SIGINT received, aborting ...')
finally:
if worker:
logger.info("worker found ... calling exit")
worker.exit()
return 0
def start_list_exchanges(args: Dict[str, Any]) -> None:
"""
Print available exchanges
:param args: Cli args from Arguments()
:return: None
"""
exchanges = ccxt_exchanges() if args['list_exchanges_all'] else available_exchanges()
if args['print_one_column']:
print('\n'.join(exchanges))
else:
if args['list_exchanges_all']:
print(f"All exchanges supported by the ccxt library: {', '.join(exchanges)}")
else:
print(f"Exchanges available for Freqtrade: {', '.join(exchanges)}")
def start_create_userdir(args: Dict[str, Any]) -> None:
"""
Create "user_data" directory to contain user data strategies, hyperopt, ...)
:param args: Cli args from Arguments()
:return: None
"""
if "user_data_dir" in args and args["user_data_dir"]:
userdir = create_userdata_dir(args["user_data_dir"], create_dir=True)
copy_sample_files(userdir, overwrite=args["reset"])
else:
logger.warning("`create-userdir` requires --userdir to be set.")
sys.exit(1)
def deploy_new_strategy(strategy_name, strategy_path: Path, subtemplate: str):
"""
Deploy new strategy from template to strategy_path
"""
indicators = render_template(templatefile=f"subtemplates/indicators_{subtemplate}.j2",)
buy_trend = render_template(templatefile=f"subtemplates/buy_trend_{subtemplate}.j2",)
sell_trend = render_template(templatefile=f"subtemplates/sell_trend_{subtemplate}.j2",)
strategy_text = render_template(templatefile='base_strategy.py.j2',
arguments={"strategy": strategy_name,
"indicators": indicators,
"buy_trend": buy_trend,
"sell_trend": sell_trend,
})
logger.info(f"Writing strategy to `{strategy_path}`.")
strategy_path.write_text(strategy_text)
def start_new_strategy(args: Dict[str, Any]) -> None:
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
if "strategy" in args and args["strategy"]:
if args["strategy"] == "DefaultStrategy":
raise OperationalException("DefaultStrategy is not allowed as name.")
new_path = config['user_data_dir'] / USERPATH_STRATEGY / (args["strategy"] + ".py")
if new_path.exists():
raise OperationalException(f"`{new_path}` already exists. "
"Please choose another Strategy Name.")
deploy_new_strategy(args['strategy'], new_path, args['template'])
else:
raise OperationalException("`new-strategy` requires --strategy to be set.")
def deploy_new_hyperopt(hyperopt_name, hyperopt_path: Path, subtemplate: str):
"""
Deploys a new hyperopt template to hyperopt_path
"""
buy_guards = render_template(
templatefile=f"subtemplates/hyperopt_buy_guards_{subtemplate}.j2",)
sell_guards = render_template(
templatefile=f"subtemplates/hyperopt_sell_guards_{subtemplate}.j2",)
buy_space = render_template(
templatefile=f"subtemplates/hyperopt_buy_space_{subtemplate}.j2",)
sell_space = render_template(
templatefile=f"subtemplates/hyperopt_sell_space_{subtemplate}.j2",)
strategy_text = render_template(templatefile='base_hyperopt.py.j2',
arguments={"hyperopt": hyperopt_name,
"buy_guards": buy_guards,
"sell_guards": sell_guards,
"buy_space": buy_space,
"sell_space": sell_space,
})
logger.info(f"Writing hyperopt to `{hyperopt_path}`.")
hyperopt_path.write_text(strategy_text)
def start_new_hyperopt(args: Dict[str, Any]) -> None:
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
if "hyperopt" in args and args["hyperopt"]:
if args["hyperopt"] == "DefaultHyperopt":
raise OperationalException("DefaultHyperopt is not allowed as name.")
new_path = config['user_data_dir'] / USERPATH_HYPEROPTS / (args["hyperopt"] + ".py")
if new_path.exists():
raise OperationalException(f"`{new_path}` already exists. "
"Please choose another Strategy Name.")
deploy_new_hyperopt(args['hyperopt'], new_path, args['template'])
else:
raise OperationalException("`new-hyperopt` requires --hyperopt to be set.")
def start_download_data(args: Dict[str, Any]) -> None:
"""
Download data (former download_backtest_data.py script)
"""
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
timerange = TimeRange()
if 'days' in config:
time_since = arrow.utcnow().shift(days=-config['days']).strftime("%Y%m%d")
