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Merge branch 'freqtrade:develop' into strategy_utils

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2
.github/workflows/ci.yml vendored
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@ -360,6 +360,8 @@ jobs:
pip install -e .
- name: Tests incl. ccxt compatibility tests
env:
CI_WEB_PROXY: http://152.67.78.211:13128
run: |
pytest --random-order --cov=freqtrade --cov-config=.coveragerc --longrun

14
.pre-commit-config.yaml
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@ -2,33 +2,33 @@
# See https://pre-commit.com/hooks.html for more hooks
repos:
- repo: https://github.com/pycqa/flake8
rev: "4.0.1"
rev: "6.0.0"
hooks:
- id: flake8
# stages: [push]
- repo: https://github.com/pre-commit/mirrors-mypy
rev: "v0.942"
rev: "v0.991"
hooks:
- id: mypy
exclude: build_helpers
additional_dependencies:
- types-cachetools==5.2.1
- types-cachetools==5.3.0.0
- types-filelock==3.2.7
- types-requests==2.28.11.7
- types-requests==2.28.11.8
- types-tabulate==0.9.0.0
- types-python-dateutil==2.8.19.5
- types-python-dateutil==2.8.19.6
# stages: [push]
- repo: https://github.com/pycqa/isort
rev: "5.10.1"
rev: "5.12.0"
hooks:
- id: isort
name: isort (python)
# stages: [push]
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v2.4.0
rev: v4.4.0
hooks:
- id: end-of-file-fixer
exclude: |

5
README.md
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@ -40,6 +40,7 @@ Please read the [exchange specific notes](docs/exchanges.md) to learn about even
- [X] [Binance](https://www.binance.com/)
- [X] [Gate.io](https://www.gate.io/ref/6266643)
- [X] [OKX](https://okx.com/)
- [X] [Bybit](https://bybit.com/)
Please make sure to read the [exchange specific notes](docs/exchanges.md), as well as the [trading with leverage](docs/leverage.md) documentation before diving in.
@ -164,6 +165,10 @@ first. If it hasn't been reported, please
ensure you follow the template guide so that the team can assist you as
quickly as possible.
For every [issue](https://github.com/freqtrade/freqtrade/issues/new/choose) created, kindly follow up and mark satisfaction or reminder to close issue when equilibrium ground is reached.
--Maintain github's [community policy](https://docs.github.com/en/site-policy/github-terms/github-community-code-of-conduct)--
### [Feature Requests](https://github.com/freqtrade/freqtrade/labels/enhancement)
Have you a great idea to improve the bot you want to share? Please,

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build_helpers/TA_Lib-0.4.25-cp311-cp311-win_amd64.whl Normal file
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3
build_helpers/install_windows.ps1
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@ -14,5 +14,8 @@ if ($pyv -eq '3.9') {
if ($pyv -eq '3.10') {
pip install build_helpers\TA_Lib-0.4.25-cp310-cp310-win_amd64.whl
}
if ($pyv -eq '3.11') {
pip install build_helpers\TA_Lib-0.4.25-cp311-cp311-win_amd64.whl
}
pip install -r requirements-dev.txt
pip install -e .

9
build_helpers/publish_docker_arm64.sh
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@ -70,20 +70,21 @@ docker push ${CACHE_IMAGE}:$TAG_ARM
# Otherwise installation might fail.
echo "create manifests"
docker manifest create --amend ${IMAGE_NAME}:${TAG} ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI} ${CACHE_IMAGE}:${TAG}
docker manifest create ${IMAGE_NAME}:${TAG} ${CACHE_IMAGE}:${TAG} ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI}
docker manifest push -p ${IMAGE_NAME}:${TAG}
docker manifest create ${IMAGE_NAME}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT_ARM} ${CACHE_IMAGE}:${TAG_PLOT}
docker manifest create ${IMAGE_NAME}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT_ARM}
docker manifest push -p ${IMAGE_NAME}:${TAG_PLOT}
docker manifest create ${IMAGE_NAME}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI_ARM} ${CACHE_IMAGE}:${TAG_FREQAI}
docker manifest create ${IMAGE_NAME}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI_ARM}
docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI}
docker manifest create ${IMAGE_NAME}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL_ARM} ${CACHE_IMAGE}:${TAG_FREQAI_RL}
docker manifest create ${IMAGE_NAME}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL_ARM}
docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI_RL}
# Tag as latest for develop builds
if [ "${TAG}" = "develop" ]; then
echo 'Tagging image as latest'
docker manifest create ${IMAGE_NAME}:latest ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI} ${CACHE_IMAGE}:${TAG}
docker manifest push -p ${IMAGE_NAME}:latest
fi

15
build_helpers/publish_docker_multi.sh
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@ -26,7 +26,10 @@ if [ "${GITHUB_EVENT_NAME}" = "schedule" ]; then
--cache-to=type=registry,ref=${CACHE_TAG} \
-f docker/Dockerfile.armhf \
--platform ${PI_PLATFORM} \
-t ${IMAGE_NAME}:${TAG_PI} --push .
-t ${IMAGE_NAME}:${TAG_PI} \
--push \
--provenance=false \
.
else
echo "event ${GITHUB_EVENT_NAME}: building with cache"
# Build regular image
@ -35,12 +38,16 @@ else
# Pull last build to avoid rebuilding the whole image
# docker pull --platform ${PI_PLATFORM} ${IMAGE_NAME}:${TAG}
# disable provenance due to https://github.com/docker/buildx/issues/1509
docker buildx build \
--cache-from=type=registry,ref=${CACHE_TAG} \
--cache-to=type=registry,ref=${CACHE_TAG} \
-f docker/Dockerfile.armhf \
--platform ${PI_PLATFORM} \
-t ${IMAGE_NAME}:${TAG_PI} --push .
-t ${IMAGE_NAME}:${TAG_PI} \
--push \
--provenance=false \
.
fi
if [ $? -ne 0 ]; then
@ -68,12 +75,10 @@ fi
docker images
docker push ${CACHE_IMAGE}
docker push ${CACHE_IMAGE}:$TAG
docker push ${CACHE_IMAGE}:$TAG_PLOT
docker push ${CACHE_IMAGE}:$TAG_FREQAI
docker push ${CACHE_IMAGE}:$TAG_FREQAI_RL
docker push ${CACHE_IMAGE}:$TAG
docker images

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build_helpers/pyarrow-10.0.0-cp39-cp39-linux_armv7l.whl → build_helpers/pyarrow-11.0.0-cp39-cp39-linux_armv7l.whl
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14
config_examples/config_binance.example.json
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@ -59,20 +59,6 @@
"pairlists": [
{"method": "StaticPairList"}
],
"edge": {
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"minimum_winrate": 0.60,
"minimum_expectancy": 0.20,
"min_trade_number": 10,
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"telegram": {
"enabled": false,
"token": "your_telegram_token",

14
config_examples/config_bittrex.example.json
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@ -56,20 +56,6 @@
"pairlists": [
{"method": "StaticPairList"}
],
"edge": {
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"minimum_winrate": 0.60,
"minimum_expectancy": 0.20,
"min_trade_number": 10,
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"telegram": {
"enabled": false,
"token": "your_telegram_token",

8
config_examples/config_freqai.example.json
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@ -21,8 +21,8 @@
"ccxt_config": {},
"ccxt_async_config": {},
"pair_whitelist": [
"1INCH/USDT",
"ALGO/USDT"
"1INCH/USDT:USDT",
"ALGO/USDT:USDT"
],
"pair_blacklist": []
},
@ -60,8 +60,8 @@
"1h"
],
"include_corr_pairlist": [
"BTC/USDT",
"ETH/USDT"
"BTC/USDT:USDT",
"ETH/USDT:USDT"
],
"label_period_candles": 20,
"include_shifted_candles": 2,

1
config_examples/config_full.example.json
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@ -60,6 +60,7 @@
"force_entry": "market",
"stoploss": "market",
"stoploss_on_exchange": false,
"stoploss_price_type": "last",
"stoploss_on_exchange_interval": 60,
"stoploss_on_exchange_limit_ratio": 0.99
},

14
config_examples/config_kraken.example.json
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@ -64,20 +64,6 @@
"pairlists": [
{"method": "StaticPairList"}
],
"edge": {
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"minimum_winrate": 0.60,
"minimum_expectancy": 0.20,
"min_trade_number": 10,
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"telegram": {
"enabled": false,
"token": "your_telegram_token",

3
docs/advanced-backtesting.md
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@ -32,7 +32,7 @@ To analyze the entry/exit tags, we now need to use the `freqtrade backtesting-an
with `--analysis-groups` option provided with space-separated arguments (default `0 1 2`):
``` bash
freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4
freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4 5
```
This command will read from the last backtesting results. The `--analysis-groups` option is
@ -43,6 +43,7 @@ ranging from the simplest (0) to the most detailed per pair, per buy and per sel
* 2: profit summaries grouped by enter_tag and exit_tag
* 3: profit summaries grouped by pair and enter_tag
* 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
* 5: profit summaries grouped by exit_tag
More options are available by running with the `-h` option.

8
docs/advanced-hyperopt.md
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@ -75,7 +75,7 @@ This function needs to return a floating point number (`float`). Smaller numbers
## Overriding pre-defined spaces
To override a pre-defined space (`roi_space`, `generate_roi_table`, `stoploss_space`, `trailing_space`), define a nested class called Hyperopt and define the required spaces as follows:
To override a pre-defined space (`roi_space`, `generate_roi_table`, `stoploss_space`, `trailing_space`, `max_open_trades_space`), define a nested class called Hyperopt and define the required spaces as follows:
```python
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal
@ -123,6 +123,12 @@ class MyAwesomeStrategy(IStrategy):
Categorical([True, False], name='trailing_only_offset_is_reached'),
]
# Define a custom max_open_trades space
def max_open_trades_space(self) -> List[Dimension]:
return [
Integer(-1, 10, name='max_open_trades'),
]
```
!!! Note

4
docs/bot-basics.md
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@ -75,3 +75,7 @@ This loop will be repeated again and again until the bot is stopped.
!!! Note
Both Backtesting and Hyperopt include exchange default Fees in the calculation. Custom fees can be passed to backtesting / hyperopt by specifying the `--fee` argument.
!!! Warning "Callback call frequency"
Backtesting will call each callback at max. once per candle (`--timeframe-detail` modifies this behavior to once per detailed candle).
Most callbacks will be called once per iteration in live (usually every ~5s) - which can cause backtesting mismatches.

8
docs/configuration.md
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@ -11,7 +11,7 @@ Per default, the bot loads the configuration from the `config.json` file, locate
You can specify a different configuration file used by the bot with the `-c/--config` command-line option.
If you used the [Quick start](installation.md/#quick-start) method for installing
If you used the [Quick start](docker_quickstart.md#docker-quick-start) method for installing
the bot, the installation script should have already created the default configuration file (`config.json`) for you.
If the default configuration file is not created we recommend to use `freqtrade new-config --config config.json` to generate a basic configuration file.
@ -134,7 +134,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| Parameter | Description |
|------------|-------------|
| `max_open_trades` | **Required.** Number of open trades your bot is allowed to have. Only one open trade per pair is possible, so the length of your pairlist is another limitation that can apply. If -1 then it is ignored (i.e. potentially unlimited open trades, limited by the pairlist). [More information below](#configuring-amount-per-trade).<br> **Datatype:** Positive integer or -1.
| `max_open_trades` | **Required.** Number of open trades your bot is allowed to have. Only one open trade per pair is possible, so the length of your pairlist is another limitation that can apply. If -1 then it is ignored (i.e. potentially unlimited open trades, limited by the pairlist). [More information below](#configuring-amount-per-trade). [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Positive integer or -1.
| `stake_currency` | **Required.** Crypto-currency used for trading. <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](#configuring-amount-per-trade). <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`.
@ -263,6 +263,7 @@ Values set in the configuration file always overwrite values set in the strategy
* `minimal_roi`
* `timeframe`
* `stoploss`
* `max_open_trades`
* `trailing_stop`
* `trailing_stop_positive`
* `trailing_stop_positive_offset`
@ -665,7 +666,7 @@ You should also make sure to read the [Exchanges](exchanges.md) section of the d
### Using proxy with Freqtrade
To use a proxy with freqtrade, export your proxy settings using the variables `"HTTP_PROXY"` and `"HTTPS_PROXY"` set to the appropriate values.
This will have the proxy settings applied to everything (telegram, coingecko, ...) except exchange requests.
This will have the proxy settings applied to everything (telegram, coingecko, ...) **except** for exchange requests.
``` bash
export HTTP_PROXY="http://addr:port"
@ -688,6 +689,7 @@ To use a proxy for exchange connections - you will have to define the proxies as
},
}
}
}
```
## Next step

35
docs/exchanges.md
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@ -75,6 +75,25 @@ Binance has been split into 2, and users must use the correct ccxt exchange ID f
* [binance.com](https://www.binance.com/) - International users. Use exchange id: `binance`.
* [binance.us](https://www.binance.us/) - US based users. Use exchange id: `binanceus`.
### Binance RSA keys
Freqtrade supports binance RSA API keys.
We recommend to use them as environment variable.
``` bash
export FREQTRADE__EXCHANGE__SECRET="$(cat ./rsa_binance.private)"
```
They can however also be configured via configuration file. Since json doesn't support multi-line strings, you'll have to replace all newlines with `\n` to have a valid json file.
``` json
// ...
"key": "<someapikey>",
"secret": "-----BEGIN PRIVATE KEY-----\nMIIEvQIBABACAFQA<...>s8KX8=\n-----END PRIVATE KEY-----"
// ...
```
### Binance Futures
Binance has specific (unfortunately complex) [Futures Trading Quantitative Rules](https://www.binance.com/en/support/faq/4f462ebe6ff445d4a170be7d9e897272) which need to be followed, and which prohibit a too low stake-amount (among others) for too many orders.
@ -224,8 +243,8 @@ OKX requires a passphrase for each api key, you will therefore need to add this
OKX only provides 100 candles per api call. Therefore, the strategy will only have a pretty low amount of data available in backtesting mode.
!!! Warning "Futures"
OKX Futures has the concept of "position mode" - which can be Net or long/short (hedge mode).
Freqtrade supports both modes (we recommend to use net mode) - but changing the mode mid-trading is not supported and will lead to exceptions and failures to place trades.
OKX Futures has the concept of "position mode" - which can be "Buy/Sell" or long/short (hedge mode).
Freqtrade supports both modes (we recommend to use Buy/Sell mode) - but changing the mode mid-trading is not supported and will lead to exceptions and failures to place trades.
OKX also only provides MARK candles for the past ~3 months. Backtesting futures prior to that date will therefore lead to slight deviations, as funding-fees cannot be calculated correctly without this data.
## Gate.io
@ -236,6 +255,18 @@ OKX requires a passphrase for each api key, you will therefore need to add this
Gate.io allows the use of `POINT` to pay for fees. As this is not a tradable currency (no regular market available), automatic fee calculations will fail (and default to a fee of 0).
The configuration parameter `exchange.unknown_fee_rate` can be used to specify the exchange rate between Point and the stake currency. Obviously, changing the stake-currency will also require changes to this value.
## Bybit
Futures trading on bybit is currently supported for USDT markets, and will use isolated futures mode.
Users with unified accounts (there's no way back) can create a Sub-account which will start as "non-unified", and can therefore use isolated futures.
On startup, freqtrade will set the position mode to "One-way Mode" for the whole (sub)account. This avoids making this call over and over again (slowing down bot operations), but means that changes to this setting may result in exceptions and errors.
As bybit doesn't provide funding rate history, the dry-run calculation is used for live trades as well.
!!! Tip "Stoploss on Exchange"
Bybit (futures only) supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange.
On futures, Bybit supports both `stop-limit` as well as `stop-market` orders. You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type to use.
## All exchanges
Should you experience constant errors with Nonce (like `InvalidNonce`), it is best to regenerate the API keys. Resetting Nonce is difficult and it's usually easier to regenerate the API keys.

167
docs/freqai-configuration.md
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@ -43,116 +43,113 @@ The FreqAI strategy requires including the following lines of code in the standa
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# the model will return all labels created by user in `populate_any_indicators`
# the model will return all labels created by user in `set_freqai_labels()`
# (& appended targets), an indication of whether or not the prediction should be accepted,
# the target mean/std values for each of the labels created by user in
# `populate_any_indicators()` for each training period.
# `feature_engineering_*` for each training period.
dataframe = self.freqai.start(dataframe, metadata, self)
return dataframe
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
def feature_engineering_expand_all(self, dataframe, period, **kwargs):
"""
Function designed to automatically generate, name and merge features
from user indicated timeframes in the configuration file. User controls the indicators
passed to the training/prediction by prepending indicators with `'%-' + pair `
(see convention below). I.e. user should not prepend any supporting metrics
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
`include_corr_pairs`. In other words, a single feature defined in this function
will automatically expand to a total of
`indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
`include_corr_pairs` numbers of features added to the model.
All features must be prepended with `%` to be recognized by FreqAI internals.
:param df: strategy dataframe which will receive the features
:param period: period of the indicator - usage example:
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
"""
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, window=t)
return dataframe
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
def feature_engineering_expand_basic(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
In other words, a single feature defined in this function
will automatically expand to a total of
`include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
numbers of features added to the model.
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
Features defined here will *not* be automatically duplicated on user defined
`indicator_periods_candles`
# Add generalized indicators here (because in live, it will call this
# function to populate indicators during training). Notice how we ensure not to
# add them multiple times
if set_generalized_indicators:
All features must be prepended with `%` to be recognized by FreqAI internals.
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
df["&-s_close"] = (
df["close"]
:param df: strategy dataframe which will receive the features
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
"""
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"]
dataframe["%-raw_price"] = dataframe["close"]
return dataframe
def feature_engineering_standard(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This optional function will be called once with the dataframe of the base timeframe.
This is the final function to be called, which means that the dataframe entering this
function will contain all the features and columns created by all other
freqai_feature_engineering_* functions.
This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
This function is a good place for any feature that should not be auto-expanded upon
(e.g. day of the week).
All features must be prepended with `%` to be recognized by FreqAI internals.
:param df: strategy dataframe which will receive the features
usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
"""
dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
dataframe["%-hour_of_day"] = (dataframe["date"].dt.hour + 1) / 25
return dataframe
def set_freqai_targets(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
Required function to set the targets for the model.
All targets must be prepended with `&` to be recognized by the FreqAI internals.
:param df: strategy dataframe which will receive the targets
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
"""
dataframe["&-s_close"] = (
dataframe["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ df["close"]
/ dataframe["close"]
- 1
)
return df
```
Notice how the `populate_any_indicators()` is where [features](freqai-feature-engineering.md#feature-engineering) and labels/targets are added. A full example strategy is available in `templates/FreqaiExampleStrategy.py`.
Notice also the location of the labels under `if set_generalized_indicators:` at the bottom of the example. This is where single features and labels/targets should be added to the feature set to avoid duplication of them from various configuration parameters that multiply the feature set, such as `include_timeframes`.
Notice how the `feature_engineering_*()` is where [features](freqai-feature-engineering.md#feature-engineering) are added. Meanwhile `set_freqai_targets()` adds the labels/targets. A full example strategy is available in `templates/FreqaiExampleStrategy.py`.
!!! Note
The `self.freqai.start()` function cannot be called outside the `populate_indicators()`.
!!! Note
Features **must** be defined in `populate_any_indicators()`. Defining FreqAI features in `populate_indicators()`
will cause the algorithm to fail in live/dry mode. In order to add generalized features that are not associated with a specific pair or timeframe, the following structure inside `populate_any_indicators()` should be used
(as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`):
```python
def populate_any_indicators(self, pair, df, tf, informative=None, set_generalized_indicators=False):
...
# Add generalized indicators here (because in live, it will call only this function to populate
# indicators for retraining). Notice how we ensure not to add them multiple times by associating
# these generalized indicators to the basepair/timeframe
if set_generalized_indicators:
df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7
df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
df["&-s_close"] = (
df["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ df["close"]
- 1
)
```
Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`.
Features **must** be defined in `feature_engineering_*()`. Defining FreqAI features in `populate_indicators()`
will cause the algorithm to fail in live/dry mode. In order to add generalized features that are not associated with a specific pair or timeframe, you should use `feature_engineering_standard()`
(as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`).
## Important dataframe key patterns
@ -160,11 +157,11 @@ Below are the values you can expect to include/use inside a typical strategy dat
| DataFrame Key | Description |
|------------|-------------|
| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). For example, to predict the close price 40 candles into the future, you would set `df['&-s_close'] = df['close'].shift(-self.freqai_info["feature_parameters"]["label_period_candles"])` with `"label_period_candles": 40` in the config. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** Depends on the output of the model.
| `df['&*']` | Any dataframe column prepended with `&` in `set_freqai_targets()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). For example, to predict the close price 40 candles into the future, you would set `df['&-s_close'] = df['close'].shift(-self.freqai_info["feature_parameters"]["label_period_candles"])` with `"label_period_candles": 40` in the config. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** Depends on the output of the model.
| `df['&*_std/mean']` | Standard deviation and mean values of the defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` and explained [here](#creating-a-dynamic-target-threshold) to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`). <br> **Datatype:** Float.
| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -2 and 2, which lets you know if the prediction is trustworthy or not. `do_predict==1` means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di)) of the input data point is above the threshold defined in the config, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM, see details [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm)) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. As with the SVM, if `use_DBSCAN_to_remove_outliers` is active, DBSCAN (see details [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan)) may also detect outliers and subtract 1 from `do_predict`. Hence, if both the SVM and DBSCAN are active and identify a datapoint that was above the DI threshold as an outlier, the result will be `do_predict==-2`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`. <br> **Datatype:** Integer between -2 and 2.
| `df['DI_values']` | Dissimilarity Index (DI) values are proxies for the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. See details about the DI [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Float.
| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features are easily engineered using the multiplictative functionality of, e.g., `include_shifted_candles` and `include_timeframes` as described in the [parameter table](freqai-parameter-table.md)), these features are removed from the dataframe that is returned from FreqAI to the strategy. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`. <br> **Datatype:** Depends on the output of the model.
| `df['%*']` | Any dataframe column prepended with `%` in `feature_engineering_*()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features are easily engineered using the multiplictative functionality of, e.g., `include_shifted_candles` and `include_timeframes` as described in the [parameter table](freqai-parameter-table.md)), these features are removed from the dataframe that is returned from FreqAI to the strategy. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`. <br> **Datatype:** Depends on the output of the model.
## Setting the `startup_candle_count`

203
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View File

@ -2,96 +2,150 @@
## Defining the features
Low level feature engineering is performed in the user strategy within a function called `populate_any_indicators()`. That function sets the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. One important syntax rule is that all `base features` string names are prepended with `%-{pair}`, while labels/targets are prepended with `&`.
Low level feature engineering is performed in the user strategy within a set of functions called `feature_engineering_*`. These function set the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. FreqAI is equipped with a set of functions to simplify rapid large-scale feature engineering:
!!! Note
Adding the full pair string, e.g. XYZ/USD, in the feature name enables improved performance for dataframe caching on the backend. If you decide *not* to add the full pair string in the feature string, FreqAI will operate in a reduced performance mode.
| Function | Description |
|---------------|-------------|
| `feature_engineering__expand_all()` | This optional function will automatically expand the defined features on the config defined `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
| `feature_engineering__expand_basic()` | This optional function will automatically expand the defined features on the config defined `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`. Note: this function does *not* expand across `include_periods_candles`.
| `feature_engineering_standard()` | This optional function will be called once with the dataframe of the base timeframe. This is the final function to be called, which means that the dataframe entering this function will contain all the features and columns from the base asset created by the other `feature_engineering_expand` functions. This function is a good place to do custom exotic feature extractions (e.g. tsfresh). This function is also a good place for any feature that should not be auto-expanded upon (e.g. day of the week).
| `set_freqai_targets()` | Required function to set the targets for the model. All targets must be prepended with `&` to be recognized by the FreqAI internals.
Meanwhile, high level feature engineering is handled within `"feature_parameters":{}` in the FreqAI config. Within this file, it is possible to decide large scale feature expansions on top of the `base_features` such as "including correlated pairs" or "including informative timeframes" or even "including recent candles."
It is advisable to start from the template `populate_any_indicators()` in the source provided example strategy (found in `templates/FreqaiExampleStrategy.py`) to ensure that the feature definitions are following the correct conventions. Here is an example of how to set the indicators and labels in the strategy:
It is advisable to start from the template `feature_engineering_*` functions in the source provided example strategy (found in `templates/FreqaiExampleStrategy.py`) to ensure that the feature definitions are following the correct conventions. Here is an example of how to set the indicators and labels in the strategy:
```python
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
def feature_engineering_expand_all(self, dataframe, period, metadata, **kwargs):
"""
Function designed to automatically generate, name, and merge features
from user-indicated timeframes in the configuration file. The user controls the indicators
passed to the training/prediction by prepending indicators with `'%-' + pair `
(see convention below). I.e., the user should not prepend any supporting metrics
(e.g., bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
`include_corr_pairs`. In other words, a single feature defined in this function
will automatically expand to a total of
`indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
`include_corr_pairs` numbers of features added to the model.
All features must be prepended with `%` to be recognized by FreqAI internals.
Access metadata such as the current pair/timeframe/period with:
`metadata["pair"]` `metadata["tf"]` `metadata["period"]`
:param df: strategy dataframe which will receive the features
:param period: period of the indicator - usage example:
:param metadata: metadata of current pair
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
"""
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, window=t)
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(informative), window=t, stds=2.2
qtpylib.typical_price(dataframe), window=period, stds=2.2
)
informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
dataframe["bb_lowerband-period"] = bollinger["lower"]
dataframe["bb_middleband-period"] = bollinger["mid"]
dataframe["bb_upperband-period"] = bollinger["upper"]
informative[f"%-{pair}bb_width-period_{t}"] = (
informative[f"{pair}bb_upperband-period_{t}"]
- informative[f"{pair}bb_lowerband-period_{t}"]
) / informative[f"{pair}bb_middleband-period_{t}"]
informative[f"%-{pair}close-bb_lower-period_{t}"] = (
informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
dataframe["%-bb_width-period"] = (
dataframe["bb_upperband-period"]
- dataframe["bb_lowerband-period"]
) / dataframe["bb_middleband-period"]
dataframe["%-close-bb_lower-period"] = (
dataframe["close"] / dataframe["bb_lowerband-period"]
)
informative[f"%-{pair}relative_volume-period_{t}"] = (
informative["volume"] / informative["volume"].rolling(t).mean()
dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
dataframe["%-relative_volume-period"] = (
dataframe["volume"] / dataframe["volume"].rolling(period).mean()
)
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
return dataframe
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
def feature_engineering_expand_basic(self, dataframe, metadata, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
In other words, a single feature defined in this function
will automatically expand to a total of
`include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
numbers of features added to the model.
# Add generalized indicators here (because in live, it will call this
# function to populate indicators during training). Notice how we ensure not to
# add them multiple times
if set_generalized_indicators:
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
Features defined here will *not* be automatically duplicated on user defined
`indicator_periods_candles`
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
df["&-s_close"] = (
df["close"]
Access metadata such as the current pair/timeframe with:
`metadata["pair"]` `metadata["tf"]`
All features must be prepended with `%` to be recognized by FreqAI internals.
:param df: strategy dataframe which will receive the features
:param metadata: metadata of current pair
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
"""
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"]
dataframe["%-raw_price"] = dataframe["close"]
return dataframe
def feature_engineering_standard(self, dataframe, metadata, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This optional function will be called once with the dataframe of the base timeframe.
This is the final function to be called, which means that the dataframe entering this
function will contain all the features and columns created by all other
freqai_feature_engineering_* functions.
This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
This function is a good place for any feature that should not be auto-expanded upon
(e.g. day of the week).
Access metadata such as the current pair with:
`metadata["pair"]`
All features must be prepended with `%` to be recognized by FreqAI internals.
:param df: strategy dataframe which will receive the features
:param metadata: metadata of current pair
usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
"""
dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
dataframe["%-hour_of_day"] = (dataframe["date"].dt.hour + 1) / 25
return dataframe
def set_freqai_targets(self, dataframe, metadata, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
Required function to set the targets for the model.
All targets must be prepended with `&` to be recognized by the FreqAI internals.
Access metadata such as the current pair with:
`metadata["pair"]`
:param df: strategy dataframe which will receive the targets
:param metadata: metadata of current pair
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
"""
dataframe["&-s_close"] = (
dataframe["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ df["close"]
/ dataframe["close"]
- 1
)
return df
return dataframe
```
In the presented example, the user does not wish to pass the `bb_lowerband` as a feature to the model,
@ -118,15 +172,28 @@ After having defined the `base features`, the next step is to expand upon them u
}
```
The `include_timeframes` in the config above are the timeframes (`tf`) of each call to `populate_any_indicators()` in the strategy. In the presented case, the user is asking for the `5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set.
The `include_timeframes` in the config above are the timeframes (`tf`) of each call to `feature_engineering_expand_*()` in the strategy. In the presented case, the user is asking for the `5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set.
You can ask for each of the defined features to be included also for informative pairs using the `include_corr_pairlist`. This means that the feature set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD` in the presented example).
You can ask for each of the defined features to be included also for informative pairs using the `include_corr_pairlist`. This means that the feature set will include all the features from `feature_engineering_expand_*()` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD` in the presented example).
`include_shifted_candles` indicates the number of previous candles to include in the feature set. For example, `include_shifted_candles: 2` tells FreqAI to include the past 2 candles for each of the features in the feature set.
In total, the number of features the user of the presented example strat has created is: length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`
In total, the number of features the user of the presented example strat has created is: length of `include_timeframes` * no. features in `feature_engineering_expand_*()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`
$= 3 * 3 * 3 * 2 * 2 = 108$.
### Gain finer control over `feature_engineering_*` functions with `metadata`
All `feature_engineering_*` and `set_freqai_targets()` functions are passed a `metadata` dictionary which contains information about the `pair`, `tf` (timeframe), and `period` that FreqAI is automating for feature building. As such, a user can use `metadata` inside `feature_engineering_*` functions as criteria for blocking/reserving features for certain timeframes, periods, pairs etc.
```py
def feature_engineering_expand_all(self, dataframe, period, metadata, **kwargs):
if metadata["tf"] == "1h":
dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
```
This will block `ta.ROC()` from being added to any timeframes other than `"1h"`.
### Returning additional info from training
Important metrics can be returned to the strategy at the end of each model training by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside the custom prediction model class.
@ -167,7 +234,7 @@ This will perform PCA on the features and reduce their dimensionality so that th
## Inlier metric
The `inlier_metric` is a metric aimed at quantifying how similar a the features of a data point are to the most recent historic data points.
The `inlier_metric` is a metric aimed at quantifying how similar the features of a data point are to the most recent historical data points.
You define the lookback window by setting `inlier_metric_window` and FreqAI computes the distance between the present time point and each of the previous `inlier_metric_window` lookback points. A Weibull function is fit to each of the lookback distributions and its cumulative distribution function (CDF) is used to produce a quantile for each lookback point. The `inlier_metric` is then computed for each time point as the average of the corresponding lookback quantiles. The figure below explains the concept for an `inlier_metric_window` of 5.