timerange = TimeRange.parse_timerange(f'{time_since}-')
if 'pairs' not in config:
raise OperationalException(
"Downloading data requires a list of pairs. "
"Please check the documentation on how to configure this.")
dl_path = Path(config['datadir'])
logger.info(f'About to download pairs: {config["pairs"]}, '
f'intervals: {config["timeframes"]} to {dl_path}')
pairs_not_available: List[str] = []
# Init exchange
exchange = ExchangeResolver(config['exchange']['name'], config).exchange
try:
if config.get('download_trades'):
pairs_not_available = refresh_backtest_trades_data(
exchange, pairs=config["pairs"], datadir=Path(config['datadir']),
timerange=timerange, erase=config.get("erase"))
# Convert downloaded trade data to different timeframes
convert_trades_to_ohlcv(
pairs=config["pairs"], timeframes=config["timeframes"],
datadir=Path(config['datadir']), timerange=timerange, erase=config.get("erase"))
else:
pairs_not_available = refresh_backtest_ohlcv_data(
exchange, pairs=config["pairs"], timeframes=config["timeframes"],
dl_path=Path(config['datadir']), timerange=timerange, erase=config.get("erase"))
except KeyboardInterrupt:
sys.exit("SIGINT received, aborting ...")
finally:
if pairs_not_available:
logger.info(f"Pairs [{','.join(pairs_not_available)}] not available "
f"on exchange {exchange.name}.")
def start_list_timeframes(args: Dict[str, Any]) -> None:
"""
Print ticker intervals (timeframes) available on Exchange
"""
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
# Do not use ticker_interval set in the config
config['ticker_interval'] = None
# Init exchange
exchange = ExchangeResolver(config['exchange']['name'], config, validate=False).exchange
if args['print_one_column']:
print('\n'.join(exchange.timeframes))
else:
print(f"Timeframes available for the exchange `{exchange.name}`: "
f"{', '.join(exchange.timeframes)}")
def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
"""
Print pairs/markets on the exchange
:param args: Cli args from Arguments()
:param pairs_only: if True print only pairs, otherwise print all instruments (markets)
:return: None
"""
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
# Init exchange
exchange = ExchangeResolver(config['exchange']['name'], config, validate=False).exchange
# By default only active pairs/markets are to be shown
active_only = not args.get('list_pairs_all', False)
base_currencies = args.get('base_currencies', [])
quote_currencies = args.get('quote_currencies', [])
try:
pairs = exchange.get_markets(base_currencies=base_currencies,
quote_currencies=quote_currencies,
pairs_only=pairs_only,
active_only=active_only)
# Sort the pairs/markets by symbol
pairs = OrderedDict(sorted(pairs.items()))
except Exception as e:
raise OperationalException(f"Cannot get markets. Reason: {e}") from e
else:
summary_str = ((f"Exchange {exchange.name} has {len(pairs)} ") +
("active " if active_only else "") +
(plural(len(pairs), "pair" if pairs_only else "market")) +
(f" with {', '.join(base_currencies)} as base "
f"{plural(len(base_currencies), 'currency', 'currencies')}"
if base_currencies else "") +
(" and" if base_currencies and quote_currencies else "") +
(f" with {', '.join(quote_currencies)} as quote "
f"{plural(len(quote_currencies), 'currency', 'currencies')}"
if quote_currencies else ""))
headers = ["Id", "Symbol", "Base", "Quote", "Active",
*(['Is pair'] if not pairs_only else [])]
tabular_data = []
for _, v in pairs.items():
tabular_data.append({'Id': v['id'], 'Symbol': v['symbol'],
'Base': v['base'], 'Quote': v['quote'],
'Active': market_is_active(v),
**({'Is pair': symbol_is_pair(v['symbol'])}
if not pairs_only else {})})
if (args.get('print_one_column', False) or
args.get('list_pairs_print_json', False) or
args.get('print_csv', False)):
# Print summary string in the log in case of machine-readable
# regular formats.
logger.info(f"{summary_str}.")
else:
# Print empty string separating leading logs and output in case of
# human-readable formats.
print()
if len(pairs):
if args.get('print_list', False):
# print data as a list, with human-readable summary
print(f"{summary_str}: {', '.join(pairs.keys())}.")
elif args.get('print_one_column', False):
print('\n'.join(pairs.keys()))
elif args.get('list_pairs_print_json', False):
print(rapidjson.dumps(list(pairs.keys()), default=str))
elif args.get('print_csv', False):
writer = csv.DictWriter(sys.stdout, fieldnames=headers)
writer.writeheader()
writer.writerows(tabular_data)
else:
# print data as a table, with the human-readable summary
print(f"{summary_str}:")
print(tabulate(tabular_data, headers='keys', tablefmt='pipe'))
elif not (args.get('print_one_column', False) or
args.get('list_pairs_print_json', False) or
args.get('print_csv', False)):
print(f"{summary_str}.")
def start_test_pairlist(args: Dict[str, Any]) -> None:
"""
Test Pairlist configuration
"""
from freqtrade.pairlist.pairlistmanager import PairListManager
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
exchange = ExchangeResolver(config['exchange']['name'], config, validate=False).exchange
quote_currencies = args.get('quote_currencies')
if not quote_currencies:
quote_currencies = [config.get('stake_currency')]
results = {}
for curr in quote_currencies:
config['stake_currency'] = curr
# Do not use ticker_interval set in the config
pairlists = PairListManager(exchange, config)
pairlists.refresh_pairlist()
results[curr] = pairlists.whitelist
for curr, pairlist in results.items():
if not args.get('print_one_column', False):
print(f"Pairs for {curr}: ")
if args.get('print_one_column', False):
print('\n'.join(pairlist))
elif args.get('list_pairs_print_json', False):
print(rapidjson.dumps(list(pairlist), default=str))
else:
print(pairlist)
def start_hyperopt_list(args: Dict[str, Any]) -> None:
"""
List hyperopt epochs previously evaluated
"""
from freqtrade.optimize.hyperopt import Hyperopt
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
only_best = config.get('hyperopt_list_best', False)
only_profitable = config.get('hyperopt_list_profitable', False)
print_colorized = config.get('print_colorized', False)
print_json = config.get('print_json', False)
no_details = config.get('hyperopt_list_no_details', False)
no_header = False
trials_file = (config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_results.pickle')
# Previous evaluations
trials = Hyperopt.load_previous_results(trials_file)
total_epochs = len(trials)
trials = _hyperopt_filter_trials(trials, only_best, only_profitable)
# 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)
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 only_best, print_colorized)
except KeyboardInterrupt:
print('User interrupted..')
if trials and not no_details:
sorted_trials = sorted(trials, key=itemgetter('loss'))
results = sorted_trials[0]
Hyperopt.print_epoch_details(results, total_epochs, print_json, no_header)
def start_hyperopt_show(args: Dict[str, Any]) -> None:
"""
Show details of a hyperopt epoch previously evaluated
"""
from freqtrade.optimize.hyperopt import Hyperopt
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
only_best = config.get('hyperopt_list_best', False)
only_profitable = config.get('hyperopt_list_profitable', False)
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)
total_epochs = len(trials)
trials = _hyperopt_filter_trials(trials, only_best, only_profitable)
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}.")
if n < -trials_epochs:
raise OperationalException(
f"The index of the epoch to show should be greater than {-trials_epochs - 1}.")
# Translate epoch index from human-readable format to pythonic
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,
header_str="Epoch details")
def _hyperopt_filter_trials(trials: List, only_best: bool, only_profitable: bool) -> List:
"""
Filter our items from the list of hyperopt results
"""
if only_best:
trials = [x for x in trials if x['is_best']]
if only_profitable:
trials = [x for x in trials if x['results_metrics']['profit'] > 0]
logger.info(f"{len(trials)} " +
("best " if only_best else "") +
("profitable " if only_profitable else "") +
"epochs found.")
return trials