9
docs/freqai-parameter-table.md
View File

@ -18,7 +18,6 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
| `purge_old_models` | Delete all unused models during live runs (not relevant to backtesting). If set to false (not default), dry/live runs will accumulate all unused models to disk. If <br> **Datatype:** Boolean. <br> Default: `True`.
| `save_backtest_models` | Save models to disk when running backtesting. Backtesting operates most efficiently by saving the prediction data and reusing them directly for subsequent runs (when you wish to tune entry/exit parameters). Saving backtesting models to disk also allows to use the same model files for starting a dry/live instance with the same model `identifier`. <br> **Datatype:** Boolean. <br> Default: `False` (no models are saved).
| `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training dataset (more information can be found [here](freqai-configuration.md#creating-a-dynamic-target-threshold)). <br> **Datatype:** Positive integer.
| `follow_mode` | Use a `follower` that will look for models associated with a specific `identifier` and load those for inferencing. A `follower` will **not** train new models. <br> **Datatype:** Boolean. <br> Default: `False`.
| `continual_learning` | Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning (more information can be found [here](freqai-running.md#continual-learning)). <br> **Datatype:** Boolean. <br> Default: `False`.
| `write_metrics_to_disk` | Collect train timings, inference timings and cpu usage in json file. <br> **Datatype:** Boolean. <br> Default: `False`
| `data_kitchen_thread_count` | <br> Designate the number of threads you want to use for data processing (outlier methods, normalization, etc.). This has no impact on the number of threads used for training. If user does not set it (default), FreqAI will use max number of threads - 2 (leaving 1 physical core available for Freqtrade bot and FreqUI) <br> **Datatype:** Positive integer.
@ -29,12 +28,12 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
|------------|-------------|
| | **Feature parameters within the `freqai.feature_parameters` sub dictionary**
| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](freqai-feature-engineering.md). <br> **Datatype:** Dictionary.
| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base indicators dataset. <br> **Datatype:** List of timeframes (strings).
| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset. <br> **Datatype:** List of assets (strings).
| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators` (see `templates/FreqaiExampleStrategy.py` for detailed usage). You can create custom labels and choose whether to make use of this parameter or not. <br> **Datatype:** Positive integer.
| `include_timeframes` | A list of timeframes that all indicators in `feature_engineering_expand_*()` will be created for. The list is added as features to the base indicators dataset. <br> **Datatype:** List of timeframes (strings).
| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `feature_engineering_expand_*()` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset. <br> **Datatype:** List of assets (strings).
| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `feature_engineering_expand_all()` (see `templates/FreqaiExampleStrategy.py` for detailed usage). You can create custom labels and choose whether to make use of this parameter or not. <br> **Datatype:** Positive integer.
| `include_shifted_candles` | Add features from previous candles to subsequent candles with the intent of adding historical information. If used, FreqAI will duplicate and shift all features from the `include_shifted_candles` previous candles so that the information is available for the subsequent candle. <br> **Datatype:** Positive integer.
| `weight_factor` | Weight training data points according to their recency (see details [here](freqai-feature-engineering.md#weighting-features-for-temporal-importance)). <br> **Datatype:** Positive float (typically < 1).
| `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `populate_any_indicators()` for indicator creation. FreqAI uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN. <br> **Datatype:** Positive integer.
| `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `feature_engineering_*()` for indicator creation. FreqAI uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN. <br> **Datatype:** Positive integer.
| `indicator_periods_candles` | Time periods to calculate indicators for. The indicators are added to the base indicator dataset. <br> **Datatype:** List of positive integers.
| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis) <br> **Datatype:** Boolean. <br> Default: `False`.
| `plot_feature_importances` | Create a feature importance plot for each model for the top/bottom `plot_feature_importances` number of features. Plot is stored in `user_data/models/<identifier>/sub-train-<COIN>_<timestamp>.html`. <br> **Datatype:** Integer. <br> Default: `0`.

80
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@ -34,65 +34,36 @@ Setting up and running a Reinforcement Learning model is the same as running a R
freqtrade trade --freqaimodel ReinforcementLearner --strategy MyRLStrategy --config config.json
```
where `ReinforcementLearner` will use the templated `ReinforcementLearner` from `freqai/prediction_models/ReinforcementLearner` (or a custom user defined one located in `user_data/freqaimodels`). The strategy, on the other hand, follows the same base [feature engineering](freqai-feature-engineering.md) with `populate_any_indicators` as a typical Regressor:
where `ReinforcementLearner` will use the templated `ReinforcementLearner` from `freqai/prediction_models/ReinforcementLearner` (or a custom user defined one located in `user_data/freqaimodels`). The strategy, on the other hand, follows the same base [feature engineering](freqai-feature-engineering.md) with `feature_engineering_*` as a typical Regressor. The difference lies in the creation of the targets, Reinforcement Learning doesn't require them. However, FreqAI requires a default (neutral) value to be set in the action column:
```python
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
def set_freqai_targets(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
Required function to set the targets for the model.
All targets must be prepended with `&` to be recognized by the FreqAI internals.
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
More details about feature engineering available:
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, window=t)
# The following raw price values are necessary for RL models
informative[f"%-{pair}raw_close"] = informative["close"]
informative[f"%-{pair}raw_open"] = informative["open"]
informative[f"%-{pair}raw_high"] = informative["high"]
informative[f"%-{pair}raw_low"] = informative["low"]
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
# Add generalized indicators here (because in live, it will call this
# function to populate indicators during training). Notice how we ensure not to
# add them multiple times
if set_generalized_indicators:
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param df: strategy dataframe which will receive the targets
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
"""
# For RL, there are no direct targets to set. This is filler (neutral)
# until the agent sends an action.
df["&-action"] = 0
return df
dataframe["&-action"] = 0
```
Most of the function remains the same as for typical Regressors, however, the function above shows how the strategy must pass the raw price data to the agent so that it has access to raw OHLCV in the training environment:
```python
def feature_engineering_standard(self, dataframe, **kwargs):
# The following features are necessary for RL models
informative[f"%-{pair}raw_close"] = informative["close"]
informative[f"%-{pair}raw_open"] = informative["open"]
informative[f"%-{pair}raw_high"] = informative["high"]
informative[f"%-{pair}raw_low"] = informative["low"]
dataframe[f"%-raw_close"] = dataframe["close"]
dataframe[f"%-raw_open"] = dataframe["open"]
dataframe[f"%-raw_high"] = dataframe["high"]
dataframe[f"%-raw_low"] = dataframe["low"]
```
Finally, there is no explicit "label" to make - instead it is necessary to assign the `&-action` column which will contain the agent's actions when accessed in `populate_entry/exit_trends()`. In the present example, the neutral action to 0. This value should align with the environment used. FreqAI provides two environments, both use 0 as the neutral action.
@ -204,10 +175,20 @@ As you begin to modify the strategy and the prediction model, you will quickly r
pnl = self.get_unrealized_profit()
factor = 100
# you can use feature values from dataframe
rsi_now = self.raw_features[f"%-rsi-period-10_shift-1_{self.pair}_"
f"{self.config['timeframe']}"].iloc[self._current_tick]
# reward agent for entering trades
if action in (Actions.Long_enter.value, Actions.Short_enter.value) \
and self._position == Positions.Neutral:
return 25
if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
and self._position == Positions.Neutral):
if rsi_now < 40:
factor = 40 / rsi_now
else:
factor = 1
return 25 * factor
# discourage agent from not entering trades
if action == Actions.Neutral.value and self._position == Positions.Neutral:
return -1
@ -272,7 +253,6 @@ FreqAI also provides a built in episodic summary logger called `self.tensorboard
!!! Note
The `self.tensorboard_log()` function is designed for tracking incremented objects only i.e. events, actions inside the training environment. If the event of interest is a float, the float can be passed as the second argument e.g. `self.tensorboard_log("float_metric1", 0.23)` would add 0.23 to `float_metric`. In this case you can also disable incrementing using `inc=False` parameter.
### Choosing a base environment
FreqAI provides three base environments, `Base3ActionRLEnvironment`, `Base4ActionEnvironment` and `Base5ActionEnvironment`. As the names imply, the environments are customized for agents that can select from 3, 4 or 5 actions. The `Base3ActionEnvironment` is the simplest, the agent can select from hold, long, or short. This environment can also be used for long-only bots (it automatically follows the `can_short` flag from the strategy), where long is the enter condition and short is the exit condition. Meanwhile, in the `Base4ActionEnvironment`, the agent can enter long, enter short, hold neutral, or exit position. Finally, in the `Base5ActionEnvironment`, the agent has the same actions as Base4, but instead of a single exit action, it separates exit long and exit short. The main changes stemming from the environment selection include:

23
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@ -67,6 +67,10 @@ Backtesting mode requires [downloading the necessary data](#downloading-data-to-
*want* to retrain a new model with the same config file, you should simply change the `identifier`.
This way, you can return to using any model you wish by simply specifying the `identifier`.
!!! Note
Backtesting calls `set_freqai_targets()` one time for each backtest window (where the number of windows is the full backtest timerange divided by the `backtest_period_days` parameter). Doing this means that the targets simulate dry/live behavior without look ahead bias. However, the definition of the features in `feature_engineering_*()` is performed once on the entire backtest timerange. This means that you should be sure that features do look-ahead into the future.
More details about look-ahead bias can be found in [Common Mistakes](strategy-customization.md#common-mistakes-when-developing-strategies).
---
### Saving prediction data
@ -135,7 +139,7 @@ freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy FreqaiExampleSt
`hyperopt` requires you to have the data pre-downloaded in the same fashion as if you were doing [backtesting](#backtesting). In addition, you must consider some restrictions when trying to hyperopt FreqAI strategies:
- The `--analyze-per-epoch` hyperopt parameter is not compatible with FreqAI.
- It's not possible to hyperopt indicators in the `populate_any_indicators()` function. This means that you cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space).
- It's not possible to hyperopt indicators in the `feature_engineering_*()` and `set_freqai_targets()` functions. This means that you cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space).
- The backtesting instructions also apply to hyperopt.
The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. You need to focus on hyperopting parameters that are not used in your features. For example, you should not try to hyperopt rolling window lengths in the feature creation, or any part of the FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only.
@ -161,20 +165,3 @@ tensorboard --logdir user_data/models/unique-id
where `unique-id` is the `identifier` set in the `freqai` configuration file. This command must be run in a separate shell if you wish to view the output in your browser at 127.0.0.1:6060 (6060 is the default port used by Tensorboard).
![tensorboard](assets/tensorboard.jpg)
## Setting up a follower
You can indicate to the bot that it should not train models, but instead should look for models trained by a leader with a specific `identifier` by defining:
```json
"freqai": {
"enabled": true,
"follow_mode": true,
"identifier": "example",
"feature_parameters": {
// leader bots feature_parameters inserted here
},
}
```
In this example, the user has a leader bot with the `"identifier": "example"`. The leader bot is already running or is launched simultaneously with the follower. The follower will load models created by the leader and inference them to obtain predictions instead of training its own models. The user will also need to duplicate the `feature_parameters` parameters from from the leaders freqai configuration file into the freqai section of the followers config.

10
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@ -50,7 +50,7 @@ usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--eps] [--dmmp] [--enable-protections]
[--dry-run-wallet DRY_RUN_WALLET]
[--timeframe-detail TIMEFRAME_DETAIL] [-e INT]
[--spaces {all,buy,sell,roi,stoploss,trailing,protection,default} [{all,buy,sell,roi,stoploss,trailing,protection,default} ...]]
[--spaces {all,buy,sell,roi,stoploss,trailing,protection,trades,default} [{all,buy,sell,roi,stoploss,trailing,protection,trades,default} ...]]
[--print-all] [--no-color] [--print-json] [-j JOBS]
[--random-state INT] [--min-trades INT]
[--hyperopt-loss NAME] [--disable-param-export]
@ -96,7 +96,7 @@ optional arguments:
Specify detail timeframe for backtesting (`1m`, `5m`,
`30m`, `1h`, `1d`).
-e INT, --epochs INT Specify number of epochs (default: 100).
--spaces {all,buy,sell,roi,stoploss,trailing,protection,default} [{all,buy,sell,roi,stoploss,trailing,protection,default} ...]
--spaces {all,buy,sell,roi,stoploss,trailing,protection,trades,default} [{all,buy,sell,roi,stoploss,trailing,protection,trades,default} ...]
Specify which parameters to hyperopt. Space-separated
list.
--print-all Print all results, not only the best ones.
@ -180,6 +180,7 @@ Rarely you may also need to create a [nested class](advanced-hyperopt.md#overrid
* `generate_roi_table` - for custom ROI optimization (if you need the ranges for the values in the ROI table that differ from default or the number of entries (steps) in the ROI table which differs from the default 4 steps)
* `stoploss_space` - for custom stoploss optimization (if you need the range for the stoploss parameter in the optimization hyperspace that differs from default)
* `trailing_space` - for custom trailing stop optimization (if you need the ranges for the trailing stop parameters in the optimization hyperspace that differ from default)
* `max_open_trades_space` - for custom max_open_trades optimization (if you need the ranges for the max_open_trades parameter in the optimization hyperspace that differ from default)
!!! Tip "Quickly optimize ROI, stoploss and trailing stoploss"
You can quickly optimize the spaces `roi`, `stoploss` and `trailing` without changing anything in your strategy.
@ -643,6 +644,7 @@ Legal values are:
* `roi`: just optimize the minimal profit table for your strategy
* `stoploss`: search for the best stoploss value
* `trailing`: search for the best trailing stop values
* `trades`: search for the best max open trades values
* `protection`: search for the best protection parameters (read the [protections section](#optimizing-protections) on how to properly define these)
* `default`: `all` except `trailing` and `protection`
* space-separated list of any of the above values for example `--spaces roi stoploss`
@ -916,5 +918,5 @@ Once the optimized strategy has been implemented into your strategy, you should
To achieve same the results (number of trades, their durations, profit, etc.) as during Hyperopt, please use the same configuration and parameters (timerange, timeframe, ...) used for hyperopt `--dmmp`/`--disable-max-market-positions` and `--eps`/`--enable-position-stacking` for Backtesting.
Should results not match, please double-check to make sure you transferred all conditions correctly.
Pay special care to the stoploss (and trailing stoploss) parameters, as these are often set in configuration files, which override changes to the strategy.
You should also carefully review the log of your backtest to ensure that there were no parameters inadvertently set by the configuration (like `stoploss` or `trailing_stop`).
Pay special care to the stoploss, max_open_trades and trailing stoploss parameters, as these are often set in configuration files, which override changes to the strategy.
You should also carefully review the log of your backtest to ensure that there were no parameters inadvertently set by the configuration (like `stoploss`, `max_open_trades` or `trailing_stop`).

1
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@ -52,6 +52,7 @@ Please read the [exchange specific notes](exchanges.md) to learn about eventual,
- [X] [Binance](https://www.binance.com/)
- [X] [Gate.io](https://www.gate.io/ref/6266643)
- [X] [OKX](https://okx.com/)
- [X] [Bybit](https://bybit.com/)
Please make sure to read the [exchange specific notes](exchanges.md), as well as the [trading with leverage](leverage.md) documentation before diving in.

6
docs/installation.md
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@ -30,6 +30,12 @@ The easiest way to install and run Freqtrade is to clone the bot Github reposito
!!! Warning "Up-to-date clock"
The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
!!! Error "Running setup.py install for gym did not run successfully."
If you get an error related with gym we suggest you to downgrade setuptools it to version 65.5.0 you can do it with the following command:
```bash
pip install setuptools==65.5.0
```
------
## Requirements

2
docs/leverage.md
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@ -67,8 +67,6 @@ You will also have to pick a "margin mode" (explanation below) - with freqtrade
Freqtrade follows the [ccxt naming conventions for futures](https://docs.ccxt.com/en/latest/manual.html?#perpetual-swap-perpetual-future).
A futures pair will therefore have the naming of `base/quote:settle` (e.g. `ETH/USDT:USDT`).
Binance is currently still an exception to this naming scheme, where pairs are named `ETH/USDT` also for futures markets, but will be aligned as soon as CCXT is ready.
### Margin mode
On top of `trading_mode` - you will also have to configure your `margin_mode`.

4
docs/requirements-docs.txt
View File

@ -1,6 +1,6 @@
markdown==3.3.7
mkdocs==1.4.2
mkdocs-material==8.5.11
mkdocs-material==9.0.11
mdx_truly_sane_lists==1.3
pymdown-extensions==9.9
pymdown-extensions==9.9.2
jinja2==3.1.2

9
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@ -163,7 +163,7 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
| `strategy <strategy>` | Get specific Strategy content. **Alpha**
| `available_pairs` | List available backtest data. **Alpha**
| `version` | Show version.
| `sysinfo` | Show informations about the system load.
| `sysinfo` | Show information about the system load.
| `health` | Show bot health (last bot loop).
!!! Warning "Alpha status"
@ -192,6 +192,11 @@ blacklist
:param add: List of coins to add (example: "BNB/BTC")
cancel_open_order
Cancel open order for trade.
:param trade_id: Cancels open orders for this trade.
count
Return the amount of open trades.
@ -274,7 +279,6 @@ reload_config
Reload configuration.
show_config
Returns part of the configuration, relevant for trading operations.
start
@ -320,6 +324,7 @@ version
whitelist
Show the current whitelist.
```
### Message WebSocket

14
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@ -24,7 +24,7 @@ These modes can be configured with these values:
```
!!! Note
Stoploss on exchange is only supported for Binance (stop-loss-limit), Huobi (stop-limit), Kraken (stop-loss-market, stop-loss-limit), Gateio (stop-limit), and Kucoin (stop-limit and stop-market) as of now.
Stoploss on exchange is only supported for Binance (stop-loss-limit), Huobi (stop-limit), Kraken (stop-loss-market, stop-loss-limit), Gate (stop-limit), and Kucoin (stop-limit and stop-market) as of now.
<ins>Do not set too low/tight stoploss value if using stop loss on exchange!</ins>
If set to low/tight then you have greater risk of missing fill on the order and stoploss will not work.
@ -52,6 +52,18 @@ The bot cannot do these every 5 seconds (at each iteration), otherwise it would
So this parameter will tell the bot how often it should update the stoploss order. The default value is 60 (1 minute).
This same logic will reapply a stoploss order on the exchange should you cancel it accidentally.
### stoploss_price_type
!!! Warning "Only applies to futures"
`stoploss_price_type` only applies to futures markets (on exchanges where it's available).
Freqtrade will perform a validation of this setting on startup, failing to start if an invalid setting for your exchange has been selected.
Supported price types are gonna differs between each exchanges. Please check with your exchange on which price types it supports.
Stoploss on exchange on futures markets can trigger on different price types.
The naming for these prices in exchange terminology often varies, but is usually something around "last" (or "contract price" ), "mark" and "index".
Acceptable values for this setting are `"last"`, `"mark"` and `"index"` - which freqtrade will transfer automatically to the corresponding API type, and place the [stoploss on exchange](#stoploss_on_exchange-and-stoploss_on_exchange_limit_ratio) order correspondingly.
### force_exit
`force_exit` is an optional value, which defaults to the same value as `exit` and is used when sending a `/forceexit` command from Telegram or from the Rest API.

2
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@ -80,7 +80,7 @@ class AwesomeStrategy(IStrategy):
## Enter Tag
When your strategy has multiple buy signals, you can name the signal that triggered.
Then you can access you buy signal on `custom_exit`
Then you can access your buy signal on `custom_exit`
```python
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