69
freqtrade/wallets.py
View File

@ -2,9 +2,12 @@
""" Wallet """
import logging
from typing import Dict, NamedTuple, Any
from typing import Any, Dict, NamedTuple
import arrow
from freqtrade.exchange import Exchange
from freqtrade import constants
from freqtrade.persistence import Trade
logger = logging.getLogger(__name__)
@ -23,14 +26,12 @@ class Wallets:
self._config = config
self._exchange = exchange
self._wallets: Dict[str, Wallet] = {}
self.start_cap = config['dry_run_wallet']
self._last_wallet_refresh = 0
self.update()
def get_free(self, currency) -> float:
if self._config['dry_run']:
return self._config.get('dry_run_wallet', constants.DRY_RUN_WALLET)
balance = self._wallets.get(currency)
if balance and balance.free:
return balance.free
@ -39,9 +40,6 @@ class Wallets:
def get_used(self, currency) -> float:
if self._config['dry_run']:
return self._config.get('dry_run_wallet', constants.DRY_RUN_WALLET)
balance = self._wallets.get(currency)
if balance and balance.used:
return balance.used
@ -50,16 +48,45 @@ class Wallets:
def get_total(self, currency) -> float:
if self._config['dry_run']:
return self._config.get('dry_run_wallet', constants.DRY_RUN_WALLET)
balance = self._wallets.get(currency)
if balance and balance.total:
return balance.total
else:
return 0
def update(self) -> None:
def _update_dry(self) -> None:
"""
Update from database in dry-run mode
- Apply apply profits of closed trades on top of stake amount
- Subtract currently tied up stake_amount in open trades
- update balances for currencies currently in trades
"""
# Recreate _wallets to reset closed trade balances
_wallets = {}
closed_trades = Trade.get_trades(Trade.is_open.is_(False)).all()
open_trades = Trade.get_trades(Trade.is_open.is_(True)).all()
tot_profit = sum([trade.calc_profit() for trade in closed_trades])
tot_in_trades = sum([trade.stake_amount for trade in open_trades])
current_stake = self.start_cap + tot_profit - tot_in_trades
_wallets[self._config['stake_currency']] = Wallet(
self._config['stake_currency'],
current_stake,
0,
current_stake
)
for trade in open_trades:
curr = trade.pair.split('/')[0]
_wallets[curr] = Wallet(
curr,
trade.amount,
0,
trade.amount
)
self._wallets = _wallets
def _update_live(self) -> None:
balances = self._exchange.get_balances()
@ -71,7 +98,21 @@ class Wallets:
balances[currency].get('total', None)
)
logger.info('Wallets synced.')
def update(self, require_update: bool = True) -> None:
"""
Updates wallets from the configured version.
By default, updates from the exchange.
Update-skipping should only be used for user-invoked /balance calls, since
for trading operations, the latest balance is needed.
:param require_update: Allow skipping an update if balances were recently refreshed
"""
if (require_update or (self._last_wallet_refresh + 3600 < arrow.utcnow().timestamp)):
if self._config['dry_run']:
self._update_dry()
else:
self._update_live()
logger.info('Wallets synced.')
self._last_wallet_refresh = arrow.utcnow().timestamp
def get_all_balances(self) -> Dict[str, Any]:
return self._wallets

28
freqtrade/worker.py
View File

@ -8,11 +8,10 @@ from typing import Any, Callable, Dict, Optional
import sdnotify
from freqtrade import (OperationalException, TemporaryError, __version__,
constants)
from freqtrade import __version__, constants
from freqtrade.configuration import Configuration
from freqtrade.exceptions import OperationalException, TemporaryError
from freqtrade.freqtradebot import FreqtradeBot
from freqtrade.rpc import RPCMessageType
from freqtrade.state import State
logger = logging.getLogger(__name__)
@ -84,10 +83,8 @@ class Worker:
# Log state transition
if state != old_state:
self.freqtrade.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': f'{state.name.lower()}'
})
self.freqtrade.notify_status(f'{state.name.lower()}')
logger.info('Changing state to: %s', state.name)
if state == State.RUNNING:
self.freqtrade.startup()
@ -136,10 +133,9 @@ class Worker:
except OperationalException:
tb = traceback.format_exc()
hint = 'Issue `/start` if you think it is safe to restart.'
self.freqtrade.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': f'OperationalException:\n```\n{tb}```{hint}'
})
self.freqtrade.notify_status(f'OperationalException:\n```\n{tb}```{hint}')
logger.exception('OperationalException. Stopping trader ...')
self.freqtrade.state = State.STOPPED
@ -159,10 +155,7 @@ class Worker:
# Load and validate config and create new instance of the bot
self._init(True)
self.freqtrade.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': 'config reloaded'
})
self.freqtrade.notify_status('config reloaded')
# Tell systemd that we completed reconfiguration
if self._sd_notify:
@ -176,8 +169,5 @@ class Worker:
self._sd_notify.notify("STOPPING=1")
if self.freqtrade:
self.freqtrade.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': 'process died'
})
self.freqtrade.notify_status('process died')
self.freqtrade.cleanup()

12
requirements-common.txt
View File

@ -1,12 +1,12 @@
# requirements without requirements installable via conda
# mainly used for Raspberry pi installs
ccxt==1.20.46
SQLAlchemy==1.3.11
python-telegram-bot==12.2.0
arrow==0.15.4
cachetools==3.1.1
ccxt==1.21.91
SQLAlchemy==1.3.13
python-telegram-bot==12.3.0
arrow==0.15.5
cachetools==4.0.0
requests==2.22.0
urllib3==1.25.7
urllib3==1.25.8
wrapt==1.11.2
jsonschema==3.2.0
TA-Lib==0.4.17

10
requirements-dev.txt
View File

@ -3,15 +3,15 @@
-r requirements-plot.txt
-r requirements-hyperopt.txt
coveralls==1.9.2
coveralls==1.10.0
flake8==3.7.9
flake8-type-annotations==0.1.0
flake8-tidy-imports==3.1.0
mypy==0.750
pytest==5.3.1
flake8-tidy-imports==4.0.0
mypy==0.761
pytest==5.3.4
pytest-asyncio==0.10.0
pytest-cov==2.8.1
pytest-mock==1.13.0
pytest-mock==2.0.0
pytest-random-order==1.0.4
# Convert jupyter notebooks to markdown documents

6
requirements-hyperopt.txt
View File

@ -2,8 +2,8 @@
-r requirements.txt
# Required for hyperopt
scipy==1.3.3
scikit-learn==0.21.3
scipy==1.4.1
scikit-learn==0.22.1
scikit-optimize==0.5.2
filelock==3.0.12
joblib==0.14.0
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==4.3.0
plotly==4.5.0

2
requirements.txt
View File

@ -1,5 +1,5 @@
# Load common requirements
-r requirements-common.txt
numpy==1.17.4
numpy==1.18.1
pandas==0.25.3

12
setup.py
View File

@ -59,7 +59,7 @@ setup(name='freqtrade',
license='GPLv3',
packages=['freqtrade'],
setup_requires=['pytest-runner', 'numpy'],
tests_require=['pytest', 'pytest-mock', 'pytest-cov'],
tests_require=['pytest', 'pytest-asyncio', 'pytest-cov', 'pytest-mock', ],
install_requires=[
# from requirements-common.txt
'ccxt>=1.18.1080',
@ -99,8 +99,12 @@ setup(name='freqtrade',
],
},
classifiers=[
'Programming Language :: Python :: 3.6',
'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
'Topic :: Office/Business :: Financial :: Investment',
'Environment :: Console',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Operating System :: MacOS',
'Operating System :: Unix',
'Topic :: Office/Business :: Financial :: Investment',
])

2
setup.sh
View File

@ -263,7 +263,7 @@ function install() {
echo "-------------------------"
echo "Run the bot !"
echo "-------------------------"
echo "You can now use the bot by executing 'source .env/bin/activate; freqtrade'."
echo "You can now use the bot by executing 'source .env/bin/activate; freqtrade trade'."
}
function plot() {