3
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@ -659,6 +659,7 @@ Position adjustments will always be applied in the direction of the trade, so a
!!! Warning "Backtesting"
During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so run-time performance will be affected.
This can also cause deviating results between live and backtesting, since backtesting can adjust the trade only once per candle, whereas live could adjust the trade multiple times per candle.
``` python
from freqtrade.persistence import Trade
@ -827,7 +828,7 @@ class AwesomeStrategy(IStrategy):
"""
# Limit orders to use and follow SMA200 as price target for the first 10 minutes since entry trigger for BTC/USDT pair.
if pair == 'BTC/USDT' and entry_tag == 'long_sma200' and side == 'long' and (current_time - timedelta(minutes=10) > trade.open_date_utc:
if pair == 'BTC/USDT' and entry_tag == 'long_sma200' and side == 'long' and (current_time - timedelta(minutes=10)) > trade.open_date_utc:
# just cancel the order if it has been filled more than half of the amount
if order.filled > order.remaining:
return None

1
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@ -80,6 +80,7 @@ from freqtrade.resolvers import StrategyResolver
from freqtrade.data.dataprovider import DataProvider
strategy = StrategyResolver.load_strategy(config)
strategy.dp = DataProvider(config, None, None)
strategy.ft_bot_start()
# Generate buy/sell signals using strategy
df = strategy.analyze_ticker(candles, {'pair': pair})

251
docs/strategy_migration.md
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@ -477,3 +477,254 @@ after:
"ignore_buying_expired_candle_after": 120
}
```
## FreqAI strategy
The `populate_any_indicators()` method has been split into `feature_engineering_expand_all()`, `feature_engineering_expand_basic()`, `feature_engineering_standard()` and`set_freqai_targets()`.
For each new function, the pair (and timeframe where necessary) will be automatically added to the column.
As such, the definition of features becomes much simpler with the new logic.
For a full explanation of each method, please go to the corresponding [freqAI documentation page](freqai-feature-engineering.md#defining-the-features)
``` python linenums="1" hl_lines="12-37 39-42 63-65 67-75"
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(informative), window=t, stds=2.2
)
informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
informative[f"%-{pair}bb_width-period_{t}"] = (
informative[f"{pair}bb_upperband-period_{t}"]
- informative[f"{pair}bb_lowerband-period_{t}"]
) / informative[f"{pair}bb_middleband-period_{t}"]
informative[f"%-{pair}close-bb_lower-period_{t}"] = (
informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
)
informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
informative[f"%-{pair}relative_volume-period_{t}"] = (
informative["volume"] / informative["volume"].rolling(t).mean()
) # (1)
informative[f"%-{pair}pct-change"] = informative["close"].pct_change()
informative[f"%-{pair}raw_volume"] = informative["volume"]
informative[f"%-{pair}raw_price"] = informative["close"]
# (2)
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
# Add generalized indicators here (because in live, it will call this
# function to populate indicators during training). Notice how we ensure not to
# add them multiple times
if set_generalized_indicators:
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
# (3)
# user adds targets here by prepending them with &- (see convention below)
df["&-s_close"] = (
df["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ df["close"]
- 1
) # (4)
return df
```
1. Features - Move to `feature_engineering_expand_all`
2. Basic features, not expanded across `include_periods_candles` - move to`feature_engineering_expand_basic()`.
3. Standard features which should not be expanded - move to `feature_engineering_standard()`.
4. Targets - Move this part to `set_freqai_targets()`.
### freqai - feature engineering expand all
Features will now expand automatically. As such, the expansion loops, as well as the `{pair}` / `{timeframe}` parts will need to be removed.
``` python linenums="1"
def feature_engineering_expand_all(self, dataframe, period, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
`include_corr_pairs`. In other words, a single feature defined in this function
will automatically expand to a total of
`indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
`include_corr_pairs` numbers of features added to the model.
All features must be prepended with `%` to be recognized by FreqAI internals.
More details on how these config defined parameters accelerate feature engineering
in the documentation at:
https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param df: strategy dataframe which will receive the features
:param period: period of the indicator - usage example:
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
"""
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe), window=period, stds=2.2
)
dataframe["bb_lowerband-period"] = bollinger["lower"]
dataframe["bb_middleband-period"] = bollinger["mid"]
dataframe["bb_upperband-period"] = bollinger["upper"]
dataframe["%-bb_width-period"] = (
dataframe["bb_upperband-period"]
- dataframe["bb_lowerband-period"]
) / dataframe["bb_middleband-period"]
dataframe["%-close-bb_lower-period"] = (
dataframe["close"] / dataframe["bb_lowerband-period"]
)
dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
dataframe["%-relative_volume-period"] = (
dataframe["volume"] / dataframe["volume"].rolling(period).mean()
)
return dataframe
```
### Freqai - feature engineering basic
Basic features. Make sure to remove the `{pair}` part from your features.
``` python linenums="1"
def feature_engineering_expand_basic(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
In other words, a single feature defined in this function
will automatically expand to a total of
`include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
numbers of features added to the model.
Features defined here will *not* be automatically duplicated on user defined
`indicator_periods_candles`
All features must be prepended with `%` to be recognized by FreqAI internals.
More details on how these config defined parameters accelerate feature engineering
in the documentation at:
https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param df: strategy dataframe which will receive the features
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
"""
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"]
dataframe["%-raw_price"] = dataframe["close"]
return dataframe
```
### FreqAI - feature engineering standard
``` python linenums="1"
def feature_engineering_standard(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This optional function will be called once with the dataframe of the base timeframe.
This is the final function to be called, which means that the dataframe entering this
function will contain all the features and columns created by all other
freqai_feature_engineering_* functions.
This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
This function is a good place for any feature that should not be auto-expanded upon
(e.g. day of the week).
All features must be prepended with `%` to be recognized by FreqAI internals.
More details about feature engineering available:
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param df: strategy dataframe which will receive the features
usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
"""
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
return dataframe
```
### FreqAI - set Targets
Targets now get their own, dedicated method.
``` python linenums="1"
def set_freqai_targets(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
Required function to set the targets for the model.
All targets must be prepended with `&` to be recognized by the FreqAI internals.
More details about feature engineering available:
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param df: strategy dataframe which will receive the targets
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
"""
dataframe["&-s_close"] = (
dataframe["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ dataframe["close"]
- 1
)
return dataframe
```

14
docs/telegram-usage.md
View File

@ -162,26 +162,33 @@ official commands. You can ask at any moment for help with `/help`.
| Command | Description |
|----------|-------------|
| **System commands**
| `/start` | Starts the trader
| `/stop` | Stops the trader
| `/stopbuy | /stopentry` | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
| `/reload_config` | Reloads the configuration file
| `/show_config` | Shows part of the current configuration with relevant settings to operation
| `/logs [limit]` | Show last log messages.
| `/help` | Show help message
| `/version` | Show version
| **Status** |
| `/status` | Lists all open trades
| `/status <trade_id>` | Lists one or more specific trade. Separate multiple <trade_id> with a blank space.
| `/status table` | List all open trades in a table format. Pending buy orders are marked with an asterisk (*) Pending sell orders are marked with a double asterisk (**)
| `/trades [limit]` | List all recently closed trades in a table format.
| `/delete <trade_id>` | Delete a specific trade from the Database. Tries to close open orders. Requires manual handling of this trade on the exchange.
| `/count` | Displays number of trades used and available
| `/locks` | Show currently locked pairs.
| `/unlock <pair or lock_id>` | Remove the lock for this pair (or for this lock id).
| `/profit [<n>]` | Display a summary of your profit/loss from close trades and some stats about your performance, over the last n days (all trades by default)
| **Modify Trade states** |
| `/forceexit <trade_id> | /fx <tradeid>` | Instantly exits the given trade (Ignoring `minimum_roi`).
| `/forceexit all | /fx all` | Instantly exits all open trades (Ignoring `minimum_roi`).
| `/fx` | alias for `/forceexit`
| `/forcelong <pair> [rate]` | Instantly buys the given pair. Rate is optional and only applies to limit orders. (`force_entry_enable` must be set to True)
| `/forceshort <pair> [rate]` | Instantly shorts the given pair. Rate is optional and only applies to limit orders. This will only work on non-spot markets. (`force_entry_enable` must be set to True)
| `/delete <trade_id>` | Delete a specific trade from the Database. Tries to close open orders. Requires manual handling of this trade on the exchange.
| `/cancel_open_order <trade_id> | /coo <trade_id>` | Cancel an open order for a trade.
| **Metrics** |
| `/profit [<n>]` | Display a summary of your profit/loss from close trades and some stats about your performance, over the last n days (all trades by default)
| `/performance` | Show performance of each finished trade grouped by pair
| `/balance` | Show account balance per currency
| `/daily <n>` | Shows profit or loss per day, over the last n days (n defaults to 7)
@ -193,8 +200,7 @@ official commands. You can ask at any moment for help with `/help`.
| `/whitelist [sorted] [baseonly]` | Show the current whitelist. Optionally display in alphabetical order and/or with just the base currency of each pairing.
| `/blacklist [pair]` | Show the current blacklist, or adds a pair to the blacklist.
| `/edge` | Show validated pairs by Edge if it is enabled.
| `/help` | Show help message
| `/version` | Show version
## Telegram commands in action

2
environment.yml
View File

@ -12,7 +12,7 @@ dependencies:
- py-find-1st
- aiohttp
- SQLAlchemy
- python-telegram-bot
- python-telegram-bot<20.0.0
- arrow
- cachetools
- requests

7
freqtrade/__init__.py
View File

@ -1,19 +1,20 @@
""" Freqtrade bot """
__version__ = '2023.1.dev'
__version__ = '2023.2.dev'
if 'dev' in __version__:
from pathlib import Path
try:
import subprocess
freqtrade_basedir = Path(__file__).parent
__version__ = __version__ + '-' + subprocess.check_output(
['git', 'log', '--format="%h"', '-n 1'],
stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
stderr=subprocess.DEVNULL, cwd=freqtrade_basedir).decode("utf-8").rstrip().strip('"')
except Exception: # pragma: no cover
# git not available, ignore
try:
# Try Fallback to freqtrade_commit file (created by CI while building docker image)
from pathlib import Path
versionfile = Path('./freqtrade_commit')
if versionfile.is_file():
__version__ = f"docker-{__version__}-{versionfile.read_text()[:8]}"

4
freqtrade/commands/build_config_commands.py
View File

@ -108,7 +108,7 @@ def ask_user_config() -> Dict[str, Any]:
"binance",
"binanceus",
"bittrex",
"gateio",
"gate",
"huobi",
"kraken",
"kucoin",
@ -123,7 +123,7 @@ def ask_user_config() -> Dict[str, Any]:
"message": "Do you want to trade Perpetual Swaps (perpetual futures)?",
"default": False,
"filter": lambda val: 'futures' if val else 'spot',
"when": lambda x: x["exchange_name"] in ['binance', 'gateio', 'okx'],
"when": lambda x: x["exchange_name"] in ['binance', 'gate', 'okx'],
},
{
"type": "autocomplete",

8
freqtrade/commands/cli_options.py
View File

@ -251,7 +251,8 @@ AVAILABLE_CLI_OPTIONS = {
"spaces": Arg(
'--spaces',
help='Specify which parameters to hyperopt. Space-separated list.',
choices=['all', 'buy', 'sell', 'roi', 'stoploss', 'trailing', 'protection', 'default'],
choices=['all', 'buy', 'sell', 'roi', 'stoploss',
'trailing', 'protection', 'trades', 'default'],
nargs='+',
default='default',
),
@ -632,10 +633,11 @@ AVAILABLE_CLI_OPTIONS = {
"1: by enter_tag, "
"2: by enter_tag and exit_tag, "
"3: by pair and enter_tag, "
"4: by pair, enter_ and exit_tag (this can get quite large)"),
"4: by pair, enter_ and exit_tag (this can get quite large), "
"5: by exit_tag"),
nargs='+',
default=['0', '1', '2'],
choices=['0', '1', '2', '3', '4'],
choices=['0', '1', '2', '3', '4', '5'],
),
"enter_reason_list": Arg(
"--enter-reason-list",

3
freqtrade/commands/data_commands.py
View File

@ -14,6 +14,7 @@ from freqtrade.exceptions import OperationalException
from freqtrade.exchange import market_is_active, timeframe_to_minutes
from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist, expand_pairlist
from freqtrade.resolvers import ExchangeResolver
from freqtrade.util.binance_mig import migrate_binance_futures_data
logger = logging.getLogger(__name__)
@ -86,6 +87,7 @@ def start_download_data(args: Dict[str, Any]) -> None:
"Please use `--dl-trades` instead for this exchange "
"(will unfortunately take a long time)."
)
migrate_binance_futures_data(config)
pairs_not_available = refresh_backtest_ohlcv_data(
exchange, pairs=expanded_pairs, timeframes=config['timeframes'],
datadir=config['datadir'], timerange=timerange,
@ -145,6 +147,7 @@ def start_convert_data(args: Dict[str, Any], ohlcv: bool = True) -> None:
"""
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
if ohlcv:
migrate_binance_futures_data(config)
candle_types = [CandleType.from_string(ct) for ct in config.get('candle_types', ['spot'])]
for candle_type in candle_types:
convert_ohlcv_format(config,

2
freqtrade/configuration/configuration.py
View File

@ -28,7 +28,7 @@ class Configuration:
Reuse this class for the bot, backtesting, hyperopt and every script that required configuration
"""
def __init__(self, args: Dict[str, Any], runmode: RunMode = None) -> None:
def __init__(self, args: Dict[str, Any], runmode: Optional[RunMode] = None) -> None:
self.args = args
self.config: Optional[Config] = None
self.runmode = runmode

2
freqtrade/configuration/environment_vars.py
View File

@ -32,7 +32,7 @@ def flat_vars_to_nested_dict(env_dict: Dict[str, Any], prefix: str) -> Dict[str,
:param prefix: Prefix to consider (usually FREQTRADE__)
:return: Nested dict based on available and relevant variables.
"""
no_convert = ['CHAT_ID']
no_convert = ['CHAT_ID', 'PASSWORD']
relevant_vars: Dict[str, Any] = {}
for env_var, val in sorted(env_dict.items()):

5
freqtrade/configuration/load_config.py
View File

@ -6,7 +6,7 @@ import re
import sys
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, List
from typing import Any, Dict, List, Optional
import rapidjson
@ -75,7 +75,8 @@ def load_config_file(path: str) -> Dict[str, Any]:
return config
def load_from_files(files: List[str], base_path: Path = None, level: int = 0) -> Dict[str, Any]:
def load_from_files(
files: List[str], base_path: Optional[Path] = None, level: int = 0) -> Dict[str, Any]:
"""
Recursively load configuration files if specified.
Sub-files are assumed to be relative to the initial config.

7
freqtrade/constants.py
View File

@ -5,7 +5,7 @@ bot constants
"""
from typing import Any, Dict, List, Literal, Tuple
from freqtrade.enums import CandleType, RPCMessageType
from freqtrade.enums import CandleType, PriceType, RPCMessageType
DEFAULT_CONFIG = 'config.json'
@ -25,6 +25,7 @@ PRICING_SIDES = ['ask', 'bid', 'same', 'other']
ORDERTYPE_POSSIBILITIES = ['limit', 'market']
_ORDERTIF_POSSIBILITIES = ['GTC', 'FOK', 'IOC', 'PO']
ORDERTIF_POSSIBILITIES = _ORDERTIF_POSSIBILITIES + [t.lower() for t in _ORDERTIF_POSSIBILITIES]
STOPLOSS_PRICE_TYPES = [p for p in PriceType]
HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
'SharpeHyperOptLoss', 'SharpeHyperOptLossDaily',
'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily',
@ -229,6 +230,7 @@ CONF_SCHEMA = {
'default': 'market'},
'stoploss': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'stoploss_on_exchange': {'type': 'boolean'},
'stoploss_price_type': {'type': 'string', 'enum': STOPLOSS_PRICE_TYPES},
'stoploss_on_exchange_interval': {'type': 'number'},
'stoploss_on_exchange_limit_ratio': {'type': 'number', 'minimum': 0.0,
'maximum': 1.0}
@ -636,7 +638,6 @@ SCHEMA_TRADE_REQUIRED = [
SCHEMA_BACKTEST_REQUIRED = [
'exchange',
'max_open_trades',
'stake_currency',
'stake_amount',
'dry_run_wallet',
@ -646,6 +647,7 @@ SCHEMA_BACKTEST_REQUIRED = [
SCHEMA_BACKTEST_REQUIRED_FINAL = SCHEMA_BACKTEST_REQUIRED + [
'stoploss',
'minimal_roi',
'max_open_trades'
]
SCHEMA_MINIMAL_REQUIRED = [
@ -681,3 +683,4 @@ MakerTaker = Literal['maker', 'taker']
BidAsk = Literal['bid', 'ask']
Config = Dict[str, Any]
IntOrInf = float

9
freqtrade/data/btanalysis.py
View File

@ -10,7 +10,7 @@ from typing import Any, Dict, List, Optional, Union
import numpy as np
import pandas as pd
from freqtrade.constants import LAST_BT_RESULT_FN
from freqtrade.constants import LAST_BT_RESULT_FN, IntOrInf
from freqtrade.exceptions import OperationalException
from freqtrade.misc import json_load
from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
@ -90,7 +90,8 @@ def get_latest_hyperopt_filename(directory: Union[Path, str]) -> str:
return 'hyperopt_results.pickle'
def get_latest_hyperopt_file(directory: Union[Path, str], predef_filename: str = None) -> Path:
def get_latest_hyperopt_file(
directory: Union[Path, str], predef_filename: Optional[str] = None) -> Path:
"""
Get latest hyperopt export based on '.last_result.json'.
:param directory: Directory to search for last result
@ -193,7 +194,7 @@ def get_backtest_resultlist(dirname: Path):
def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, str],
min_backtest_date: datetime = None) -> Dict[str, Any]:
min_backtest_date: Optional[datetime] = None) -> Dict[str, Any]:
"""
Find existing backtest stats that match specified run IDs and load them.
:param dirname: pathlib.Path object, or string pointing to the file.
@ -332,7 +333,7 @@ def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataF
def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
max_open_trades: int) -> pd.DataFrame:
max_open_trades: IntOrInf) -> pd.DataFrame:
"""
Find overlapping trades by expanding each trade once per period it was open
and then counting overlaps

20
freqtrade/data/dataprovider.py
View File

@ -9,7 +9,7 @@ from collections import deque
from datetime import datetime, timezone
from typing import Any, Dict, List, Optional, Tuple
from pandas import DataFrame, to_timedelta
from pandas import DataFrame, Timedelta, Timestamp, to_timedelta
from freqtrade.configuration import TimeRange
from freqtrade.constants import (FULL_DATAFRAME_THRESHOLD, Config, ListPairsWithTimeframes,
@ -206,9 +206,11 @@ class DataProvider:
existing_df, _ = self.__producer_pairs_df[producer_name][pair_key]
# CHECK FOR MISSING CANDLES
timeframe_delta = to_timedelta(timeframe) # Convert the timeframe to a timedelta for pandas
local_last = existing_df.iloc[-1]['date'] # We want the last date from our copy
incoming_first = dataframe.iloc[0]['date'] # We want the first date from the incoming
# Convert the timeframe to a timedelta for pandas
timeframe_delta: Timedelta = to_timedelta(timeframe)
local_last: Timestamp = existing_df.iloc[-1]['date'] # We want the last date from our copy
# We want the first date from the incoming
incoming_first: Timestamp = dataframe.iloc[0]['date']
# Remove existing candles that are newer than the incoming first candle
existing_df1 = existing_df[existing_df['date'] < incoming_first]
@ -221,7 +223,7 @@ class DataProvider:
# we missed some candles between our data and the incoming
# so return False and candle_difference.
if candle_difference > 1:
return (False, candle_difference)
return (False, int(candle_difference))
if existing_df1.empty:
appended_df = dataframe
else:
@ -281,7 +283,7 @@ class DataProvider:
def historic_ohlcv(
self,
pair: str,
timeframe: str = None,
timeframe: Optional[str] = None,
candle_type: str = ''
) -> DataFrame:
"""
@ -333,7 +335,7 @@ class DataProvider:
def get_pair_dataframe(
self,
pair: str,
timeframe: str = None,
timeframe: Optional[str] = None,
candle_type: str = ''
) -> DataFrame:
"""
@ -415,7 +417,7 @@ class DataProvider:
def refresh(self,
pairlist: ListPairsWithTimeframes,
helping_pairs: ListPairsWithTimeframes = None) -> None:
helping_pairs: Optional[ListPairsWithTimeframes] = None) -> None:
"""
Refresh data, called with each cycle
"""
@ -439,7 +441,7 @@ class DataProvider:
def ohlcv(
self,
pair: str,
timeframe: str = None,
timeframe: Optional[str] = None,
copy: bool = True,
candle_type: str = ''
) -> DataFrame:

6
freqtrade/data/entryexitanalysis.py
View File

@ -141,6 +141,12 @@ def _do_group_table_output(bigdf, glist):
# 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
if g == "4":
group_mask = ['pair', 'enter_reason', 'exit_reason']
# 5: profit summaries grouped by exit_tag
if g == "5":
group_mask = ['exit_reason']
sortcols = ['exit_reason']
if group_mask:
new = bigdf.groupby(group_mask).agg(agg_mask).reset_index()
new.columns = group_mask + agg_cols

12
freqtrade/data/history/history_utils.py
View File

@ -28,8 +28,8 @@ def load_pair_history(pair: str,
fill_up_missing: bool = True,
drop_incomplete: bool = False,
startup_candles: int = 0,
data_format: str = None,
data_handler: IDataHandler = None,
data_format: Optional[str] = None,
data_handler: Optional[IDataHandler] = None,
candle_type: CandleType = CandleType.SPOT
) -> DataFrame:
"""
@ -69,7 +69,7 @@ def load_data(datadir: Path,
fail_without_data: bool = False,
data_format: str = 'json',
candle_type: CandleType = CandleType.SPOT,
user_futures_funding_rate: int = None,
user_futures_funding_rate: Optional[int] = None,
) -> Dict[str, DataFrame]:
"""
Load ohlcv history data for a list of pairs.
@ -116,7 +116,7 @@ def refresh_data(*, datadir: Path,
timeframe: str,
pairs: List[str],
exchange: Exchange,
data_format: str = None,
data_format: Optional[str] = None,
timerange: Optional[TimeRange] = None,
candle_type: CandleType,
) -> None:
@ -189,7 +189,7 @@ def _download_pair_history(pair: str, *,
timeframe: str = '5m',
process: str = '',
new_pairs_days: int = 30,
data_handler: IDataHandler = None,
data_handler: Optional[IDataHandler] = None,
timerange: Optional[TimeRange] = None,
candle_type: CandleType,
erase: bool = False,
@ -272,7 +272,7 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes
datadir: Path, trading_mode: str,
timerange: Optional[TimeRange] = None,
new_pairs_days: int = 30, erase: bool = False,
data_format: str = None,
data_format: Optional[str] = None,
prepend: bool = False,
) -> List[str]:
"""

23
freqtrade/data/history/idatahandler.py
View File

@ -308,7 +308,7 @@ class IDataHandler(ABC):
timerange=timerange_startup,
candle_type=candle_type
)
if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data, True):
if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data):
return pairdf
else:
enddate = pairdf.iloc[-1]['date']
@ -316,7 +316,7 @@ class IDataHandler(ABC):
if timerange_startup:
self._validate_pairdata(pair, pairdf, timeframe, candle_type, timerange_startup)
pairdf = trim_dataframe(pairdf, timerange_startup)
if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data):
if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data, True):
return pairdf
# incomplete candles should only be dropped if we didn't trim the end beforehand.
@ -374,6 +374,21 @@ class IDataHandler(ABC):
logger.warning(f"{pair}, {candle_type}, {timeframe}, "
f"data ends at {pairdata.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}")
def rename_futures_data(
self, pair: str, new_pair: str, timeframe: str, candle_type: CandleType):
"""
Temporary method to migrate data from old naming to new naming (BTC/USDT -> BTC/USDT:USDT)
Only used for binance to support the binance futures naming unification.
"""
file_old = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
file_new = self._pair_data_filename(self._datadir, new_pair, timeframe, candle_type)
# print(file_old, file_new)
if file_new.exists():
logger.warning(f"{file_new} exists already, can't migrate {pair}.")
return
file_old.rename(file_new)
def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
"""
@ -403,8 +418,8 @@ def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
raise ValueError(f"No datahandler for datatype {datatype} available.")
def get_datahandler(datadir: Path, data_format: str = None,
data_handler: IDataHandler = None) -> IDataHandler:
def get_datahandler(datadir: Path, data_format: Optional[str] = None,
data_handler: Optional[IDataHandler] = None) -> IDataHandler:
"""
:param datadir: Folder to save data
:param data_format: dataformat to use

2
freqtrade/data/metrics.py
View File

@ -197,7 +197,7 @@ def calculate_cagr(days_passed: int, starting_balance: float, final_balance: flo
def calculate_expectancy(trades: pd.DataFrame) -> float:
"""
Calculate expectancy
:param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
:param trades: DataFrame containing trades (requires columns close_date and profit_abs)
:return: expectancy
"""
if len(trades) == 0:

1
freqtrade/enums/__init__.py
View File

@ -6,6 +6,7 @@ from freqtrade.enums.exittype import ExitType
from freqtrade.enums.hyperoptstate import HyperoptState
from freqtrade.enums.marginmode import MarginMode
from freqtrade.enums.ordertypevalue import OrderTypeValues
from freqtrade.enums.pricetype import PriceType
from freqtrade.enums.rpcmessagetype import NO_ECHO_MESSAGES, RPCMessageType, RPCRequestType
from freqtrade.enums.runmode import NON_UTIL_MODES, OPTIMIZE_MODES, TRADING_MODES, RunMode
from freqtrade.enums.signaltype import SignalDirection, SignalTagType, SignalType

8
freqtrade/enums/pricetype.py Normal file
View File

@ -0,0 +1,8 @@
from enum import Enum
class PriceType(str, Enum):
"""Enum to distinguish possible trigger prices for stoplosses"""
LAST = "last"
MARK = "mark"
INDEX = "index"

2
freqtrade/exchange/__init__.py
View File

@ -17,7 +17,7 @@ from freqtrade.exchange.exchange_utils import (amount_to_contract_precision, amo
timeframe_to_next_date, timeframe_to_prev_date,
timeframe_to_seconds, validate_exchange,
validate_exchanges)
from freqtrade.exchange.gateio import Gateio
from freqtrade.exchange.gate import Gate
from freqtrade.exchange.hitbtc import Hitbtc
from freqtrade.exchange.huobi import Huobi
from freqtrade.exchange.kraken import Kraken

45
freqtrade/exchange/binance.py
View File

@ -7,11 +7,11 @@ from typing import Dict, List, Optional, Tuple
import arrow
import ccxt
from freqtrade.enums import CandleType, MarginMode, TradingMode
from freqtrade.enums import CandleType, MarginMode, PriceType, TradingMode
from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
from freqtrade.exchange import Exchange
from freqtrade.exchange.common import retrier
from freqtrade.exchange.types import Tickers
from freqtrade.exchange.types import OHLCVResponse, Tickers
from freqtrade.misc import deep_merge_dicts, json_load
@ -28,11 +28,16 @@ class Binance(Exchange):
"trades_pagination": "id",
"trades_pagination_arg": "fromId",
"l2_limit_range": [5, 10, 20, 50, 100, 500, 1000],
"ccxt_futures_name": "future"
}
_ft_has_futures: Dict = {
"stoploss_order_types": {"limit": "stop", "market": "stop_market"},
"tickers_have_price": False,
"floor_leverage": True,
"stop_price_type_field": "workingType",
"stop_price_type_value_mapping": {
PriceType.LAST: "CONTRACT_PRICE",
PriceType.MARK: "MARK_PRICE",
},
}
_supported_trading_mode_margin_pairs: List[Tuple[TradingMode, MarginMode]] = [
@ -78,33 +83,9 @@ class Binance(Exchange):
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not set leverage due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
f'Error in additional_exchange_init due to {e.__class__.__name__}. Message: {e}'
) from e
@retrier
def _set_leverage(
self,
leverage: float,
pair: Optional[str] = None,
trading_mode: Optional[TradingMode] = None
):
"""
Set's the leverage before making a trade, in order to not
have the same leverage on every trade
"""
trading_mode = trading_mode or self.trading_mode
if self._config['dry_run'] or trading_mode != TradingMode.FUTURES:
return
try:
self._api.set_leverage(symbol=pair, leverage=round(leverage))
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not set leverage due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
@ -112,7 +93,7 @@ class Binance(Exchange):
since_ms: int, candle_type: CandleType,
is_new_pair: bool = False, raise_: bool = False,
until_ms: Optional[int] = None
) -> Tuple[str, str, str, List]:
) -> OHLCVResponse:
"""
Overwrite to introduce "fast new pair" functionality by detecting the pair's listing date
Does not work for other exchanges, which don't return the earliest data when called with "0"
@ -150,6 +131,7 @@ class Binance(Exchange):
is_short: bool,
amount: float,
stake_amount: float,
leverage: float,
wallet_balance: float, # Or margin balance
mm_ex_1: float = 0.0, # (Binance) Cross only
upnl_ex_1: float = 0.0, # (Binance) Cross only
@ -159,11 +141,12 @@ class Binance(Exchange):
MARGIN: https://www.binance.com/en/support/faq/f6b010588e55413aa58b7d63ee0125ed
PERPETUAL: https://www.binance.com/en/support/faq/b3c689c1f50a44cabb3a84e663b81d93
:param exchange_name:
:param pair: Pair to calculate liquidation price for
:param open_rate: Entry price of position
:param is_short: True if the trade is a short, false otherwise
:param amount: Absolute value of position size incl. leverage (in base currency)
:param stake_amount: Stake amount - Collateral in settle currency.
:param leverage: Leverage used for this position.
:param trading_mode: SPOT, MARGIN, FUTURES, etc.
:param margin_mode: Either ISOLATED or CROSS
:param wallet_balance: Amount of margin_mode in the wallet being used to trade

12130
freqtrade/exchange/binance_leverage_tiers.json
View File

File diff suppressed because it is too large Load Diff

180
freqtrade/exchange/bybit.py
View File

@ -1,9 +1,16 @@
""" Bybit exchange subclass """
import logging
from typing import Dict, List, Tuple
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
from freqtrade.enums import MarginMode, TradingMode
import ccxt
from freqtrade.constants import BuySell
from freqtrade.enums import MarginMode, PriceType, TradingMode
from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
from freqtrade.exchange import Exchange
from freqtrade.exchange.common import retrier
from freqtrade.exchange.exchange_utils import timeframe_to_msecs
logger = logging.getLogger(__name__)
@ -21,17 +28,27 @@ class Bybit(Exchange):
_ft_has: Dict = {
"ohlcv_candle_limit": 1000,
"ccxt_futures_name": "linear",
"ohlcv_has_history": False,
}
_ft_has_futures: Dict = {
"ohlcv_candle_limit": 200,
"ohlcv_has_history": True,
"mark_ohlcv_timeframe": "4h",
"funding_fee_timeframe": "8h",
"stoploss_on_exchange": True,
"stoploss_order_types": {"limit": "limit", "market": "market"},
"stop_price_type_field": "triggerBy",
"stop_price_type_value_mapping": {
PriceType.LAST: "LastPrice",
PriceType.MARK: "MarkPrice",
PriceType.INDEX: "IndexPrice",
},
}
_supported_trading_mode_margin_pairs: List[Tuple[TradingMode, MarginMode]] = [
# TradingMode.SPOT always supported and not required in this list
# (TradingMode.FUTURES, MarginMode.CROSS),
# (TradingMode.FUTURES, MarginMode.ISOLATED)
(TradingMode.FUTURES, MarginMode.ISOLATED)
]
@property
@ -47,3 +64,158 @@ class Bybit(Exchange):
})
config.update(super()._ccxt_config)
return config
def market_is_future(self, market: Dict[str, Any]) -> bool:
main = super().market_is_future(market)
# For ByBit, we'll only support USDT markets for now.
return (
main and market['settle'] == 'USDT'
)
@retrier
def additional_exchange_init(self) -> None:
"""
Additional exchange initialization logic.
.api will be available at this point.
Must be overridden in child methods if required.
"""
try:
if self.trading_mode == TradingMode.FUTURES and not self._config['dry_run']:
position_mode = self._api.set_position_mode(False)
self._log_exchange_response('set_position_mode', position_mode)
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Error in additional_exchange_init due to {e.__class__.__name__}. Message: {e}'
) from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
async def _fetch_funding_rate_history(
self,
pair: str,
timeframe: str,
limit: int,
since_ms: Optional[int] = None,
) -> List[List]:
"""
Fetch funding rate history
Necessary workaround until https://github.com/ccxt/ccxt/issues/15990 is fixed.
"""
params = {}
if since_ms:
until = since_ms + (timeframe_to_msecs(timeframe) * self._ft_has['ohlcv_candle_limit'])
params.update({'until': until})
# Funding rate
data = await self._api_async.fetch_funding_rate_history(
pair, since=since_ms,
params=params)
# Convert funding rate to candle pattern
data = [[x['timestamp'], x['fundingRate'], 0, 0, 0, 0] for x in data]
return data
def _lev_prep(self, pair: str, leverage: float, side: BuySell):
if self.trading_mode != TradingMode.SPOT:
params = {'leverage': leverage}
self.set_margin_mode(pair, self.margin_mode, accept_fail=True, params=params)
self._set_leverage(leverage, pair, accept_fail=True)
def _get_params(
self,
side: BuySell,
ordertype: str,
leverage: float,
reduceOnly: bool,
time_in_force: str = 'GTC',
) -> Dict:
params = super()._get_params(
side=side,
ordertype=ordertype,
leverage=leverage,
reduceOnly=reduceOnly,
time_in_force=time_in_force,
)
if self.trading_mode == TradingMode.FUTURES and self.margin_mode:
params['position_idx'] = 0
return params
def dry_run_liquidation_price(
self,
pair: str,
open_rate: float, # Entry price of position
is_short: bool,
amount: float,
stake_amount: float,
leverage: float,
wallet_balance: float, # Or margin balance
mm_ex_1: float = 0.0, # (Binance) Cross only
upnl_ex_1: float = 0.0, # (Binance) Cross only
) -> Optional[float]:
"""
Important: Must be fetching data from cached values as this is used by backtesting!
PERPETUAL:
bybit:
https://www.bybithelp.com/HelpCenterKnowledge/bybitHC_Article?language=en_US&id=000001067
Long:
Liquidation Price = (
Entry Price * (1 - Initial Margin Rate + Maintenance Margin Rate)
- Extra Margin Added/ Contract)
Short:
Liquidation Price = (
Entry Price * (1 + Initial Margin Rate - Maintenance Margin Rate)
+ Extra Margin Added/ Contract)
Implementation Note: Extra margin is currently not used.
:param pair: Pair to calculate liquidation price for
:param open_rate: Entry price of position
:param is_short: True if the trade is a short, false otherwise
:param amount: Absolute value of position size incl. leverage (in base currency)
:param stake_amount: Stake amount - Collateral in settle currency.
:param leverage: Leverage used for this position.
:param trading_mode: SPOT, MARGIN, FUTURES, etc.
:param margin_mode: Either ISOLATED or CROSS
:param wallet_balance: Amount of margin_mode in the wallet being used to trade
Cross-Margin Mode: crossWalletBalance
Isolated-Margin Mode: isolatedWalletBalance
"""
market = self.markets[pair]
mm_ratio, _ = self.get_maintenance_ratio_and_amt(pair, stake_amount)
if self.trading_mode == TradingMode.FUTURES and self.margin_mode == MarginMode.ISOLATED:
if market['inverse']:
raise OperationalException(
"Freqtrade does not yet support inverse contracts")
initial_margin_rate = 1 / leverage
# See docstring - ignores extra margin!
if is_short:
return open_rate * (1 + initial_margin_rate - mm_ratio)
else:
return open_rate * (1 - initial_margin_rate + mm_ratio)
else:
raise OperationalException(
"Freqtrade only supports isolated futures for leverage trading")
def get_funding_fees(
self, pair: str, amount: float, is_short: bool, open_date: datetime) -> float:
"""
Fetch funding fees, either from the exchange (live) or calculates them
based on funding rate/mark price history
:param pair: The quote/base pair of the trade
:param is_short: trade direction
:param amount: Trade amount
:param open_date: Open date of the trade
:return: funding fee since open_date
:raises: ExchangeError if something goes wrong.
"""
# Bybit does not provide "applied" funding fees per position.
if self.trading_mode == TradingMode.FUTURES:
return self._fetch_and_calculate_funding_fees(
pair, amount, is_short, open_date)
return 0.0

4
freqtrade/exchange/common.py
View File

@ -46,13 +46,13 @@ MAP_EXCHANGE_CHILDCLASS = {
'binanceje': 'binance',
'binanceusdm': 'binance',
'okex': 'okx',
'gate': 'gateio',
'gateio': 'gate',
}
SUPPORTED_EXCHANGES = [
'binance',
'bittrex',
'gateio',
'gate',
'huobi',
'kraken',
'okx',

115
freqtrade/exchange/exchange.py
View File

@ -3,11 +3,11 @@
Cryptocurrency Exchanges support
"""
import asyncio
import http
import inspect
import logging
from copy import deepcopy
from datetime import datetime, timedelta, timezone
from math import floor
from threading import Lock
from typing import Any, Coroutine, Dict, List, Literal, Optional, Tuple, Union
@ -24,6 +24,7 @@ from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHAN
PairWithTimeframe)
from freqtrade.data.converter import clean_ohlcv_dataframe, ohlcv_to_dataframe, trades_dict_to_list
from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, TradingMode
from freqtrade.enums.pricetype import PriceType
from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
InvalidOrderException, OperationalException, PricingError,
RetryableOrderError, TemporaryError)
@ -36,7 +37,7 @@ from freqtrade.exchange.exchange_utils import (CcxtModuleType, amount_to_contrac
price_to_precision, timeframe_to_minutes,
timeframe_to_msecs, timeframe_to_next_date,
timeframe_to_prev_date, timeframe_to_seconds)
from freqtrade.exchange.types import Ticker, Tickers
from freqtrade.exchange.types import OHLCVResponse, Ticker, Tickers
from freqtrade.misc import (chunks, deep_merge_dicts, file_dump_json, file_load_json,
safe_value_fallback2)
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
@ -45,12 +46,6 @@ from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
logger = logging.getLogger(__name__)
# Workaround for adding samesite support to pre 3.8 python
# Only applies to python3.7, and only on certain exchanges (kraken)
# Replicates the fix from starlette (which is actually causing this problem)
http.cookies.Morsel._reserved["samesite"] = "SameSite" # type: ignore
class Exchange:
# Parameters to add directly to buy/sell calls (like agreeing to trading agreement)
@ -474,7 +469,7 @@ class Exchange:
try:
if self._api_async:
self.loop.run_until_complete(
self._api_async.load_markets(reload=reload))
self._api_async.load_markets(reload=reload, params={}))
except (asyncio.TimeoutError, ccxt.BaseError) as e:
logger.warning('Could not load async markets. Reason: %s', e)
@ -483,7 +478,7 @@ class Exchange:
def _load_markets(self) -> None:
""" Initialize markets both sync and async """
try:
self._markets = self._api.load_markets()
self._markets = self._api.load_markets(params={})
self._load_async_markets()
self._last_markets_refresh = arrow.utcnow().int_timestamp
if self._ft_has['needs_trading_fees']:
@ -501,7 +496,7 @@ class Exchange:
return None
logger.debug("Performing scheduled market reload..")
try:
self._markets = self._api.load_markets(reload=True)
self._markets = self._api.load_markets(reload=True, params={})
# Also reload async markets to avoid issues with newly listed pairs
self._load_async_markets(reload=True)
self._last_markets_refresh = arrow.utcnow().int_timestamp
@ -606,12 +601,27 @@ class Exchange:
if not self.exchange_has('createMarketOrder'):
raise OperationalException(
f'Exchange {self.name} does not support market orders.')
self.validate_stop_ordertypes(order_types)
def validate_stop_ordertypes(self, order_types: Dict) -> None:
"""
Validate stoploss order types
"""
if (order_types.get("stoploss_on_exchange")
and not self._ft_has.get("stoploss_on_exchange", False)):
raise OperationalException(
f'On exchange stoploss is not supported for {self.name}.'
)
if self.trading_mode == TradingMode.FUTURES:
price_mapping = self._ft_has.get('stop_price_type_value_mapping', {}).keys()
if (
order_types.get("stoploss_on_exchange", False) is True
and 'stoploss_price_type' in order_types
and order_types['stoploss_price_type'] not in price_mapping
):
raise OperationalException(
f'On exchange stoploss price type is not supported for {self.name}.'
)
def validate_pricing(self, pricing: Dict) -> None:
if pricing.get('use_order_book', False) and not self.exchange_has('fetchL2OrderBook'):
@ -682,7 +692,7 @@ class Exchange:
f"Freqtrade does not support {mm_value} {trading_mode.value} on {self.name}"
)
def get_option(self, param: str, default: Any = None) -> Any:
def get_option(self, param: str, default: Optional[Any] = None) -> Any:
"""
Get parameter value from _ft_has
"""
@ -1167,6 +1177,10 @@ class Exchange:
stop_price=stop_price_norm)
if self.trading_mode == TradingMode.FUTURES:
params['reduceOnly'] = True
if 'stoploss_price_type' in order_types and 'stop_price_type_field' in self._ft_has:
price_type = self._ft_has['stop_price_type_value_mapping'][
order_types.get('stoploss_price_type', PriceType.LAST)]
params[self._ft_has['stop_price_type_field']] = price_type
amount = self.amount_to_precision(pair, self._amount_to_contracts(pair, amount))
@ -1357,7 +1371,7 @@ class Exchange:
raise OperationalException(e) from e
@retrier
def fetch_positions(self, pair: str = None) -> List[Dict]:
def fetch_positions(self, pair: Optional[str] = None) -> List[Dict]:
"""
Fetch positions from the exchange.
If no pair is given, all positions are returned.
@ -1705,7 +1719,7 @@ class Exchange:
return self._config['fee']
# validate that markets are loaded before trying to get fee
if self._api.markets is None or len(self._api.markets) == 0:
self._api.load_markets()
self._api.load_markets(params={})
return self._api.calculate_fee(symbol=symbol, type=type, side=side, amount=amount,
price=price, takerOrMaker=taker_or_maker)['rate']
@ -1801,7 +1815,7 @@ class Exchange:
def get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int, candle_type: CandleType,
is_new_pair: bool = False,
until_ms: int = None) -> List:
until_ms: Optional[int] = None) -> List:
"""
Get candle history using asyncio and returns the list of candles.
Handles all async work for this.
@ -1813,32 +1827,18 @@ class Exchange:
:param candle_type: '', mark, index, premiumIndex, or funding_rate
:return: List with candle (OHLCV) data
"""
pair, _, _, data = self.loop.run_until_complete(
pair, _, _, data, _ = self.loop.run_until_complete(
self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
since_ms=since_ms, until_ms=until_ms,
is_new_pair=is_new_pair, candle_type=candle_type))
logger.info(f"Downloaded data for {pair} with length {len(data)}.")
return data
def get_historic_ohlcv_as_df(self, pair: str, timeframe: str,
since_ms: int, candle_type: CandleType) -> DataFrame:
"""
Minimal wrapper around get_historic_ohlcv - converting the result into a dataframe
:param pair: Pair to download
:param timeframe: Timeframe to get data for
:param since_ms: Timestamp in milliseconds to get history from
:param candle_type: Any of the enum CandleType (must match trading mode!)
:return: OHLCV DataFrame
"""
ticks = self.get_historic_ohlcv(pair, timeframe, since_ms=since_ms, candle_type=candle_type)
return ohlcv_to_dataframe(ticks, timeframe, pair=pair, fill_missing=True,
drop_incomplete=self._ohlcv_partial_candle)
async def _async_get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int, candle_type: CandleType,
is_new_pair: bool = False, raise_: bool = False,
until_ms: Optional[int] = None
) -> Tuple[str, str, str, List]:
) -> OHLCVResponse:
"""
Download historic ohlcv
:param is_new_pair: used by binance subclass to allow "fast" new pair downloading
@ -1869,15 +1869,16 @@ class Exchange:
continue
else:
# Deconstruct tuple if it's not an exception
p, _, c, new_data = res
p, _, c, new_data, _ = res
if p == pair and c == candle_type:
data.extend(new_data)
# Sort data again after extending the result - above calls return in "async order"
data = sorted(data, key=lambda x: x[0])
return pair, timeframe, candle_type, data
return pair, timeframe, candle_type, data, self._ohlcv_partial_candle
def _build_coroutine(self, pair: str, timeframe: str, candle_type: CandleType,
since_ms: Optional[int], cache: bool) -> Coroutine:
def _build_coroutine(
self, pair: str, timeframe: str, candle_type: CandleType,
since_ms: Optional[int], cache: bool) -> Coroutine[Any, Any, OHLCVResponse]:
not_all_data = cache and self.required_candle_call_count > 1
if cache and (pair, timeframe, candle_type) in self._klines:
candle_limit = self.ohlcv_candle_limit(timeframe, candle_type)
@ -1914,7 +1915,7 @@ class Exchange:
"""
Build Coroutines to execute as part of refresh_latest_ohlcv
"""
input_coroutines = []
input_coroutines: List[Coroutine[Any, Any, OHLCVResponse]] = []
cached_pairs = []
for pair, timeframe, candle_type in set(pair_list):
if (timeframe not in self.timeframes
@ -1978,7 +1979,6 @@ class Exchange:
:return: Dict of [{(pair, timeframe): Dataframe}]
"""
logger.debug("Refreshing candle (OHLCV) data for %d pairs", len(pair_list))
drop_incomplete = self._ohlcv_partial_candle if drop_incomplete is None else drop_incomplete
# Gather coroutines to run
input_coroutines, cached_pairs = self._build_ohlcv_dl_jobs(pair_list, since_ms, cache)
@ -1996,8 +1996,9 @@ class Exchange:
if isinstance(res, Exception):
logger.warning(f"Async code raised an exception: {repr(res)}")
continue
# Deconstruct tuple (has 4 elements)
pair, timeframe, c_type, ticks = res
# Deconstruct tuple (has 5 elements)
pair, timeframe, c_type, ticks, drop_hint = res
drop_incomplete = drop_hint if drop_incomplete is None else drop_incomplete
ohlcv_df = self._process_ohlcv_df(
pair, timeframe, c_type, ticks, cache, drop_incomplete)
@ -2025,7 +2026,7 @@ class Exchange:
timeframe: str,
candle_type: CandleType,
since_ms: Optional[int] = None,
) -> Tuple[str, str, str, List]:
) -> OHLCVResponse:
"""
Asynchronously get candle history data using fetch_ohlcv
:param candle_type: '', mark, index, premiumIndex, or funding_rate
@ -2065,9 +2066,9 @@ class Exchange:
data = sorted(data, key=lambda x: x[0])
except IndexError:
logger.exception("Error loading %s. Result was %s.", pair, data)
return pair, timeframe, candle_type, []
return pair, timeframe, candle_type, [], self._ohlcv_partial_candle
logger.debug("Done fetching pair %s, interval %s ...", pair, timeframe)
return pair, timeframe, candle_type, data
return pair, timeframe, candle_type, data, self._ohlcv_partial_candle
except ccxt.NotSupported as e:
raise OperationalException(
@ -2504,7 +2505,8 @@ class Exchange:
self,
leverage: float,
pair: Optional[str] = None,
trading_mode: Optional[TradingMode] = None
trading_mode: Optional[TradingMode] = None,
accept_fail: bool = False,
):
"""
Set's the leverage before making a trade, in order to not
@ -2513,12 +2515,18 @@ class Exchange:
if self._config['dry_run'] or not self.exchange_has("setLeverage"):
# Some exchanges only support one margin_mode type
return
if self._ft_has.get('floor_leverage', False) is True:
# Rounding for binance ...
leverage = floor(leverage)
try:
res = self._api.set_leverage(symbol=pair, leverage=leverage)
self._log_exchange_response('set_leverage', res)
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except ccxt.BadRequest as e:
if not accept_fail:
raise TemporaryError(
f'Could not set leverage due to {e.__class__.__name__}. Message: {e}') from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not set leverage due to {e.__class__.__name__}. Message: {e}') from e
@ -2540,7 +2548,8 @@ class Exchange:
return open_date.minute > 0 or open_date.second > 0
@retrier
def set_margin_mode(self, pair: str, margin_mode: MarginMode, params: dict = {}):
def set_margin_mode(self, pair: str, margin_mode: MarginMode, accept_fail: bool = False,
params: dict = {}):
"""
Set's the margin mode on the exchange to cross or isolated for a specific pair
:param pair: base/quote currency pair (e.g. "ADA/USDT")
@ -2554,6 +2563,10 @@ class Exchange:
self._log_exchange_response('set_margin_mode', res)
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except ccxt.BadRequest as e:
if not accept_fail:
raise TemporaryError(
f'Could not set margin mode due to {e.__class__.__name__}. Message: {e}') from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not set margin mode due to {e.__class__.__name__}. Message: {e}') from e
@ -2687,7 +2700,7 @@ class Exchange:
:param amount: Trade amount
:param open_date: Open date of the trade
:return: funding fee since open_date
:raies: ExchangeError if something goes wrong.
:raises: ExchangeError if something goes wrong.
"""
if self.trading_mode == TradingMode.FUTURES:
if self._config['dry_run']:
@ -2707,6 +2720,7 @@ class Exchange:
is_short: bool,
amount: float, # Absolute value of position size
stake_amount: float,
leverage: float,
wallet_balance: float,
mm_ex_1: float = 0.0, # (Binance) Cross only
upnl_ex_1: float = 0.0, # (Binance) Cross only
@ -2728,6 +2742,7 @@ class Exchange:
open_rate=open_rate,
is_short=is_short,
amount=amount,
leverage=leverage,
stake_amount=stake_amount,
wallet_balance=wallet_balance,
mm_ex_1=mm_ex_1,
@ -2739,7 +2754,7 @@ class Exchange:
pos = positions[0]
isolated_liq = pos['liquidationPrice']
if isolated_liq:
if isolated_liq is not None:
buffer_amount = abs(open_rate - isolated_liq) * self.liquidation_buffer
isolated_liq = (
isolated_liq - buffer_amount
@ -2757,6 +2772,7 @@ class Exchange:
is_short: bool,
amount: float,
stake_amount: float,
leverage: float,
wallet_balance: float, # Or margin balance
mm_ex_1: float = 0.0, # (Binance) Cross only
upnl_ex_1: float = 0.0, # (Binance) Cross only
@ -2764,7 +2780,7 @@ class Exchange:
"""
Important: Must be fetching data from cached values as this is used by backtesting!
PERPETUAL:
gateio: https://www.gate.io/help/futures/futures/27724/liquidation-price-bankruptcy-price
gate: https://www.gate.io/help/futures/futures/27724/liquidation-price-bankruptcy-price
> Liquidation Price = (Entry Price ± Margin / Contract Multiplier / Size) /
[ 1 ± (Maintenance Margin Ratio + Taker Rate)]
Wherein, "+" or "-" depends on whether the contract goes long or short:
@ -2778,13 +2794,14 @@ class Exchange:
:param is_short: True if the trade is a short, false otherwise
:param amount: Absolute value of position size incl. leverage (in base currency)
:param stake_amount: Stake amount - Collateral in settle currency.
:param leverage: Leverage used for this position.
:param trading_mode: SPOT, MARGIN, FUTURES, etc.
:param margin_mode: Either ISOLATED or CROSS
:param wallet_balance: Amount of margin_mode in the wallet being used to trade
Cross-Margin Mode: crossWalletBalance
Isolated-Margin Mode: isolatedWalletBalance
# * Not required by Gateio or OKX
# * Not required by Gate or OKX
:param mm_ex_1:
:param upnl_ex_1:
"""