0
tests/commands/__init__.py Normal file
View File

74
tests/test_utils.py → tests/commands/test_commands.py
View File

@ -4,14 +4,15 @@ from unittest.mock import MagicMock, PropertyMock
import pytest
from freqtrade import OperationalException
from freqtrade.commands import (start_create_userdir, start_download_data,
start_hyperopt_list, start_hyperopt_show,
start_list_exchanges, start_list_markets,
start_list_strategies, start_list_timeframes,
start_new_hyperopt, start_new_strategy,
start_test_pairlist, start_trading)
from freqtrade.configuration import setup_utils_configuration
from freqtrade.exceptions import OperationalException
from freqtrade.state import RunMode
from freqtrade.utils import (setup_utils_configuration, start_create_userdir,
start_download_data, start_list_exchanges,
start_list_markets, start_list_timeframes,
start_new_hyperopt, start_new_strategy,
start_test_pairlist, start_trading,
start_hyperopt_list, start_hyperopt_show)
from tests.conftest import (get_args, log_has, log_has_re, patch_exchange,
patched_configuration_load_config_file)
@ -444,8 +445,14 @@ def test_create_datadir_failed(caplog):
def test_create_datadir(caplog, mocker):
cud = mocker.patch("freqtrade.utils.create_userdata_dir", MagicMock())
csf = mocker.patch("freqtrade.utils.copy_sample_files", MagicMock())
# 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())
args = [
"create-userdir",
"--userdir",
@ -531,7 +538,7 @@ def test_start_new_hyperopt_no_arg(mocker, caplog):
def test_download_data_keyboardInterrupt(mocker, caplog, markets):
dl_mock = mocker.patch('freqtrade.utils.refresh_backtest_ohlcv_data',
dl_mock = mocker.patch('freqtrade.commands.data_commands.refresh_backtest_ohlcv_data',
MagicMock(side_effect=KeyboardInterrupt))
patch_exchange(mocker)
mocker.patch(
@ -549,7 +556,7 @@ def test_download_data_keyboardInterrupt(mocker, caplog, markets):
def test_download_data_no_markets(mocker, caplog):
dl_mock = mocker.patch('freqtrade.utils.refresh_backtest_ohlcv_data',
dl_mock = mocker.patch('freqtrade.commands.data_commands.refresh_backtest_ohlcv_data',
MagicMock(return_value=["ETH/BTC", "XRP/BTC"]))
patch_exchange(mocker, id='binance')
mocker.patch(
@ -567,7 +574,7 @@ def test_download_data_no_markets(mocker, caplog):
def test_download_data_no_exchange(mocker, caplog):
mocker.patch('freqtrade.utils.refresh_backtest_ohlcv_data',
mocker.patch('freqtrade.commands.data_commands.refresh_backtest_ohlcv_data',
MagicMock(return_value=["ETH/BTC", "XRP/BTC"]))
patch_exchange(mocker)
mocker.patch(
@ -587,7 +594,7 @@ def test_download_data_no_pairs(mocker, caplog):
mocker.patch.object(Path, "exists", MagicMock(return_value=False))
mocker.patch('freqtrade.utils.refresh_backtest_ohlcv_data',
mocker.patch('freqtrade.commands.data_commands.refresh_backtest_ohlcv_data',
MagicMock(return_value=["ETH/BTC", "XRP/BTC"]))
patch_exchange(mocker)
mocker.patch(
@ -606,9 +613,9 @@ def test_download_data_no_pairs(mocker, caplog):
def test_download_data_trades(mocker, caplog):
dl_mock = mocker.patch('freqtrade.utils.refresh_backtest_trades_data',
dl_mock = mocker.patch('freqtrade.commands.data_commands.refresh_backtest_trades_data',
MagicMock(return_value=[]))
convert_mock = mocker.patch('freqtrade.utils.convert_trades_to_ohlcv',
convert_mock = mocker.patch('freqtrade.commands.data_commands.convert_trades_to_ohlcv',
MagicMock(return_value=[]))
patch_exchange(mocker)
mocker.patch(
@ -627,11 +634,42 @@ def test_download_data_trades(mocker, caplog):
assert convert_mock.call_count == 1
def test_start_test_pairlist(mocker, caplog, markets, tickers, default_conf, capsys):
def test_start_list_strategies(mocker, caplog, capsys):
args = [
"list-strategies",
"--strategy-path",
str(Path(__file__).parent.parent / "strategy"),
"-1"
]
pargs = get_args(args)
# pargs['config'] = None
start_list_strategies(pargs)
captured = capsys.readouterr()
assert "TestStrategyLegacy" in captured.out
assert "legacy_strategy.py" not in captured.out
assert "DefaultStrategy" in captured.out
# Test regular output
args = [
"list-strategies",
"--strategy-path",
str(Path(__file__).parent.parent / "strategy"),
]
pargs = get_args(args)
# pargs['config'] = None
start_list_strategies(pargs)
captured = capsys.readouterr()
assert "TestStrategyLegacy" in captured.out
assert "legacy_strategy.py" in captured.out
assert "DefaultStrategy" in captured.out
def test_start_test_pairlist(mocker, caplog, tickers, default_conf, capsys):
patch_exchange(mocker, mock_markets=True)
mocker.patch.multiple('freqtrade.exchange.Exchange',
markets=PropertyMock(return_value=markets),
exchange_has=MagicMock(return_value=True),
get_tickers=tickers
get_tickers=tickers,
)
default_conf['pairlists'] = [

135
tests/conftest.py
View File

File diff suppressed because one or more lines are too long

2
tests/data/test_btanalysis.py
View File

@ -20,7 +20,7 @@ def test_load_backtest_data(testdatadir):
filename = testdatadir / "backtest-result_test.json"
bt_data = load_backtest_data(filename)
assert isinstance(bt_data, DataFrame)
assert list(bt_data.columns) == BT_DATA_COLUMNS + ["profitabs"]
assert list(bt_data.columns) == BT_DATA_COLUMNS + ["profit"]
assert len(bt_data) == 179
# Test loading from string (must yield same result)

Some files were not shown because too many files have changed in this diff Show More