11
freqtrade/exchange/exchange_utils.py
View File

@ -15,18 +15,19 @@ from freqtrade.util import FtPrecise
CcxtModuleType = Any
def is_exchange_known_ccxt(exchange_name: str, ccxt_module: CcxtModuleType = None) -> bool:
def is_exchange_known_ccxt(
exchange_name: str, ccxt_module: Optional[CcxtModuleType] = None) -> bool:
return exchange_name in ccxt_exchanges(ccxt_module)
def ccxt_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
def ccxt_exchanges(ccxt_module: Optional[CcxtModuleType] = None) -> List[str]:
"""
Return the list of all exchanges known to ccxt
"""
return ccxt_module.exchanges if ccxt_module is not None else ccxt.exchanges
def available_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
def available_exchanges(ccxt_module: Optional[CcxtModuleType] = None) -> List[str]:
"""
Return exchanges available to the bot, i.e. non-bad exchanges in the ccxt list
"""
@ -86,7 +87,7 @@ def timeframe_to_msecs(timeframe: str) -> int:
return ccxt.Exchange.parse_timeframe(timeframe) * 1000
def timeframe_to_prev_date(timeframe: str, date: datetime = None) -> datetime:
def timeframe_to_prev_date(timeframe: str, date: Optional[datetime] = None) -> datetime:
"""
Use Timeframe and determine the candle start date for this date.
Does not round when given a candle start date.
@ -102,7 +103,7 @@ def timeframe_to_prev_date(timeframe: str, date: datetime = None) -> datetime:
return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
def timeframe_to_next_date(timeframe: str, date: datetime = None) -> datetime:
def timeframe_to_next_date(timeframe: str, date: Optional[datetime] = None) -> datetime:
"""
Use Timeframe and determine next candle.
:param timeframe: timeframe in string format (e.g. "5m")

13
freqtrade/exchange/gateio.py → freqtrade/exchange/gate.py
View File

@ -4,7 +4,7 @@ from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
from freqtrade.constants import BuySell
from freqtrade.enums import MarginMode, TradingMode
from freqtrade.enums import MarginMode, PriceType, TradingMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import Exchange
from freqtrade.misc import safe_value_fallback2
@ -13,7 +13,7 @@ from freqtrade.misc import safe_value_fallback2
logger = logging.getLogger(__name__)
class Gateio(Exchange):
class Gate(Exchange):
"""
Gate.io exchange class. Contains adjustments needed for Freqtrade to work
with this exchange.
@ -34,6 +34,12 @@ class Gateio(Exchange):
"needs_trading_fees": True,
"fee_cost_in_contracts": False, # Set explicitly to false for clarity
"order_props_in_contracts": ['amount', 'filled', 'remaining'],
"stop_price_type_field": "price_type",
"stop_price_type_value_mapping": {
PriceType.LAST: 0,
PriceType.MARK: 1,
PriceType.INDEX: 2,
},
}
_supported_trading_mode_margin_pairs: List[Tuple[TradingMode, MarginMode]] = [
@ -49,6 +55,7 @@ class Gateio(Exchange):
if any(v == 'market' for k, v in order_types.items()):
raise OperationalException(
f'Exchange {self.name} does not support market orders.')
super().validate_stop_ordertypes(order_types)
def _get_params(
self,
@ -77,7 +84,7 @@ class Gateio(Exchange):
if self.trading_mode == TradingMode.FUTURES:
# Futures usually don't contain fees in the response.
# As such, futures orders on gateio will not contain a fee, which causes
# As such, futures orders on gate will not contain a fee, which causes
# a repeated "update fee" cycle and wrong calculations.
# Therefore we patch the response with fees if it's not available.
# An alternative also contianing fees would be

3
freqtrade/exchange/kraken.py
View File

@ -158,7 +158,8 @@ class Kraken(Exchange):
self,
leverage: float,
pair: Optional[str] = None,
trading_mode: Optional[TradingMode] = None
trading_mode: Optional[TradingMode] = None,
accept_fail: bool = False,
):
"""
Kraken set's the leverage as an option in the order object, so we need to

31
freqtrade/exchange/kucoin.py
View File

@ -36,3 +36,34 @@ class Kucoin(Exchange):
'stop': 'loss'
})
return params
def create_order(
self,
*,
pair: str,
ordertype: str,
side: BuySell,
amount: float,
rate: float,
leverage: float,
reduceOnly: bool = False,
time_in_force: str = 'GTC',
) -> Dict:
res = super().create_order(
pair=pair,
ordertype=ordertype,
side=side,
amount=amount,
rate=rate,
leverage=leverage,
reduceOnly=reduceOnly,
time_in_force=time_in_force,
)
# Kucoin returns only the order-id.
# ccxt returns status = 'closed' at the moment - which is information ccxt invented.
# Since we rely on status heavily, we must set it to 'open' here.
# ref: https://github.com/ccxt/ccxt/pull/16674, (https://github.com/ccxt/ccxt/pull/16553)
res['type'] = ordertype
res['status'] = 'open'
return res

11
freqtrade/exchange/okx.py
View File

@ -5,6 +5,7 @@ import ccxt
from freqtrade.constants import BuySell
from freqtrade.enums import CandleType, MarginMode, TradingMode
from freqtrade.enums.pricetype import PriceType
from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
from freqtrade.exchange import Exchange, date_minus_candles
from freqtrade.exchange.common import retrier
@ -27,6 +28,12 @@ class Okx(Exchange):
_ft_has_futures: Dict = {
"tickers_have_quoteVolume": False,
"fee_cost_in_contracts": True,
"stop_price_type_field": "tpTriggerPxType",
"stop_price_type_value_mapping": {
PriceType.LAST: "last",
PriceType.MARK: "index",
PriceType.INDEX: "mark",
},
}
_supported_trading_mode_margin_pairs: List[Tuple[TradingMode, MarginMode]] = [
@ -118,13 +125,15 @@ class Okx(Exchange):
if self.trading_mode != TradingMode.SPOT and self.margin_mode is not None:
try:
# TODO-lev: Test me properly (check mgnMode passed)
self._api.set_leverage(
res = self._api.set_leverage(
leverage=leverage,
symbol=pair,
params={
"mgnMode": self.margin_mode.value,
"posSide": self._get_posSide(side, False),
})
self._log_exchange_response('set_leverage', res)
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:

7
freqtrade/exchange/types.py
View File

@ -1,4 +1,6 @@
from typing import Dict, Optional, TypedDict
from typing import Dict, List, Optional, Tuple, TypedDict
from freqtrade.enums import CandleType
class Ticker(TypedDict):
@ -14,3 +16,6 @@ class Ticker(TypedDict):
Tickers = Dict[str, Ticker]
# pair, timeframe, candleType, OHLCV, drop last?,
OHLCVResponse = Tuple[str, str, CandleType, List, bool]

34
freqtrade/freqai/RL/BaseEnvironment.py
View File

@ -45,7 +45,8 @@ class BaseEnvironment(gym.Env):
def __init__(self, df: DataFrame = DataFrame(), prices: DataFrame = DataFrame(),
reward_kwargs: dict = {}, window_size=10, starting_point=True,
id: str = 'baseenv-1', seed: int = 1, config: dict = {}, live: bool = False,
fee: float = 0.0015, can_short: bool = False):
fee: float = 0.0015, can_short: bool = False, pair: str = "",
df_raw: DataFrame = DataFrame()):
"""
Initializes the training/eval environment.
:param df: dataframe of features
@ -60,12 +61,14 @@ class BaseEnvironment(gym.Env):
:param fee: The fee to use for environmental interactions.
:param can_short: Whether or not the environment can short
"""
self.config = config
self.rl_config = config['freqai']['rl_config']
self.add_state_info = self.rl_config.get('add_state_info', False)
self.id = id
self.max_drawdown = 1 - self.rl_config.get('max_training_drawdown_pct', 0.8)
self.compound_trades = config['stake_amount'] == 'unlimited'
self.config: dict = config
self.rl_config: dict = config['freqai']['rl_config']
self.add_state_info: bool = self.rl_config.get('add_state_info', False)
self.id: str = id
self.max_drawdown: float = 1 - self.rl_config.get('max_training_drawdown_pct', 0.8)
self.compound_trades: bool = config['stake_amount'] == 'unlimited'
self.pair: str = pair
self.raw_features: DataFrame = df_raw
if self.config.get('fee', None) is not None:
self.fee = self.config['fee']
else:
@ -74,8 +77,8 @@ class BaseEnvironment(gym.Env):
# set here to default 5Ac, but all children envs can override this
self.actions: Type[Enum] = BaseActions
self.tensorboard_metrics: dict = {}
self.can_short = can_short
self.live = live
self.can_short: bool = can_short
self.live: bool = live
if not self.live and self.add_state_info:
self.add_state_info = False
logger.warning("add_state_info is not available in backtesting. Deactivating.")
@ -93,13 +96,12 @@ class BaseEnvironment(gym.Env):
:param reward_kwargs: extra config settings assigned by user in `rl_config`
:param starting_point: start at edge of window or not
"""
self.df = df
self.signal_features = self.df
self.prices = prices
self.window_size = window_size
self.starting_point = starting_point
self.rr = reward_kwargs["rr"]
self.profit_aim = reward_kwargs["profit_aim"]
self.signal_features: DataFrame = df
self.prices: DataFrame = prices
self.window_size: int = window_size
self.starting_point: bool = starting_point
self.rr: float = reward_kwargs["rr"]
self.profit_aim: float = reward_kwargs["profit_aim"]
# # spaces
if self.add_state_info:

60
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
View File

@ -1,3 +1,4 @@
import copy
import importlib
import logging
from abc import abstractmethod
@ -50,6 +51,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
self.eval_callback: Optional[EvalCallback] = None
self.model_type = self.freqai_info['rl_config']['model_type']
self.rl_config = self.freqai_info['rl_config']
self.df_raw: DataFrame = DataFrame()
self.continual_learning = self.freqai_info.get('continual_learning', False)
if self.model_type in SB3_MODELS:
import_str = 'stable_baselines3'
@ -107,6 +109,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
data_dictionary: Dict[str, Any] = dk.make_train_test_datasets(
features_filtered, labels_filtered)
self.df_raw = copy.deepcopy(data_dictionary["train_features"])
dk.fit_labels() # FIXME useless for now, but just satiating append methods
# normalize all data based on train_dataset only
@ -143,7 +146,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
env_info = self.pack_env_dict()
env_info = self.pack_env_dict(dk.pair)
self.train_env = self.MyRLEnv(df=train_df,
prices=prices_train,
@ -158,7 +161,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
actions = self.train_env.get_actions()
self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)
def pack_env_dict(self) -> Dict[str, Any]:
def pack_env_dict(self, pair: str) -> Dict[str, Any]:
"""
Create dictionary of environment arguments
"""
@ -166,7 +169,9 @@ class BaseReinforcementLearningModel(IFreqaiModel):
"reward_kwargs": self.reward_params,
"config": self.config,
"live": self.live,
"can_short": self.can_short}
"can_short": self.can_short,
"pair": pair,
"df_raw": self.df_raw}
if self.data_provider:
env_info["fee"] = self.data_provider._exchange \
.get_fee(symbol=self.data_provider.current_whitelist()[0]) # type: ignore
@ -280,26 +285,36 @@ class BaseReinforcementLearningModel(IFreqaiModel):
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
# %-raw_volume_gen_shift-2_ETH/USDT_1h
# price data for model training and evaluation
tf = self.config['timeframe']
ohlc_list = [f'%-{pair}raw_open_{tf}', f'%-{pair}raw_low_{tf}',
f'%-{pair}raw_high_{tf}', f'%-{pair}raw_close_{tf}']
rename_dict = {f'%-{pair}raw_open_{tf}': 'open', f'%-{pair}raw_low_{tf}': 'low',
rename_dict = {'%-raw_open': 'open', '%-raw_low': 'low',
'%-raw_high': ' high', '%-raw_close': 'close'}
rename_dict_old = {f'%-{pair}raw_open_{tf}': 'open', f'%-{pair}raw_low_{tf}': 'low',
f'%-{pair}raw_high_{tf}': ' high', f'%-{pair}raw_close_{tf}': 'close'}
prices_train = train_df.filter(ohlc_list, axis=1)
if prices_train.empty:
raise OperationalException('Reinforcement learning module didnt find the raw prices '
'assigned in populate_any_indicators. Please assign them '
'with:\n'
'informative[f"%-{pair}raw_close"] = informative["close"]\n'
'informative[f"%-{pair}raw_open"] = informative["open"]\n'
'informative[f"%-{pair}raw_high"] = informative["high"]\n'
'informative[f"%-{pair}raw_low"] = informative["low"]\n')
prices_train = train_df.filter(rename_dict.keys(), axis=1)
prices_train_old = train_df.filter(rename_dict_old.keys(), axis=1)
if prices_train.empty or not prices_train_old.empty:
if not prices_train_old.empty:
prices_train = prices_train_old
rename_dict = rename_dict_old
logger.warning('Reinforcement learning module didnt find the correct raw prices '
'assigned in feature_engineering_standard(). '
'Please assign them with:\n'
'dataframe["%-raw_close"] = dataframe["close"]\n'
'dataframe["%-raw_open"] = dataframe["open"]\n'
'dataframe["%-raw_high"] = dataframe["high"]\n'
'dataframe["%-raw_low"] = dataframe["low"]\n'
'inside `feature_engineering_standard()')
elif prices_train.empty:
raise OperationalException("No prices found, please follow log warning "
"instructions to correct the strategy.")
prices_train.rename(columns=rename_dict, inplace=True)
prices_train.reset_index(drop=True)
prices_test = test_df.filter(ohlc_list, axis=1)
prices_test = test_df.filter(rename_dict.keys(), axis=1)
prices_test.rename(columns=rename_dict, inplace=True)
prices_test.reset_index(drop=True)
@ -337,7 +352,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
sets a custom reward based on profit and trade duration.
"""
def calculate_reward(self, action: int) -> float:
def calculate_reward(self, action: int) -> float: # noqa: C901
"""
An example reward function. This is the one function that users will likely
wish to inject their own creativity into.
@ -353,10 +368,19 @@ class BaseReinforcementLearningModel(IFreqaiModel):
pnl = self.get_unrealized_profit()
factor = 100.
# you can use feature values from dataframe
rsi_now = self.raw_features[f"%-rsi-period-10_shift-1_{self.pair}_"
f"{self.config['timeframe']}"].iloc[self._current_tick]
# reward agent for entering trades
if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
and self._position == Positions.Neutral):
return 25
if rsi_now < 40:
factor = 40 / rsi_now
else:
factor = 1
return 25 * factor
# discourage agent from not entering trades
if action == Actions.Neutral.value and self._position == Positions.Neutral:
return -1

51
freqtrade/freqai/data_drawer.py
View File

@ -59,7 +59,7 @@ class FreqaiDataDrawer:
Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
"""
def __init__(self, full_path: Path, config: Config, follow_mode: bool = False):
def __init__(self, full_path: Path, config: Config):
self.config = config
self.freqai_info = config.get("freqai", {})
@ -84,9 +84,6 @@ class FreqaiDataDrawer:
self.pair_dictionary_path = Path(self.full_path / "pair_dictionary.json")
self.global_metadata_path = Path(self.full_path / "global_metadata.json")
self.metric_tracker_path = Path(self.full_path / "metric_tracker.json")
self.follow_mode = follow_mode
if follow_mode:
self.create_follower_dict()
self.load_drawer_from_disk()
self.load_historic_predictions_from_disk()
self.metric_tracker: Dict[str, Dict[str, Dict[str, list]]] = {}
@ -149,13 +146,8 @@ class FreqaiDataDrawer:
if exists:
with open(self.pair_dictionary_path, "r") as fp:
self.pair_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
elif not self.follow_mode:
logger.info("Could not find existing datadrawer, starting from scratch")
else:
logger.warning(
f"Follower could not find pair_dictionary at {self.full_path} "
"sending null values back to strategy"
)
logger.info("Could not find existing datadrawer, starting from scratch")
def load_metric_tracker_from_disk(self):
"""
@ -193,13 +185,8 @@ class FreqaiDataDrawer:
self.historic_predictions = cloudpickle.load(fp)
logger.warning('FreqAI successfully loaded the backup historical predictions file.')
elif not self.follow_mode:
logger.info("Could not find existing historic_predictions, starting from scratch")
else:
logger.warning(
f"Follower could not find historic predictions at {self.full_path} "
"sending null values back to strategy"
)
logger.info("Could not find existing historic_predictions, starting from scratch")
return exists
@ -248,23 +235,6 @@ class FreqaiDataDrawer:
rapidjson.dump(metadata, fp, default=self.np_encoder,
number_mode=rapidjson.NM_NATIVE)
def create_follower_dict(self):
"""
Create or dictionary for each follower to maintain unique persistent prediction targets
"""
whitelist_pairs = self.config.get("exchange", {}).get("pair_whitelist")
exists = self.follower_dict_path.is_file()
if exists:
logger.info("Found an existing follower dictionary")
for pair in whitelist_pairs:
self.follower_dict[pair] = {}
self.save_follower_dict_to_disk()
def np_encoder(self, object):
if isinstance(object, np.generic):
return object.item()
@ -282,27 +252,17 @@ class FreqaiDataDrawer:
"""
pair_dict = self.pair_dict.get(pair)
data_path_set = self.pair_dict.get(pair, self.empty_pair_dict).get("data_path", "")
# data_path_set = self.pair_dict.get(pair, self.empty_pair_dict).get("data_path", "")
return_null_array = False
if pair_dict:
model_filename = pair_dict["model_filename"]
trained_timestamp = pair_dict["trained_timestamp"]
elif not self.follow_mode:
else:
self.pair_dict[pair] = self.empty_pair_dict.copy()
model_filename = ""
trained_timestamp = 0
if not data_path_set and self.follow_mode:
logger.warning(
f"Follower could not find current pair {pair} in "
f"pair_dictionary at path {self.full_path}, sending null values "
"back to strategy."
)
trained_timestamp = 0
model_filename = ''
return_null_array = True
return model_filename, trained_timestamp, return_null_array
def set_pair_dict_info(self, metadata: dict) -> None:
@ -311,7 +271,6 @@ class FreqaiDataDrawer:
return
else:
self.pair_dict[metadata["pair"]] = self.empty_pair_dict.copy()
return
def set_initial_return_values(self, pair: str, pred_df: DataFrame) -> None:

193
freqtrade/freqai/data_kitchen.py
View File

@ -1,10 +1,11 @@
import copy
import inspect
import logging
import shutil
from datetime import datetime, timezone
from math import cos, sin
from pathlib import Path
from typing import Any, Dict, List, Tuple
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
import numpy.typing as npt
@ -23,6 +24,7 @@ from freqtrade.constants import Config
from freqtrade.data.converter import reduce_dataframe_footprint
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds
from freqtrade.strategy import merge_informative_pair
from freqtrade.strategy.interface import IStrategy
@ -110,7 +112,7 @@ class FreqaiDataKitchen:
def set_paths(
self,
pair: str,
trained_timestamp: int = None,
trained_timestamp: Optional[int] = None,
) -> None:
"""
Set the paths to the data for the present coin/botloop
@ -1145,9 +1147,9 @@ class FreqaiDataKitchen:
for pair in pairs:
pair = pair.replace(':', '') # lightgbm doesnt like colons
valid_strs = [f"%-{pair}", f"%{pair}", f"%_{pair}"]
pair_cols = [col for col in dataframe.columns if
any(substr in col for substr in valid_strs)]
pair_cols = [col for col in dataframe.columns if col.startswith("%")
and f"{pair}_" in col]
if pair_cols:
pair_cols.insert(0, 'date')
corr_dataframes[pair] = dataframe.filter(pair_cols, axis=1)
@ -1176,6 +1178,105 @@ class FreqaiDataKitchen:
return dataframe
def get_pair_data_for_features(self,
pair: str,
tf: str,
strategy: IStrategy,
corr_dataframes: dict = {},
base_dataframes: dict = {},
is_corr_pairs: bool = False) -> DataFrame:
"""
Get the data for the pair. If it's not in the dictionary, get it from the data provider
:param pair: str = pair to get data for
:param tf: str = timeframe to get data for
:param strategy: IStrategy = user defined strategy object
:param corr_dataframes: dict = dict containing the df pair dataframes
(for user defined timeframes)
:param base_dataframes: dict = dict containing the current pair dataframes
(for user defined timeframes)
:param is_corr_pairs: bool = whether the pair is a corr pair or not
:return: dataframe = dataframe containing the pair data
"""
if is_corr_pairs:
dataframe = corr_dataframes[pair][tf]
if not dataframe.empty:
return dataframe
else:
dataframe = strategy.dp.get_pair_dataframe(pair=pair, timeframe=tf)
return dataframe
else:
dataframe = base_dataframes[tf]
if not dataframe.empty:
return dataframe
else:
dataframe = strategy.dp.get_pair_dataframe(pair=pair, timeframe=tf)
return dataframe
def merge_features(self, df_main: DataFrame, df_to_merge: DataFrame,
tf: str, timeframe_inf: str, suffix: str) -> DataFrame:
"""
Merge the features of the dataframe and remove HLCV and date added columns
:param df_main: DataFrame = main dataframe
:param df_to_merge: DataFrame = dataframe to merge
:param tf: str = timeframe of the main dataframe
:param timeframe_inf: str = timeframe of the dataframe to merge
:param suffix: str = suffix to add to the columns of the dataframe to merge
:return: dataframe = merged dataframe
"""
dataframe = merge_informative_pair(df_main, df_to_merge, tf, timeframe_inf=timeframe_inf,
append_timeframe=False, suffix=suffix, ffill=True)
skip_columns = [
(f"{s}_{suffix}") for s in ["date", "open", "high", "low", "close", "volume"]
]
dataframe = dataframe.drop(columns=skip_columns)
return dataframe
def populate_features(self, dataframe: DataFrame, pair: str, strategy: IStrategy,
corr_dataframes: dict, base_dataframes: dict,
is_corr_pairs: bool = False) -> DataFrame:
"""
Use the user defined strategy functions for populating features
:param dataframe: DataFrame = dataframe to populate
:param pair: str = pair to populate
:param strategy: IStrategy = user defined strategy object
:param corr_dataframes: dict = dict containing the df pair dataframes
:param base_dataframes: dict = dict containing the current pair dataframes
:param is_corr_pairs: bool = whether the pair is a corr pair or not
:return: dataframe = populated dataframe
"""
tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
for tf in tfs:
metadata = {"pair": pair, "tf": tf}
informative_df = self.get_pair_data_for_features(
pair, tf, strategy, corr_dataframes, base_dataframes, is_corr_pairs)
informative_copy = informative_df.copy()
for t in self.freqai_config["feature_parameters"]["indicator_periods_candles"]:
df_features = strategy.feature_engineering_expand_all(
informative_copy.copy(), t, metadata=metadata)
suffix = f"{t}"
informative_df = self.merge_features(informative_df, df_features, tf, tf, suffix)
generic_df = strategy.feature_engineering_expand_basic(
informative_copy.copy(), metadata=metadata)
suffix = "gen"
informative_df = self.merge_features(informative_df, generic_df, tf, tf, suffix)
indicators = [col for col in informative_df if col.startswith("%")]
for n in range(self.freqai_config["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
df_shift = informative_df[indicators].shift(n)
df_shift = df_shift.add_suffix("_shift-" + str(n))
informative_df = pd.concat((informative_df, df_shift), axis=1)
dataframe = self.merge_features(dataframe.copy(), informative_df,
self.config["timeframe"], tf, f'{pair}_{tf}')
return dataframe
def use_strategy_to_populate_indicators(
self,
strategy: IStrategy,
@ -1188,7 +1289,87 @@ class FreqaiDataKitchen:
"""
Use the user defined strategy for populating indicators during retrain
:param strategy: IStrategy = user defined strategy object
:param corr_dataframes: dict = dict containing the informative pair dataframes
:param corr_dataframes: dict = dict containing the df pair dataframes
(for user defined timeframes)
:param base_dataframes: dict = dict containing the current pair dataframes
(for user defined timeframes)
:param pair: str = pair to populate
:param prediction_dataframe: DataFrame = dataframe containing the pair data
used for prediction
:param do_corr_pairs: bool = whether to populate corr pairs or not
:return:
dataframe: DataFrame = dataframe containing populated indicators
"""
# this is a hack to check if the user is using the populate_any_indicators function
new_version = inspect.getsource(strategy.populate_any_indicators) == (
inspect.getsource(IStrategy.populate_any_indicators))
if new_version:
tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
pairs: List[str] = self.freqai_config["feature_parameters"].get(
"include_corr_pairlist", [])
for tf in tfs:
if tf not in base_dataframes:
base_dataframes[tf] = pd.DataFrame()
for p in pairs:
if p not in corr_dataframes:
corr_dataframes[p] = {}
if tf not in corr_dataframes[p]:
corr_dataframes[p][tf] = pd.DataFrame()
if not prediction_dataframe.empty:
dataframe = prediction_dataframe.copy()
else:
dataframe = base_dataframes[self.config["timeframe"]].copy()
corr_pairs: List[str] = self.freqai_config["feature_parameters"].get(
"include_corr_pairlist", [])
dataframe = self.populate_features(dataframe.copy(), pair, strategy,
corr_dataframes, base_dataframes)
metadata = {"pair": pair}
dataframe = strategy.feature_engineering_standard(dataframe.copy(), metadata=metadata)
# ensure corr pairs are always last
for corr_pair in corr_pairs:
if pair == corr_pair:
continue # dont repeat anything from whitelist
if corr_pairs and do_corr_pairs:
dataframe = self.populate_features(dataframe.copy(), corr_pair, strategy,
corr_dataframes, base_dataframes, True)
dataframe = strategy.set_freqai_targets(dataframe.copy(), metadata=metadata)
self.get_unique_classes_from_labels(dataframe)
dataframe = self.remove_special_chars_from_feature_names(dataframe)
if self.config.get('reduce_df_footprint', False):
dataframe = reduce_dataframe_footprint(dataframe)
return dataframe
else:
# the user is using the populate_any_indicators functions which is deprecated
df = self.use_strategy_to_populate_indicators_old_version(
strategy, corr_dataframes, base_dataframes, pair,
prediction_dataframe, do_corr_pairs)
return df
def use_strategy_to_populate_indicators_old_version(
self,
strategy: IStrategy,
corr_dataframes: dict = {},
base_dataframes: dict = {},
pair: str = "",
prediction_dataframe: DataFrame = pd.DataFrame(),
do_corr_pairs: bool = True,
) -> DataFrame:
"""
Use the user defined strategy for populating indicators during retrain
:param strategy: IStrategy = user defined strategy object
:param corr_dataframes: dict = dict containing the df pair dataframes
(for user defined timeframes)
:param base_dataframes: dict = dict containing the current pair dataframes
(for user defined timeframes)

95
freqtrade/freqai/freqai_interface.py
View File

@ -1,3 +1,4 @@
import inspect
import logging
import threading
import time
@ -65,12 +66,11 @@ class IFreqaiModel(ABC):
self.retrain = False
self.first = True
self.set_full_path()
self.follow_mode: bool = self.freqai_info.get("follow_mode", False)
self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", True)
if self.save_backtest_models:
logger.info('Backtesting module configured to save all models.')
self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
self.dd = FreqaiDataDrawer(Path(self.full_path), self.config)
# set current candle to arbitrary historical date
self.current_candle: datetime = datetime.fromtimestamp(637887600, tz=timezone.utc)
self.dd.current_candle = self.current_candle
@ -106,6 +106,8 @@ class IFreqaiModel(ABC):
self.max_system_threads = max(int(psutil.cpu_count() * 2 - 2), 1)
self.can_short = True # overridden in start() with strategy.can_short
self.warned_deprecated_populate_any_indicators = False
record_params(config, self.full_path)
def __getstate__(self):
@ -136,6 +138,9 @@ class IFreqaiModel(ABC):
self.data_provider = strategy.dp
self.can_short = strategy.can_short
# check if the strategy has deprecated populate_any_indicators function
self.check_deprecated_populate_any_indicators(strategy)
if self.live:
self.inference_timer('start')
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
@ -147,14 +152,11 @@ class IFreqaiModel(ABC):
# (backtest window, i.e. window immediately following the training window).
# FreqAI slides the window and sequentially builds the backtesting results before returning
# the concatenated results for the full backtesting period back to the strategy.
elif not self.follow_mode:
else:
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
dataframe = self.dk.use_strategy_to_populate_indicators(
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
)
if not self.config.get("freqai_backtest_live_models", False):
logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
dk = self.start_backtesting(dataframe, metadata, self.dk)
dk = self.start_backtesting(dataframe, metadata, self.dk, strategy)
dataframe = dk.remove_features_from_df(dk.return_dataframe)
else:
logger.info(
@ -255,7 +257,7 @@ class IFreqaiModel(ABC):
self.dd.save_metric_tracker_to_disk()
def start_backtesting(
self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen, strategy: IStrategy
) -> FreqaiDataKitchen:
"""
The main broad execution for backtesting. For backtesting, each pair enters and then gets
@ -267,19 +269,22 @@ class IFreqaiModel(ABC):
:param dataframe: DataFrame = strategy passed dataframe
:param metadata: Dict = pair metadata
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
:param strategy: Strategy to train on
:return:
FreqaiDataKitchen = Data management/analysis tool associated to present pair only
"""
self.pair_it += 1
train_it = 0
pair = metadata["pair"]
populate_indicators = True
check_features = True
# Loop enforcing the sliding window training/backtesting paradigm
# tr_train is the training time range e.g. 1 historical month
# tr_backtest is the backtesting time range e.g. the week directly
# following tr_train. Both of these windows slide through the
# entire backtest
for tr_train, tr_backtest in zip(dk.training_timeranges, dk.backtesting_timeranges):
pair = metadata["pair"]
(_, _, _) = self.dd.get_pair_dict_info(pair)
train_it += 1
total_trains = len(dk.backtesting_timeranges)
@ -301,18 +306,44 @@ class IFreqaiModel(ABC):
dk.set_new_model_names(pair, timestamp_model_id)
if dk.check_if_backtest_prediction_is_valid(len_backtest_df):
if check_features:
self.dd.load_metadata(dk)
dk.find_features(dataframe)
dataframe_dummy_features = self.dk.use_strategy_to_populate_indicators(
strategy, prediction_dataframe=dataframe.tail(1), pair=metadata["pair"]
)
dk.find_features(dataframe_dummy_features)
self.check_if_feature_list_matches_strategy(dk)
check_features = False
append_df = dk.get_backtesting_prediction()
dk.append_predictions(append_df)
else:
dataframe_train = dk.slice_dataframe(tr_train, dataframe)
dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
if populate_indicators:
dataframe = self.dk.use_strategy_to_populate_indicators(
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
)
populate_indicators = False
dataframe_base_train = dataframe.loc[dataframe["date"] < tr_train.stopdt, :]
dataframe_base_train = strategy.set_freqai_targets(
dataframe_base_train, metadata=metadata)
dataframe_base_backtest = dataframe.loc[dataframe["date"] < tr_backtest.stopdt, :]
dataframe_base_backtest = strategy.set_freqai_targets(
dataframe_base_backtest, metadata=metadata)
dataframe_train = dk.slice_dataframe(tr_train, dataframe_base_train)
dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe_base_backtest)
if not self.model_exists(dk):
dk.find_features(dataframe_train)
dk.find_labels(dataframe_train)
try:
self.model = self.train(dataframe_train, pair, dk)
except Exception as msg:
logger.warning(
f"Training {pair} raised exception {msg.__class__.__name__}. "
f"Message: {msg}, skipping.")
self.dd.pair_dict[pair]["trained_timestamp"] = int(
tr_train.stopts)
if self.plot_features:
@ -350,27 +381,15 @@ class IFreqaiModel(ABC):
dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
"""
# update follower
if self.follow_mode:
self.dd.update_follower_metadata()
# get the model metadata associated with the current pair
(_, trained_timestamp, return_null_array) = self.dd.get_pair_dict_info(metadata["pair"])
# if the metadata doesn't exist, the follower returns null arrays to strategy
if self.follow_mode and return_null_array:
logger.info("Returning null array from follower to strategy")
self.dd.return_null_values_to_strategy(dataframe, dk)
return dk
# append the historic data once per round
if self.dd.historic_data:
self.dd.update_historic_data(strategy, dk)
logger.debug(f'Updating historic data on pair {metadata["pair"]}')
self.track_current_candle()
if not self.follow_mode:
(_, new_trained_timerange, data_load_timerange) = dk.check_if_new_training_required(
trained_timestamp
)
@ -384,13 +403,6 @@ class IFreqaiModel(ABC):
self.scanning = True
self.start_scanning(strategy)
elif self.follow_mode:
dk.set_paths(metadata["pair"], trained_timestamp)
logger.info(
"FreqAI instance set to follow_mode, finding existing pair "
f"using { self.identifier }"
)
# load the model and associated data into the data kitchen
self.model = self.dd.load_data(metadata["pair"], dk)
@ -913,9 +925,28 @@ class IFreqaiModel(ABC):
dk.return_dataframe = dk.return_dataframe.drop(columns=list(columns_to_drop))
dk.return_dataframe = pd.merge(
dk.return_dataframe, saved_dataframe, how='left', left_on='date', right_on="date_pred")
# dk.return_dataframe = dk.return_dataframe[saved_dataframe.columns].fillna(0)
return dk
def check_deprecated_populate_any_indicators(self, strategy: IStrategy):
"""
Check and warn if the deprecated populate_any_indicators function is used.
:param strategy: strategy object
"""
if not self.warned_deprecated_populate_any_indicators:
self.warned_deprecated_populate_any_indicators = True
old_version = inspect.getsource(strategy.populate_any_indicators) != (
inspect.getsource(IStrategy.populate_any_indicators))
if old_version:
logger.warning("DEPRECATION WARNING: "
"You are using the deprecated populate_any_indicators function. "
"This function will raise an error on March 1 2023. "
"Please update your strategy by using "
"the new feature_engineering functions. See \n"
"https://www.freqtrade.io/en/latest/freqai-feature-engineering/"
"for details.")
# Following methods which are overridden by user made prediction models.
# See freqai/prediction_models/CatboostPredictionModel.py for an example.

2
freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py
View File

@ -34,7 +34,7 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
env_info = self.pack_env_dict()
env_info = self.pack_env_dict(dk.pair)
env_id = "train_env"
self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,

68
freqtrade/freqtradebot.py
View File

@ -33,6 +33,7 @@ from freqtrade.rpc.external_message_consumer import ExternalMessageConsumer
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.util import FtPrecise
from freqtrade.util.binance_mig import migrate_binance_futures_names
from freqtrade.wallets import Wallets
@ -177,6 +178,8 @@ class FreqtradeBot(LoggingMixin):
Called on startup and after reloading the bot - triggers notifications and
performs startup tasks
"""
migrate_binance_futures_names(self.config)
self.rpc.startup_messages(self.config, self.pairlists, self.protections)
# Update older trades with precision and precision mode
self.startup_backpopulate_precision()
@ -341,7 +344,15 @@ class FreqtradeBot(LoggingMixin):
try:
fo = self.exchange.fetch_order_or_stoploss_order(order.order_id, order.ft_pair,
order.ft_order_side == 'stoploss')
if not order.trade:
# This should not happen, but it does if trades were deleted manually.
# This can only incur on sqlite, which doesn't enforce foreign constraints.
logger.warning(
f"Order {order.order_id} has no trade attached. "
"This may suggest a database corruption. "
f"The expected trade ID is {order.ft_trade_id}. Ignoring this order."
)
continue
self.update_trade_state(order.trade, order.order_id, fo,
stoploss_order=(order.ft_order_side == 'stoploss'))
@ -352,7 +363,7 @@ class FreqtradeBot(LoggingMixin):
"Order is older than 5 days. Assuming order was fully cancelled.")
fo = order.to_ccxt_object()
fo['status'] = 'canceled'
self.handle_timedout_order(fo, order.trade)
self.handle_cancel_order(fo, order.trade, constants.CANCEL_REASON['TIMEOUT'])
except ExchangeError as e:
@ -374,7 +385,7 @@ class FreqtradeBot(LoggingMixin):
for trade in trades:
if not trade.is_open and not trade.fee_updated(trade.exit_side):
# Get sell fee
order = trade.select_order(trade.exit_side, False)
order = trade.select_order(trade.exit_side, False, only_filled=True)
if not order:
order = trade.select_order('stoploss', False)
if order:
@ -390,7 +401,7 @@ class FreqtradeBot(LoggingMixin):
for trade in trades:
with self._exit_lock:
if trade.is_open and not trade.fee_updated(trade.entry_side):
order = trade.select_order(trade.entry_side, False)
order = trade.select_order(trade.entry_side, False, only_filled=True)
open_order = trade.select_order(trade.entry_side, True)
if order and open_order is None:
logger.info(
@ -720,7 +731,7 @@ class FreqtradeBot(LoggingMixin):
time_in_force=time_in_force,
leverage=leverage
)
order_obj = Order.parse_from_ccxt_object(order, pair, side)
order_obj = Order.parse_from_ccxt_object(order, pair, side, amount, enter_limit_requested)
order_id = order['id']
order_status = order.get('status')
logger.info(f"Order #{order_id} was created for {pair} and status is {order_status}.")
@ -747,13 +758,15 @@ class FreqtradeBot(LoggingMixin):
self.exchange.name, order['filled'], order['amount'],
order['remaining']
)
amount = safe_value_fallback(order, 'filled', 'amount')
enter_limit_filled_price = safe_value_fallback(order, 'average', 'price')
amount = safe_value_fallback(order, 'filled', 'amount', amount)
enter_limit_filled_price = safe_value_fallback(
order, 'average', 'price', enter_limit_filled_price)
# in case of FOK the order may be filled immediately and fully
elif order_status == 'closed':
amount = safe_value_fallback(order, 'filled', 'amount')
enter_limit_filled_price = safe_value_fallback(order, 'average', 'price')
amount = safe_value_fallback(order, 'filled', 'amount', amount)
enter_limit_filled_price = safe_value_fallback(
order, 'average', 'price', enter_limit_requested)
# Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
fee = self.exchange.get_fee(symbol=pair, taker_or_maker='maker')
@ -1094,7 +1107,8 @@ class FreqtradeBot(LoggingMixin):
leverage=trade.leverage
)
order_obj = Order.parse_from_ccxt_object(stoploss_order, trade.pair, 'stoploss')
order_obj = Order.parse_from_ccxt_object(stoploss_order, trade.pair, 'stoploss',
trade.amount, stop_price)
trade.orders.append(order_obj)
trade.stoploss_order_id = str(stoploss_order['id'])
trade.stoploss_last_update = datetime.now(timezone.utc)
@ -1156,15 +1170,13 @@ class FreqtradeBot(LoggingMixin):
# If enter order is fulfilled but there is no stoploss, we add a stoploss on exchange
if not stoploss_order:
stoploss = (
self.edge.stoploss(pair=trade.pair)
if self.edge else
trade.stop_loss_pct / trade.leverage
stop_price = trade.stoploss_or_liquidation
if self.edge:
stoploss = self.edge.stoploss(pair=trade.pair)
stop_price = (
trade.open_rate * (1 - stoploss) if trade.is_short
else trade.open_rate * (1 + stoploss)
)
if trade.is_short:
stop_price = trade.open_rate * (1 - stoploss)
else:
stop_price = trade.open_rate * (1 + stoploss)
if self.create_stoploss_order(trade=trade, stop_price=stop_price):
# The above will return False if the placement failed and the trade was force-sold.
@ -1249,11 +1261,11 @@ class FreqtradeBot(LoggingMixin):
if not_closed:
if fully_cancelled or (order_obj and self.strategy.ft_check_timed_out(
trade, order_obj, datetime.now(timezone.utc))):
self.handle_timedout_order(order, trade)
self.handle_cancel_order(order, trade, constants.CANCEL_REASON['TIMEOUT'])
else:
self.replace_order(order, order_obj, trade)
def handle_timedout_order(self, order: Dict, trade: Trade) -> None:
def handle_cancel_order(self, order: Dict, trade: Trade, reason: str) -> None:
"""
Check if current analyzed order timed out and cancel if necessary.
:param order: Order dict grabbed with exchange.fetch_order()
@ -1261,10 +1273,10 @@ class FreqtradeBot(LoggingMixin):
:return: None
"""
if order['side'] == trade.entry_side:
self.handle_cancel_enter(trade, order, constants.CANCEL_REASON['TIMEOUT'])
self.handle_cancel_enter(trade, order, reason)
else:
canceled = self.handle_cancel_exit(
trade, order, constants.CANCEL_REASON['TIMEOUT'])
trade, order, reason)
canceled_count = trade.get_exit_order_count()
max_timeouts = self.config.get('unfilledtimeout', {}).get('exit_timeout_count', 0)
if canceled and max_timeouts > 0 and canceled_count >= max_timeouts:
@ -1518,7 +1530,7 @@ class FreqtradeBot(LoggingMixin):
*,
exit_tag: Optional[str] = None,
ordertype: Optional[str] = None,
sub_trade_amt: float = None,
sub_trade_amt: Optional[float] = None,
) -> bool:
"""
Executes a trade exit for the given trade and limit
@ -1595,7 +1607,7 @@ class FreqtradeBot(LoggingMixin):
self.handle_insufficient_funds(trade)
return False
order_obj = Order.parse_from_ccxt_object(order, trade.pair, trade.exit_side)
order_obj = Order.parse_from_ccxt_object(order, trade.pair, trade.exit_side, amount, limit)
trade.orders.append(order_obj)
trade.open_order_id = order['id']
@ -1612,7 +1624,7 @@ class FreqtradeBot(LoggingMixin):
return True
def _notify_exit(self, trade: Trade, order_type: str, fill: bool = False,
sub_trade: bool = False, order: Order = None) -> None:
sub_trade: bool = False, order: Optional[Order] = None) -> None:
"""
Sends rpc notification when a sell occurred.
"""
@ -1622,7 +1634,7 @@ class FreqtradeBot(LoggingMixin):
# second condition is for mypy only; order will always be passed during sub trade
if sub_trade and order is not None:
amount = order.safe_filled if fill else order.amount
amount = order.safe_filled if fill else order.safe_amount
order_rate: float = order.safe_price
profit = trade.calc_profit(rate=order_rate, amount=amount, open_rate=trade.open_rate)
@ -1725,7 +1737,8 @@ class FreqtradeBot(LoggingMixin):
# Common update trade state methods
#
def update_trade_state(self, trade: Trade, order_id: str, action_order: Dict[str, Any] = None,
def update_trade_state(
self, trade: Trade, order_id: str, action_order: Optional[Dict[str, Any]] = None,
stoploss_order: bool = False, send_msg: bool = True) -> bool:
"""
Checks trades with open orders and updates the amount if necessary
@ -1784,6 +1797,7 @@ class FreqtradeBot(LoggingMixin):
is_short=trade.is_short,
amount=trade.amount,
stake_amount=trade.stake_amount,
leverage=trade.leverage,
wallet_balance=trade.stake_amount,
))

4
freqtrade/main.py
View File

@ -5,7 +5,7 @@ Read the documentation to know what cli arguments you need.
"""
import logging
import sys
from typing import Any, List
from typing import Any, List, Optional
from freqtrade.util.gc_setup import gc_set_threshold
@ -23,7 +23,7 @@ from freqtrade.loggers import setup_logging_pre
logger = logging.getLogger('freqtrade')
def main(sysargv: List[str] = None) -> None:
def main(sysargv: Optional[List[str]] = None) -> None:
"""
This function will initiate the bot and start the trading loop.
:return: None

6
freqtrade/misc.py
View File

@ -6,7 +6,7 @@ import logging
import re
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, Iterator, List, Mapping, Union
from typing import Any, Dict, Iterator, List, Mapping, Optional, Union
from typing.io import IO
from urllib.parse import urlparse
@ -205,7 +205,7 @@ def safe_value_fallback2(dict1: dictMap, dict2: dictMap, key1: str, key2: str, d
return default_value
def plural(num: float, singular: str, plural: str = None) -> str:
def plural(num: float, singular: str, plural: Optional[str] = None) -> str:
return singular if (num == 1 or num == -1) else plural or singular + 's'
@ -269,6 +269,8 @@ def dataframe_to_json(dataframe: pd.DataFrame) -> str:
def default(z):
if isinstance(z, pd.Timestamp):
return z.timestamp() * 1e3
if z is pd.NaT:
return 'NaT'
raise TypeError
return str(orjson.dumps(dataframe.to_dict(orient='split'), default=default), 'utf-8')

101
freqtrade/optimize/backtesting.py
View File

@ -15,7 +15,7 @@ from pandas import DataFrame
from freqtrade import constants
from freqtrade.configuration import TimeRange, validate_config_consistency
from freqtrade.constants import DATETIME_PRINT_FORMAT, Config, LongShort
from freqtrade.constants import DATETIME_PRINT_FORMAT, Config, IntOrInf, LongShort
from freqtrade.data import history
from freqtrade.data.btanalysis import find_existing_backtest_stats, trade_list_to_dataframe
from freqtrade.data.converter import trim_dataframe, trim_dataframes
@ -37,6 +37,7 @@ from freqtrade.plugins.protectionmanager import ProtectionManager
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.util.binance_mig import migrate_binance_futures_data
from freqtrade.wallets import Wallets
@ -157,6 +158,7 @@ class Backtesting:
self._can_short = self.trading_mode != TradingMode.SPOT
self._position_stacking: bool = self.config.get('position_stacking', False)
self.enable_protections: bool = self.config.get('enable_protections', False)
migrate_binance_futures_data(config)
self.init_backtest()
@ -573,26 +575,6 @@ class Backtesting:
""" Rate is within candle, therefore filled"""
return row[LOW_IDX] <= rate <= row[HIGH_IDX]
def _get_exit_trade_entry_for_candle(self, trade: LocalTrade,
row: Tuple) -> Optional[LocalTrade]:
# Check if we need to adjust our current positions
if self.strategy.position_adjustment_enable:
trade = self._get_adjust_trade_entry_for_candle(trade, row)
enter = row[SHORT_IDX] if trade.is_short else row[LONG_IDX]
exit_sig = row[ESHORT_IDX] if trade.is_short else row[ELONG_IDX]
exits = self.strategy.should_exit(
trade, row[OPEN_IDX], row[DATE_IDX].to_pydatetime(), # type: ignore
enter=enter, exit_=exit_sig,
low=row[LOW_IDX], high=row[HIGH_IDX]
)
for exit_ in exits:
t = self._get_exit_for_signal(trade, row, exit_)
if t:
return t
return None
def _get_exit_for_signal(
self, trade: LocalTrade, row: Tuple, exit_: ExitCheckTuple,
amount: Optional[float] = None) -> Optional[LocalTrade]:
@ -662,7 +644,7 @@ class Backtesting:
return None
def _exit_trade(self, trade: LocalTrade, sell_row: Tuple,
close_rate: float, amount: float = None) -> Optional[LocalTrade]:
close_rate: float, amount: Optional[float] = None) -> Optional[LocalTrade]:
self.order_id_counter += 1
exit_candle_time = sell_row[DATE_IDX].to_pydatetime()
order_type = self.strategy.order_types['exit']
@ -692,11 +674,10 @@ class Backtesting:
trade.orders.append(order)
return trade
def _get_exit_trade_entry(
self, trade: LocalTrade, row: Tuple, is_first: bool) -> Optional[LocalTrade]:
def _check_trade_exit(self, trade: LocalTrade, row: Tuple) -> Optional[LocalTrade]:
exit_candle_time: datetime = row[DATE_IDX].to_pydatetime()
if is_first and self.trading_mode == TradingMode.FUTURES:
if self.trading_mode == TradingMode.FUTURES:
trade.funding_fees = self.exchange.calculate_funding_fees(
self.futures_data[trade.pair],
amount=trade.amount,
@ -705,7 +686,22 @@ class Backtesting:
close_date=exit_candle_time,
)
return self._get_exit_trade_entry_for_candle(trade, row)
# Check if we need to adjust our current positions
if self.strategy.position_adjustment_enable:
trade = self._get_adjust_trade_entry_for_candle(trade, row)
enter = row[SHORT_IDX] if trade.is_short else row[LONG_IDX]
exit_sig = row[ESHORT_IDX] if trade.is_short else row[ELONG_IDX]
exits = self.strategy.should_exit(
trade, row[OPEN_IDX], row[DATE_IDX].to_pydatetime(), # type: ignore
enter=enter, exit_=exit_sig,
low=row[LOW_IDX], high=row[HIGH_IDX]
)
for exit_ in exits:
t = self._get_exit_for_signal(trade, row, exit_)
if t:
return t
return None
def get_valid_price_and_stake(
self, pair: str, row: Tuple, propose_rate: float, stake_amount: float,
@ -779,6 +775,11 @@ class Backtesting:
trade: Optional[LocalTrade] = None,
requested_rate: Optional[float] = None,
requested_stake: Optional[float] = None) -> Optional[LocalTrade]:
"""
:param trade: Trade to adjust - initial entry if None
:param requested_rate: Adjusted entry rate
:param requested_stake: Stake amount for adjusted orders (`adjust_entry_price`).
"""
current_time = row[DATE_IDX].to_pydatetime()
entry_tag = row[ENTER_TAG_IDX] if len(row) >= ENTER_TAG_IDX + 1 else None
@ -804,7 +805,7 @@ class Backtesting:
return trade
time_in_force = self.strategy.order_time_in_force['entry']
if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
if stake_amount and (not min_stake_amount or stake_amount >= min_stake_amount):
self.order_id_counter += 1
base_currency = self.exchange.get_pair_base_currency(pair)
amount_p = (stake_amount / propose_rate) * leverage
@ -867,6 +868,7 @@ class Backtesting:
open_rate=propose_rate,
amount=amount,
stake_amount=trade.stake_amount,
leverage=trade.leverage,
wallet_balance=trade.stake_amount,
is_short=is_short,
))
@ -920,8 +922,9 @@ class Backtesting:
trade.close(exit_row[OPEN_IDX], show_msg=False)
LocalTrade.close_bt_trade(trade)
def trade_slot_available(self, max_open_trades: int, open_trade_count: int) -> bool:
def trade_slot_available(self, open_trade_count: int) -> bool:
# Always allow trades when max_open_trades is enabled.
max_open_trades: IntOrInf = self.config['max_open_trades']
if max_open_trades <= 0 or open_trade_count < max_open_trades:
return True
# Rejected trade
@ -1051,7 +1054,8 @@ class Backtesting:
def backtest_loop(
self, row: Tuple, pair: str, current_time: datetime, end_date: datetime,
max_open_trades: int, open_trade_count_start: int, is_first: bool = True) -> int:
open_trade_count_start: int, trade_dir: Optional[LongShort],
is_first: bool = True) -> int:
"""
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
@ -1070,11 +1074,10 @@ class Backtesting:
# max_open_trades must be respected
# don't open on the last row
# We only open trades on the main candle, not on detail candles
trade_dir = self.check_for_trade_entry(row)
if (
(self._position_stacking or len(LocalTrade.bt_trades_open_pp[pair]) == 0)
and is_first
and self.trade_slot_available(max_open_trades, open_trade_count_start)
and self.trade_slot_available(open_trade_count_start)
and current_time != end_date
and trade_dir is not None
and not PairLocks.is_pair_locked(pair, row[DATE_IDX], trade_dir)
@ -1099,7 +1102,7 @@ class Backtesting:
# 4. Create exit orders (if any)
if not trade.open_order_id:
self._get_exit_trade_entry(trade, row, is_first) # Place exit order if necessary
self._check_trade_exit(trade, row) # Place exit order if necessary
# 5. Process exit orders.
order = trade.select_order(trade.exit_side, is_open=True)
@ -1121,8 +1124,7 @@ class Backtesting:
return open_trade_count_start
def backtest(self, processed: Dict,
start_date: datetime, end_date: datetime,
max_open_trades: int = 0) -> Dict[str, Any]:
start_date: datetime, end_date: datetime) -> Dict[str, Any]:
"""
Implement backtesting functionality
@ -1134,7 +1136,6 @@ class Backtesting:
optimize memory usage!
: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
:return: DataFrame with trades (results of backtesting)
"""
self.prepare_backtest(self.enable_protections)
@ -1164,7 +1165,15 @@ class Backtesting:
indexes[pair] = row_index
self.dataprovider._set_dataframe_max_index(row_index)
current_detail_time: datetime = row[DATE_IDX].to_pydatetime()
if self.timeframe_detail and pair in self.detail_data:
trade_dir: Optional[LongShort] = self.check_for_trade_entry(row)
if (
(trade_dir is not None or len(LocalTrade.bt_trades_open_pp[pair]) > 0)
and self.timeframe_detail and pair in self.detail_data
):
# Spread out into detail timeframe.
# Should only happen when we are either in a trade for this pair
# or when we got the signal for a new trade.
exit_candle_end = current_detail_time + timedelta(minutes=self.timeframe_min)
detail_data = self.detail_data[pair]
@ -1175,8 +1184,8 @@ class Backtesting:
if len(detail_data) == 0:
# Fall back to "regular" data if no detail data was found for this candle
open_trade_count_start = self.backtest_loop(
row, pair, current_time, end_date, max_open_trades,
open_trade_count_start)
row, pair, current_time, end_date,
open_trade_count_start, trade_dir)
continue
detail_data.loc[:, 'enter_long'] = row[LONG_IDX]
detail_data.loc[:, 'exit_long'] = row[ELONG_IDX]
@ -1188,13 +1197,14 @@ class Backtesting:
current_time_det = current_time
for det_row in detail_data[HEADERS].values.tolist():
open_trade_count_start = self.backtest_loop(
det_row, pair, current_time_det, end_date, max_open_trades,
open_trade_count_start, is_first)
det_row, pair, current_time_det, end_date,
open_trade_count_start, trade_dir, is_first)
current_time_det += timedelta(minutes=self.timeframe_detail_min)
is_first = False
else:
open_trade_count_start = self.backtest_loop(
row, pair, current_time, end_date, max_open_trades, open_trade_count_start)
row, pair, current_time, end_date,
open_trade_count_start, trade_dir)
# Move time one configured time_interval ahead.
self.progress.increment()
@ -1226,13 +1236,11 @@ class Backtesting:
self._set_strategy(strat)
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
# Must come from strategy config, as the strategy may modify this setting.
max_open_trades = self.strategy.config['max_open_trades']
else:
if not self.config.get('use_max_market_positions', True):
logger.info(
'Ignoring max_open_trades (--disable-max-market-positions was used) ...')
max_open_trades = 0
self.strategy.max_open_trades = float('inf')
self.config.update({'max_open_trades': self.strategy.max_open_trades})
# need to reprocess data every time to populate signals
preprocessed = self.strategy.advise_all_indicators(data)
@ -1255,7 +1263,6 @@ class Backtesting:
processed=preprocessed,
start_date=min_date,
end_date=max_date,
max_open_trades=max_open_trades,
)
backtest_end_time = datetime.now(timezone.utc)
results.update({

38
freqtrade/optimize/hyperopt.py
View File

@ -74,6 +74,7 @@ class Hyperopt:
self.roi_space: List[Dimension] = []
self.stoploss_space: List[Dimension] = []
self.trailing_space: List[Dimension] = []
self.max_open_trades_space: List[Dimension] = []
self.dimensions: List[Dimension] = []
self.config = config
@ -117,11 +118,10 @@ class Hyperopt:
self.current_best_epoch: Optional[Dict[str, Any]] = None
# Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
self.max_open_trades = self.config['max_open_trades']
else:
if not self.config.get('use_max_market_positions', True):
logger.debug('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
self.max_open_trades = 0
self.backtesting.strategy.max_open_trades = float('inf')
config.update({'max_open_trades': self.backtesting.strategy.max_open_trades})
if HyperoptTools.has_space(self.config, 'sell'):
# Make sure use_exit_signal is enabled
@ -209,6 +209,10 @@ class Hyperopt:
result['stoploss'] = {p.name: params.get(p.name) for p in self.stoploss_space}
if HyperoptTools.has_space(self.config, 'trailing'):
result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)
if HyperoptTools.has_space(self.config, 'trades'):
result['max_open_trades'] = {
'max_open_trades': self.backtesting.strategy.max_open_trades
if self.backtesting.strategy.max_open_trades != float('inf') else -1}
return result
@ -229,6 +233,8 @@ class Hyperopt:
'trailing_stop_positive_offset': strategy.trailing_stop_positive_offset,
'trailing_only_offset_is_reached': strategy.trailing_only_offset_is_reached,
}
if not HyperoptTools.has_space(self.config, 'trades'):
result['max_open_trades'] = {'max_open_trades': strategy.max_open_trades}
return result
def print_results(self, results) -> None:
@ -280,8 +286,13 @@ class Hyperopt:
logger.debug("Hyperopt has 'trailing' space")
self.trailing_space = self.custom_hyperopt.trailing_space()
if HyperoptTools.has_space(self.config, 'trades'):
logger.debug("Hyperopt has 'trades' space")
self.max_open_trades_space = self.custom_hyperopt.max_open_trades_space()
self.dimensions = (self.buy_space + self.sell_space + self.protection_space
+ self.roi_space + self.stoploss_space + self.trailing_space)
+ self.roi_space + self.stoploss_space + self.trailing_space
+ self.max_open_trades_space)
def assign_params(self, params_dict: Dict, category: str) -> None:
"""
@ -328,6 +339,20 @@ class Hyperopt:
self.backtesting.strategy.trailing_only_offset_is_reached = \
d['trailing_only_offset_is_reached']
if HyperoptTools.has_space(self.config, 'trades'):
if self.config["stake_amount"] == "unlimited" and \
(params_dict['max_open_trades'] == -1 or params_dict['max_open_trades'] == 0):
# Ignore unlimited max open trades if stake amount is unlimited
params_dict.update({'max_open_trades': self.config['max_open_trades']})
updated_max_open_trades = int(params_dict['max_open_trades']) \
if (params_dict['max_open_trades'] != -1
and params_dict['max_open_trades'] != 0) else float('inf')
self.config.update({'max_open_trades': updated_max_open_trades})
self.backtesting.strategy.max_open_trades = updated_max_open_trades
with self.data_pickle_file.open('rb') as f:
processed = load(f, mmap_mode='r')
if self.analyze_per_epoch:
@ -337,8 +362,7 @@ class Hyperopt:
bt_results = self.backtesting.backtest(
processed=processed,
start_date=self.min_date,
end_date=self.max_date,
max_open_trades=self.max_open_trades,
end_date=self.max_date
)
backtest_end_time = datetime.now(timezone.utc)
bt_results.update({

3
freqtrade/optimize/hyperopt_auto.py
View File

@ -91,5 +91,8 @@ class HyperOptAuto(IHyperOpt):
def trailing_space(self) -> List['Dimension']:
return self._get_func('trailing_space')()
def max_open_trades_space(self) -> List['Dimension']:
return self._get_func('max_open_trades_space')()
def generate_estimator(self, dimensions: List['Dimension'], **kwargs) -> EstimatorType:
return self._get_func('generate_estimator')(dimensions=dimensions, **kwargs)

10
freqtrade/optimize/hyperopt_interface.py
View File

@ -191,6 +191,16 @@ class IHyperOpt(ABC):
Categorical([True, False], name='trailing_only_offset_is_reached'),
]
def max_open_trades_space(self) -> List[Dimension]:
"""
Create a max open trades space.
You may override it in your custom Hyperopt class.
"""
return [
Integer(-1, 10, name='max_open_trades'),
]
# This is needed for proper unpickling the class attribute timeframe
# which is set to the actual value by the resolver.
# Why do I still need such shamanic mantras in modern python?

41
freqtrade/optimize/hyperopt_loss/hyperopt_loss_calmar.py
View File

@ -5,13 +5,11 @@ This module defines the alternative HyperOptLoss class which can be used for
Hyperoptimization.
"""
from datetime import datetime
from math import sqrt as msqrt
from typing import Any, Dict
from pandas import DataFrame
from freqtrade.constants import Config
from freqtrade.data.metrics import calculate_max_drawdown
from freqtrade.data.metrics import calculate_calmar
from freqtrade.optimize.hyperopt import IHyperOptLoss
@ -23,42 +21,15 @@ class CalmarHyperOptLoss(IHyperOptLoss):
"""
@staticmethod
def hyperopt_loss_function(
results: DataFrame,
trade_count: int,
min_date: datetime,
max_date: datetime,
config: Config,
processed: Dict[str, DataFrame],
backtest_stats: Dict[str, Any],
*args,
**kwargs
) -> float:
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
config: Config, *args, **kwargs) -> float:
"""
Objective function, returns smaller number for more optimal results.
Uses Calmar Ratio calculation.
"""
total_profit = backtest_stats["profit_total"]
days_period = (max_date - min_date).days
# adding slippage of 0.1% per trade
total_profit = total_profit - 0.0005
expected_returns_mean = total_profit.sum() / days_period * 100
# calculate max drawdown
try:
_, _, _, _, _, max_drawdown = calculate_max_drawdown(
results, value_col="profit_abs"
)
except ValueError:
max_drawdown = 0
if max_drawdown != 0:
calmar_ratio = expected_returns_mean / max_drawdown * msqrt(365)
else:
# Define high (negative) calmar ratio to be clear that this is NOT optimal.
calmar_ratio = -20.0
starting_balance = config['dry_run_wallet']
calmar_ratio = calculate_calmar(results, min_date, max_date, starting_balance)
# print(expected_returns_mean, max_drawdown, calmar_ratio)
return -calmar_ratio

21
freqtrade/optimize/hyperopt_loss/hyperopt_loss_sharpe.py
View File

@ -6,9 +6,10 @@ Hyperoptimization.
"""
from datetime import datetime
import numpy as np
from pandas import DataFrame
from freqtrade.constants import Config
from freqtrade.data.metrics import calculate_sharpe
from freqtrade.optimize.hyperopt import IHyperOptLoss
@ -22,25 +23,13 @@ class SharpeHyperOptLoss(IHyperOptLoss):
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
*args, **kwargs) -> float:
config: Config, *args, **kwargs) -> float:
"""
Objective function, returns smaller number for more optimal results.
Uses Sharpe Ratio calculation.
"""
total_profit = results["profit_ratio"]
days_period = (max_date - min_date).days
# adding slippage of 0.1% per trade
total_profit = total_profit - 0.0005
expected_returns_mean = total_profit.sum() / days_period
up_stdev = np.std(total_profit)
if up_stdev != 0:
sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
else:
# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
sharp_ratio = -20.
starting_balance = config['dry_run_wallet']
sharp_ratio = calculate_sharpe(results, min_date, max_date, starting_balance)
# print(expected_returns_mean, up_stdev, sharp_ratio)
return -sharp_ratio

2
freqtrade/optimize/hyperopt_loss/hyperopt_loss_sharpe_daily.py
View File

@ -44,7 +44,7 @@ class SharpeHyperOptLossDaily(IHyperOptLoss):
sum_daily = (
results.resample(resample_freq, on='close_date').agg(
{"profit_ratio_after_slippage": sum}).reindex(t_index).fillna(0)
{"profit_ratio_after_slippage": 'sum'}).reindex(t_index).fillna(0)
)
total_profit = sum_daily["profit_ratio_after_slippage"] - risk_free_rate

24
freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino.py
View File

@ -6,9 +6,10 @@ Hyperoptimization.
"""
from datetime import datetime
import numpy as np
from pandas import DataFrame
from freqtrade.constants import Config
from freqtrade.data.metrics import calculate_sortino
from freqtrade.optimize.hyperopt import IHyperOptLoss
@ -22,28 +23,13 @@ class SortinoHyperOptLoss(IHyperOptLoss):
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
*args, **kwargs) -> float:
config: Config, *args, **kwargs) -> float:
"""
Objective function, returns smaller number for more optimal results.
Uses Sortino Ratio calculation.
"""
total_profit = results["profit_ratio"]
days_period = (max_date - min_date).days
# adding slippage of 0.1% per trade
total_profit = total_profit - 0.0005
expected_returns_mean = total_profit.sum() / days_period
results['downside_returns'] = 0
results.loc[total_profit < 0, 'downside_returns'] = results['profit_ratio']
down_stdev = np.std(results['downside_returns'])
if down_stdev != 0:
sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
else:
# Define high (negative) sortino ratio to be clear that this is NOT optimal.
sortino_ratio = -20.
starting_balance = config['dry_run_wallet']
sortino_ratio = calculate_sortino(results, min_date, max_date, starting_balance)
# print(expected_returns_mean, down_stdev, sortino_ratio)
return -sortino_ratio

2
freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino_daily.py
View File

@ -46,7 +46,7 @@ class SortinoHyperOptLossDaily(IHyperOptLoss):
sum_daily = (
results.resample(resample_freq, on='close_date').agg(
{"profit_ratio_after_slippage": sum}).reindex(t_index).fillna(0)
{"profit_ratio_after_slippage": 'sum'}).reindex(t_index).fillna(0)
)
total_profit = sum_daily["profit_ratio_after_slippage"] - minimum_acceptable_return

15
freqtrade/optimize/hyperopt_tools.py
View File

@ -96,7 +96,7 @@ class HyperoptTools():
Tell if the space value is contained in the configuration
"""
# 'trailing' and 'protection spaces are not included in the 'default' set of spaces
if space in ('trailing', 'protection'):
if space in ('trailing', 'protection', 'trades'):
return any(s in config['spaces'] for s in [space, 'all'])
else:
return any(s in config['spaces'] for s in [space, 'all', 'default'])
@ -170,7 +170,7 @@ class HyperoptTools():
@staticmethod
def show_epoch_details(results, total_epochs: int, print_json: bool,
no_header: bool = False, header_str: str = None) -> None:
no_header: bool = False, header_str: Optional[str] = None) -> None:
"""
Display details of the hyperopt result
"""
@ -187,7 +187,8 @@ class HyperoptTools():
if print_json:
result_dict: Dict = {}
for s in ['buy', 'sell', 'protection', 'roi', 'stoploss', 'trailing']:
for s in ['buy', 'sell', 'protection',
'roi', 'stoploss', 'trailing', 'max_open_trades']:
HyperoptTools._params_update_for_json(result_dict, params, non_optimized, s)
print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
@ -201,6 +202,8 @@ class HyperoptTools():
HyperoptTools._params_pretty_print(params, 'roi', "ROI table:", non_optimized)
HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:", non_optimized)
HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:", non_optimized)
HyperoptTools._params_pretty_print(
params, 'max_open_trades', "Max Open Trades:", non_optimized)
@staticmethod
def _params_update_for_json(result_dict, params, non_optimized, space: str) -> None:
@ -239,7 +242,9 @@ class HyperoptTools():
if space == "stoploss":
stoploss = safe_value_fallback2(space_params, no_params, space, space)
result += (f"stoploss = {stoploss}{appendix}")
elif space == "max_open_trades":
max_open_trades = safe_value_fallback2(space_params, no_params, space, space)
result += (f"max_open_trades = {max_open_trades}{appendix}")
elif space == "roi":
result = result[:-1] + f'{appendix}\n'
minimal_roi_result = rapidjson.dumps({
@ -259,7 +264,7 @@ class HyperoptTools():
print(result)
@staticmethod
def _space_params(params, space: str, r: int = None) -> Dict:
def _space_params(params, space: str, r: Optional[int] = None) -> Dict:
d = params.get(space)
if d:
# Round floats to `r` digits after the decimal point if requested

4
freqtrade/optimize/optimize_reports.py
View File

@ -8,7 +8,7 @@ from pandas import DataFrame, to_datetime
from tabulate import tabulate
from freqtrade.constants import (DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT,
Config)
Config, IntOrInf)
from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum,
calculate_expectancy, calculate_market_change,
calculate_max_drawdown, calculate_sharpe, calculate_sortino)
@ -191,7 +191,7 @@ def generate_tag_metrics(tag_type: str,
return []
def generate_exit_reason_stats(max_open_trades: int, results: DataFrame) -> List[Dict]:
def generate_exit_reason_stats(max_open_trades: IntOrInf, results: DataFrame) -> List[Dict]:
"""
Generate small table outlining Backtest results
:param max_open_trades: Max_open_trades parameter

13
freqtrade/persistence/migrations.py
View File

@ -214,17 +214,22 @@ def migrate_orders_table(engine, table_back_name: str, cols_order: List):
average = get_column_def(cols_order, 'average', 'null')
stop_price = get_column_def(cols_order, 'stop_price', 'null')
funding_fee = get_column_def(cols_order, 'funding_fee', '0.0')
ft_amount = get_column_def(cols_order, 'ft_amount', 'coalesce(amount, 0.0)')
ft_price = get_column_def(cols_order, 'ft_price', 'coalesce(price, 0.0)')
# sqlite does not support literals for booleans
with engine.begin() as connection:
connection.execute(text(f"""
insert into orders (id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id,
status, symbol, order_type, side, price, amount, filled, average, remaining, cost,
stop_price, order_date, order_filled_date, order_update_date, ft_fee_base, funding_fee)
stop_price, order_date, order_filled_date, order_update_date, ft_fee_base, funding_fee,
ft_amount, ft_price
)
select id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id,
status, symbol, order_type, side, price, amount, filled, {average} average, remaining,
cost, {stop_price} stop_price, order_date, order_filled_date,
order_update_date, {ft_fee_base} ft_fee_base, {funding_fee} funding_fee
order_update_date, {ft_fee_base} ft_fee_base, {funding_fee} funding_fee,
{ft_amount} ft_amount, {ft_price} ft_price
from {table_back_name}
"""))
@ -311,8 +316,8 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
# if ('orders' not in previous_tables
# or not has_column(cols_orders, 'funding_fee')):
migrating = False
# if not has_column(cols_orders, 'funding_fee'):
if not has_column(cols_trades, 'max_stake_amount'):
# if not has_column(cols_trades, 'max_stake_amount'):
if not has_column(cols_orders, 'ft_price'):
migrating = True
logger.info(f"Running database migration for trades - "
f"backup: {table_back_name}, {order_table_bak_name}")

4
freqtrade/persistence/pairlock.py
View File

@ -21,9 +21,9 @@ class PairLock(_DECL_BASE):
side = Column(String(25), nullable=False, default="*")
reason = Column(String(255), nullable=True)
# Time the pair was locked (start time)
lock_time = Column(DateTime, nullable=False)
lock_time = Column(DateTime(), nullable=False)
# Time until the pair is locked (end time)
lock_end_time = Column(DateTime, nullable=False, index=True)
lock_end_time = Column(DateTime(), nullable=False, index=True)
active = Column(Boolean, nullable=False, default=True, index=True)

4
freqtrade/persistence/pairlock_middleware.py
View File

@ -30,8 +30,8 @@ class PairLocks():
PairLocks.locks = []
@staticmethod
def lock_pair(pair: str, until: datetime, reason: str = None, *,
now: datetime = None, side: str = '*') -> PairLock:
def lock_pair(pair: str, until: datetime, reason: Optional[str] = None, *,
now: Optional[datetime] = None, side: str = '*') -> PairLock:
"""
Create PairLock from now to "until".
Uses database by default, unless PairLocks.use_db is set to False,

146
freqtrade/persistence/trade_model.py
View File

@ -46,29 +46,31 @@ class Order(_DECL_BASE):
trade = relationship("Trade", back_populates="orders")
# order_side can only be 'buy', 'sell' or 'stoploss'
ft_order_side: str = Column(String(25), nullable=False)
ft_pair: str = Column(String(25), nullable=False)
ft_order_side = Column(String(25), nullable=False)
ft_pair = Column(String(25), nullable=False)
ft_is_open = Column(Boolean, nullable=False, default=True, index=True)
ft_amount = Column(Float(), nullable=False)
ft_price = Column(Float(), nullable=False)
order_id: str = Column(String(255), nullable=False, index=True)
order_id = Column(String(255), nullable=False, index=True)
status = Column(String(255), nullable=True)
symbol = Column(String(25), nullable=True)
order_type: str = Column(String(50), nullable=True)
order_type = Column(String(50), nullable=True)
side = Column(String(25), nullable=True)
price = Column(Float, nullable=True)
average = Column(Float, nullable=True)
amount = Column(Float, nullable=True)
filled = Column(Float, nullable=True)
remaining = Column(Float, nullable=True)
cost = Column(Float, nullable=True)
stop_price = Column(Float, nullable=True)
order_date = Column(DateTime, nullable=True, default=datetime.utcnow)
order_filled_date = Column(DateTime, nullable=True)
order_update_date = Column(DateTime, nullable=True)
price = Column(Float(), nullable=True)
average = Column(Float(), nullable=True)
amount = Column(Float(), nullable=True)
filled = Column(Float(), nullable=True)
remaining = Column(Float(), nullable=True)
cost = Column(Float(), nullable=True)
stop_price = Column(Float(), nullable=True)
order_date = Column(DateTime(), nullable=True, default=datetime.utcnow)
order_filled_date = Column(DateTime(), nullable=True)
order_update_date = Column(DateTime(), nullable=True)
funding_fee = Column(Float, nullable=True)
funding_fee = Column(Float(), nullable=True)
ft_fee_base = Column(Float, nullable=True)
ft_fee_base = Column(Float(), nullable=True)
@property
def order_date_utc(self) -> datetime:
@ -82,9 +84,13 @@ class Order(_DECL_BASE):
self.order_filled_date.replace(tzinfo=timezone.utc) if self.order_filled_date else None
)
@property
def safe_amount(self) -> float:
return self.amount or self.ft_amount
@property
def safe_price(self) -> float:
return self.average or self.price or self.stop_price
return self.average or self.price or self.stop_price or self.ft_price
@property
def safe_filled(self) -> float:
@ -94,7 +100,7 @@ class Order(_DECL_BASE):
def safe_remaining(self) -> float:
return (
self.remaining if self.remaining is not None else
self.amount - (self.filled or 0.0)
self.safe_amount - (self.filled or 0.0)
)
@property
@ -140,7 +146,7 @@ class Order(_DECL_BASE):
# Assign funding fee up to this point
# (represents the funding fee since the last order)
self.funding_fee = self.trade.funding_fees
if (order.get('filled', 0.0) or 0.0) > 0:
if (order.get('filled', 0.0) or 0.0) > 0 and not self.order_filled_date:
self.order_filled_date = datetime.now(timezone.utc)
self.order_update_date = datetime.now(timezone.utc)
@ -166,7 +172,7 @@ class Order(_DECL_BASE):
def to_json(self, entry_side: str, minified: bool = False) -> Dict[str, Any]:
resp = {
'amount': self.amount,
'amount': self.safe_amount,
'safe_price': self.safe_price,
'ft_order_side': self.ft_order_side,
'order_filled_timestamp': int(self.order_filled_date.replace(
@ -227,11 +233,20 @@ class Order(_DECL_BASE):
logger.warning(f"Did not find order for {order}.")
@staticmethod
def parse_from_ccxt_object(order: Dict[str, Any], pair: str, side: str) -> 'Order':
def parse_from_ccxt_object(
order: Dict[str, Any], pair: str, side: str,
amount: Optional[float] = None, price: Optional[float] = None) -> 'Order':
"""
Parse an order from a ccxt object and return a new order Object.
Optional support for overriding amount and price is only used for test simplification.
"""
o = Order(order_id=str(order['id']), ft_order_side=side, ft_pair=pair)
o = Order(
order_id=str(order['id']),
ft_order_side=side,
ft_pair=pair,
ft_amount=amount if amount else order['amount'],
ft_price=price if price else order['price'],
)
o.update_from_ccxt_object(order)
return o
@ -784,7 +799,7 @@ class LocalTrade():
else:
return close_trade - fees
def calc_close_trade_value(self, rate: float, amount: float = None) -> float:
def calc_close_trade_value(self, rate: float, amount: Optional[float] = None) -> float:
"""
Calculate the Trade's close value including fees
:param rate: rate to compare with.
@ -822,7 +837,8 @@ class LocalTrade():
raise OperationalException(
f"{self.trading_mode.value} trading is not yet available using freqtrade")
def calc_profit(self, rate: float, amount: float = None, open_rate: float = None) -> float:
def calc_profit(self, rate: float, amount: Optional[float] = None,
open_rate: Optional[float] = None) -> float:
"""
Calculate the absolute profit in stake currency between Close and Open trade
:param rate: close rate to compare with.
@ -843,7 +859,8 @@ class LocalTrade():
return float(f"{profit:.8f}")
def calc_profit_ratio(
self, rate: float, amount: float = None, open_rate: float = None) -> float:
self, rate: float, amount: Optional[float] = None,
open_rate: Optional[float] = None) -> float:
"""
Calculates the profit as ratio (including fee).
:param rate: rate to compare with.
@ -956,11 +973,12 @@ class LocalTrade():
return None
def select_order(self, order_side: Optional[str] = None,
is_open: Optional[bool] = None) -> Optional[Order]:
is_open: Optional[bool] = None, only_filled: bool = False) -> Optional[Order]:
"""
Finds latest order for this orderside and status
:param order_side: ft_order_side of the order (either 'buy', 'sell' or 'stoploss')
:param is_open: Only search for open orders?
:param only_filled: Only search for Filled orders (only valid with is_open=False).
:return: latest Order object if it exists, else None
"""
orders = self.orders
@ -968,6 +986,8 @@ class LocalTrade():
orders = [o for o in orders if o.ft_order_side == order_side]
if is_open is not None:
orders = [o for o in orders if o.ft_is_open == is_open]
if is_open is False and only_filled:
orders = [o for o in orders if o.filled and o.status in NON_OPEN_EXCHANGE_STATES]
if len(orders) > 0:
return orders[-1]
else:
@ -1041,8 +1061,9 @@ class LocalTrade():
return self.exit_reason
@staticmethod
def get_trades_proxy(*, pair: str = None, is_open: bool = None,
open_date: datetime = None, close_date: datetime = None,
def get_trades_proxy(*, pair: Optional[str] = None, is_open: Optional[bool] = None,
open_date: Optional[datetime] = None,
close_date: Optional[datetime] = None,
) -> List['LocalTrade']:
"""
Helper function to query Trades.
@ -1156,44 +1177,44 @@ class Trade(_DECL_BASE, LocalTrade):
base_currency = Column(String(25), nullable=True)
stake_currency = Column(String(25), nullable=True)
is_open = Column(Boolean, nullable=False, default=True, index=True)
fee_open = Column(Float, nullable=False, default=0.0)
fee_open_cost = Column(Float, nullable=True)
fee_open = Column(Float(), nullable=False, default=0.0)
fee_open_cost = Column(Float(), nullable=True)
fee_open_currency = Column(String(25), nullable=True)
fee_close = Column(Float, nullable=False, default=0.0)
fee_close_cost = Column(Float, nullable=True)
fee_close = Column(Float(), nullable=False, default=0.0)
fee_close_cost = Column(Float(), nullable=True)
fee_close_currency = Column(String(25), nullable=True)
open_rate: float = Column(Float)
open_rate_requested = Column(Float)
open_rate: float = Column(Float())
open_rate_requested = Column(Float())
# open_trade_value - calculated via _calc_open_trade_value
open_trade_value = Column(Float)
close_rate: Optional[float] = Column(Float)
close_rate_requested = Column(Float)
realized_profit = Column(Float, default=0.0)
close_profit = Column(Float)
close_profit_abs = Column(Float)
stake_amount = Column(Float, nullable=False)
max_stake_amount = Column(Float)
amount = Column(Float)
amount_requested = Column(Float)
open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
close_date = Column(DateTime)
open_trade_value = Column(Float())
close_rate: Optional[float] = Column(Float())
close_rate_requested = Column(Float())
realized_profit = Column(Float(), default=0.0)
close_profit = Column(Float())
close_profit_abs = Column(Float())
stake_amount = Column(Float(), nullable=False)
max_stake_amount = Column(Float())
amount = Column(Float())
amount_requested = Column(Float())
open_date = Column(DateTime(), nullable=False, default=datetime.utcnow)
close_date = Column(DateTime())
open_order_id = Column(String(255))
# absolute value of the stop loss
stop_loss = Column(Float, nullable=True, default=0.0)
stop_loss = Column(Float(), nullable=True, default=0.0)
# percentage value of the stop loss
stop_loss_pct = Column(Float, nullable=True)
stop_loss_pct = Column(Float(), nullable=True)
# absolute value of the initial stop loss
initial_stop_loss = Column(Float, nullable=True, default=0.0)
initial_stop_loss = Column(Float(), nullable=True, default=0.0)
# percentage value of the initial stop loss
initial_stop_loss_pct = Column(Float, nullable=True)
initial_stop_loss_pct = Column(Float(), nullable=True)
# stoploss order id which is on exchange
stoploss_order_id = Column(String(255), nullable=True, index=True)
# last update time of the stoploss order on exchange
stoploss_last_update = Column(DateTime, nullable=True)
stoploss_last_update = Column(DateTime(), nullable=True)
# absolute value of the highest reached price
max_rate = Column(Float, nullable=True, default=0.0)
max_rate = Column(Float(), nullable=True, default=0.0)
# Lowest price reached
min_rate = Column(Float, nullable=True)
min_rate = Column(Float(), nullable=True)
exit_reason = Column(String(100), nullable=True)
exit_order_status = Column(String(100), nullable=True)
strategy = Column(String(100), nullable=True)
@ -1201,21 +1222,21 @@ class Trade(_DECL_BASE, LocalTrade):
timeframe = Column(Integer, nullable=True)
trading_mode = Column(Enum(TradingMode), nullable=True)
amount_precision = Column(Float, nullable=True)
price_precision = Column(Float, nullable=True)
amount_precision = Column(Float(), nullable=True)
price_precision = Column(Float(), nullable=True)
precision_mode = Column(Integer, nullable=True)
contract_size = Column(Float, nullable=True)
contract_size = Column(Float(), nullable=True)
# Leverage trading properties
leverage = Column(Float, nullable=True, default=1.0)
leverage = Column(Float(), nullable=True, default=1.0)
is_short = Column(Boolean, nullable=False, default=False)
liquidation_price = Column(Float, nullable=True)
liquidation_price = Column(Float(), nullable=True)
# Margin Trading Properties
interest_rate = Column(Float, nullable=False, default=0.0)
interest_rate = Column(Float(), nullable=False, default=0.0)
# Futures properties
funding_fees = Column(Float, nullable=True, default=None)
funding_fees = Column(Float(), nullable=True, default=None)
def __init__(self, **kwargs):
super().__init__(**kwargs)
@ -1239,8 +1260,9 @@ class Trade(_DECL_BASE, LocalTrade):
Trade.query.session.rollback()
@staticmethod
def get_trades_proxy(*, pair: str = None, is_open: bool = None,
open_date: datetime = None, close_date: datetime = None,
def get_trades_proxy(*, pair: Optional[str] = None, is_open: Optional[bool] = None,
open_date: Optional[datetime] = None,
close_date: Optional[datetime] = None,
) -> List['LocalTrade']:
"""
Helper function to query Trades.j

6
freqtrade/plot/plotting.py
View File

@ -436,9 +436,9 @@ def create_scatter(
return None
def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFrame = None, *,
indicators1: List[str] = [],
indicators2: List[str] = [],
def generate_candlestick_graph(
pair: str, data: pd.DataFrame, trades: Optional[pd.DataFrame] = None, *,
indicators1: List[str] = [], indicators2: List[str] = [],
plot_config: Dict[str, Dict] = {},
) -> go.Figure:
"""

6
freqtrade/plugins/pairlistmanager.py
View File

@ -23,7 +23,8 @@ logger = logging.getLogger(__name__)
class PairListManager(LoggingMixin):
def __init__(self, exchange, config: Config, dataprovider: DataProvider = None) -> None:
def __init__(
self, exchange, config: Config, dataprovider: Optional[DataProvider] = None) -> None:
self._exchange = exchange
self._config = config
self._whitelist = self._config['exchange'].get('pair_whitelist')
@ -153,7 +154,8 @@ class PairListManager(LoggingMixin):
return []
return whitelist
def create_pair_list(self, pairs: List[str], timeframe: str = None) -> ListPairsWithTimeframes:
def create_pair_list(
self, pairs: List[str], timeframe: Optional[str] = None) -> ListPairsWithTimeframes:
"""
Create list of pair tuples with (pair, timeframe)
"""

3
freqtrade/resolvers/iresolver.py
View File

@ -89,7 +89,8 @@ class IResolver:
module = importlib.util.module_from_spec(spec)
try:
spec.loader.exec_module(module) # type: ignore # importlib does not use typehints
except (ModuleNotFoundError, SyntaxError, ImportError, NameError) as err:
except (AttributeError, ModuleNotFoundError, SyntaxError,
ImportError, NameError) as err:
# Catch errors in case a specific module is not installed
logger.warning(f"Could not import {module_path} due to '{err}'")
if enum_failed:

9
freqtrade/resolvers/strategy_resolver.py
View File

@ -33,7 +33,7 @@ class StrategyResolver(IResolver):
extra_path = "strategy_path"
@staticmethod
def load_strategy(config: Config = None) -> IStrategy:
def load_strategy(config: Optional[Config] = None) -> IStrategy:
"""
Load the custom class from config parameter
:param config: configuration dictionary or None
@ -76,6 +76,7 @@ class StrategyResolver(IResolver):
("ignore_buying_expired_candle_after", 0),
("position_adjustment_enable", False),
("max_entry_position_adjustment", -1),
("max_open_trades", -1)
]
for attribute, default in attributes:
StrategyResolver._override_attribute_helper(strategy, config,
@ -110,6 +111,10 @@ class StrategyResolver(IResolver):
val = getattr(strategy, attribute)
# None's cannot exist in the config, so do not copy them
if val is not None:
# max_open_trades set to -1 in the strategy will be copied as infinity in the config
if attribute == 'max_open_trades' and val == -1:
config[attribute] = float('inf')
else:
config[attribute] = val
# Explicitly check for None here as other "falsy" values are possible
elif default is not None:
@ -128,6 +133,8 @@ class StrategyResolver(IResolver):
key=lambda t: t[0]))
if hasattr(strategy, 'stoploss'):
strategy.stoploss = float(strategy.stoploss)
if hasattr(strategy, 'max_open_trades') and strategy.max_open_trades < 0:
strategy.max_open_trades = float('inf')
return strategy
@staticmethod

7
freqtrade/rpc/api_server/api_schemas.py
View File

@ -3,7 +3,7 @@ from typing import Any, Dict, List, Optional, Union
from pydantic import BaseModel
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.constants import DATETIME_PRINT_FORMAT, IntOrInf
from freqtrade.enums import OrderTypeValues, SignalDirection, TradingMode
@ -165,9 +165,10 @@ class ShowConfig(BaseModel):
stake_amount: str
available_capital: Optional[float]
stake_currency_decimals: int
max_open_trades: int
max_open_trades: IntOrInf
minimal_roi: Dict[str, Any]
stoploss: Optional[float]
stoploss_on_exchange: bool
trailing_stop: Optional[bool]
trailing_stop_positive: Optional[float]
trailing_stop_positive_offset: Optional[float]
@ -422,7 +423,7 @@ class BacktestRequest(BaseModel):
timeframe: Optional[str]
timeframe_detail: Optional[str]
timerange: Optional[str]
max_open_trades: Optional[int]
max_open_trades: Optional[IntOrInf]
stake_amount: Optional[str]
enable_protections: bool
dry_run_wallet: Optional[float]

22
freqtrade/rpc/api_server/api_v1.py
View File

@ -40,7 +40,9 @@ logger = logging.getLogger(__name__)
# 2.20: Add websocket endpoints
# 2.21: Add new_candle messagetype
# 2.22: Add FreqAI to backtesting
API_VERSION = 2.22
# 2.23: Allow plot config request in webserver mode
# 2.24: Add cancel_open_order endpoint
API_VERSION = 2.24
# Public API, requires no auth.
router_public = APIRouter()
@ -122,6 +124,12 @@ def trades_delete(tradeid: int, rpc: RPC = Depends(get_rpc)):
return rpc._rpc_delete(tradeid)
@router.delete('/trades/{tradeid}/open-order', response_model=OpenTradeSchema, tags=['trading'])
def cancel_open_order(tradeid: int, rpc: RPC = Depends(get_rpc)):
rpc._rpc_cancel_open_order(tradeid)
return rpc._rpc_trade_status([tradeid])[0]
# TODO: Missing response model
@router.get('/edge', tags=['info'])
def edge(rpc: RPC = Depends(get_rpc)):
@ -248,8 +256,18 @@ def pair_history(pair: str, timeframe: str, timerange: str, strategy: str,
@router.get('/plot_config', response_model=PlotConfig, tags=['candle data'])
def plot_config(rpc: RPC = Depends(get_rpc)):
def plot_config(strategy: Optional[str] = None, config=Depends(get_config),
rpc: Optional[RPC] = Depends(get_rpc_optional)):
if not strategy:
if not rpc:
raise RPCException("Strategy is mandatory in webserver mode.")
return PlotConfig.parse_obj(rpc._rpc_plot_config())
else:
config1 = deepcopy(config)
config1.update({
'strategy': strategy
})
return PlotConfig.parse_obj(RPC._rpc_plot_config_with_strategy(config1))
@router.get('/strategies', response_model=StrategyListResponse, tags=['strategy'])

2
freqtrade/rpc/api_server/api_ws.py
View File

@ -90,7 +90,7 @@ async def _process_consumer_request(
elif type == RPCRequestType.ANALYZED_DF:
# Limit the amount of candles per dataframe to 'limit' or 1500
limit = min(data.get('limit', 1500), 1500) if data else None
limit = int(min(data.get('limit', 1500), 1500)) if data else None
pair = data.get('pair', None) if data else None
# For every pair in the generator, send a separate message

41
freqtrade/rpc/rpc.py
View File

@ -122,6 +122,7 @@ class RPC:
if config['max_open_trades'] != float('inf') else -1),
'minimal_roi': config['minimal_roi'].copy() if 'minimal_roi' in config else {},
'stoploss': config.get('stoploss'),
'stoploss_on_exchange': config.get('stoploss_on_exchange', False),
'trailing_stop': config.get('trailing_stop'),
'trailing_stop_positive': config.get('trailing_stop_positive'),
'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset'),
@ -673,6 +674,7 @@ class RPC:
if self._freqtrade.state == State.RUNNING:
# Set 'max_open_trades' to 0
self._freqtrade.config['max_open_trades'] = 0
self._freqtrade.strategy.max_open_trades = 0
return {'status': 'No more entries will occur from now. Run /reload_config to reset.'}
@ -811,6 +813,29 @@ class RPC:
else:
raise RPCException(f'Failed to enter position for {pair}.')
def _rpc_cancel_open_order(self, trade_id: int):
if self._freqtrade.state != State.RUNNING:
raise RPCException('trader is not running')
with self._freqtrade._exit_lock:
# Query for trade
trade = Trade.get_trades(
trade_filter=[Trade.id == trade_id, Trade.is_open.is_(True), ]
).first()
if not trade:
logger.warning('cancel_open_order: Invalid trade_id received.')
raise RPCException('Invalid trade_id.')
if not trade.open_order_id:
logger.warning('cancel_open_order: No open order for trade_id.')
raise RPCException('No open order for trade_id.')
try:
order = self._freqtrade.exchange.fetch_order(trade.open_order_id, trade.pair)
except ExchangeError as e:
logger.info(f"Cannot query order for {trade} due to {e}.", exc_info=True)
raise RPCException("Order not found.")
self._freqtrade.handle_cancel_order(order, trade, CANCEL_REASON['USER_CANCEL'])
Trade.commit()
def _rpc_delete(self, trade_id: int) -> Dict[str, Union[str, int]]:
"""
Handler for delete <id>.
@ -944,7 +969,7 @@ class RPC:
resp['errors'] = errors
return resp
def _rpc_blacklist(self, add: List[str] = None) -> Dict:
def _rpc_blacklist(self, add: Optional[List[str]] = None) -> Dict:
""" Returns the currently active blacklist"""
errors = {}
if add:
@ -1126,12 +1151,12 @@ class RPC:
return self._freqtrade.active_pair_whitelist
@staticmethod
def _rpc_analysed_history_full(config, pair: str, timeframe: str,
def _rpc_analysed_history_full(config: Config, pair: str, timeframe: str,
timerange: str, exchange) -> Dict[str, Any]:
timerange_parsed = TimeRange.parse_timerange(timerange)
_data = load_data(
datadir=config.get("datadir"),
datadir=config["datadir"],
pairs=[pair],
timeframe=timeframe,
timerange=timerange_parsed,
@ -1156,6 +1181,16 @@ class RPC:
self._freqtrade.strategy.plot_config['subplots'] = {}
return self._freqtrade.strategy.plot_config
@staticmethod
def _rpc_plot_config_with_strategy(config: Config) -> Dict[str, Any]:
from freqtrade.resolvers.strategy_resolver import StrategyResolver
strategy = StrategyResolver.load_strategy(config)
if (strategy.plot_config and 'subplots' not in strategy.plot_config):
strategy.plot_config['subplots'] = {}
return strategy.plot_config
@staticmethod
def _rpc_sysinfo() -> Dict[str, Any]:
return {

26
freqtrade/rpc/telegram.py
View File

@ -174,6 +174,7 @@ class Telegram(RPCHandler):
self._force_enter, order_side=SignalDirection.SHORT)),
CommandHandler('trades', self._trades),
CommandHandler('delete', self._delete_trade),
CommandHandler(['coo', 'cancel_open_order'], self._cancel_open_order),
CommandHandler('performance', self._performance),
CommandHandler(['buys', 'entries'], self._enter_tag_performance),
CommandHandler(['sells', 'exits'], self._exit_reason_performance),
@ -1144,10 +1145,25 @@ class Telegram(RPCHandler):
raise RPCException("Trade-id not set.")
trade_id = int(context.args[0])
msg = self._rpc._rpc_delete(trade_id)
self._send_msg((
self._send_msg(
f"`{msg['result_msg']}`\n"
'Please make sure to take care of this asset on the exchange manually.'
))
)
@authorized_only
def _cancel_open_order(self, update: Update, context: CallbackContext) -> None:
"""
Handler for /cancel_open_order <id>.
Cancel open order for tradeid
:param bot: telegram bot
:param update: message update
:return: None
"""
if not context.args or len(context.args) == 0:
raise RPCException("Trade-id not set.")
trade_id = int(context.args[0])
self._rpc._rpc_cancel_open_order(trade_id)
self._send_msg('Open order canceled.')
@authorized_only
def _performance(self, update: Update, context: CallbackContext) -> None:
@ -1456,6 +1472,10 @@ class Telegram(RPCHandler):
"*/fx <trade_id>|all:* `Alias to /forceexit`\n"
f"{force_enter_text if self._config.get('force_entry_enable', False) else ''}"
"*/delete <trade_id>:* `Instantly delete the given trade in the database`\n"
"*/cancel_open_order <trade_id>:* `Cancels open orders for trade. "
"Only valid when the trade has open orders.`\n"
"*/coo <trade_id>|all:* `Alias to /cancel_open_order`\n"
"*/whitelist [sorted] [baseonly]:* `Show current whitelist. Optionally in "
"order and/or only displaying the base currency of each pairing.`\n"
"*/blacklist [pair]:* `Show current blacklist, or adds one or more pairs "
@ -1605,7 +1625,7 @@ class Telegram(RPCHandler):
def _send_msg(self, msg: str, parse_mode: str = ParseMode.MARKDOWN,
disable_notification: bool = False,
keyboard: List[List[InlineKeyboardButton]] = None,
keyboard: Optional[List[List[InlineKeyboardButton]]] = None,
callback_path: str = "",
reload_able: bool = False,
query: Optional[CallbackQuery] = None) -> None:

7
freqtrade/strategy/hyper.py
View File

@ -4,7 +4,7 @@ This module defines a base class for auto-hyperoptable strategies.
"""
import logging
from pathlib import Path
from typing import Any, Dict, Iterator, List, Tuple, Type, Union
from typing import Any, Dict, Iterator, List, Optional, Tuple, Type, Union
from freqtrade.constants import Config
from freqtrade.exceptions import OperationalException
@ -36,7 +36,8 @@ class HyperStrategyMixin:
self._ft_params_from_file = params
# Init/loading of parameters is done as part of ft_bot_start().
def enumerate_parameters(self, category: str = None) -> Iterator[Tuple[str, BaseParameter]]:
def enumerate_parameters(
self, category: Optional[str] = None) -> Iterator[Tuple[str, BaseParameter]]:
"""
Find all optimizable parameters and return (name, attr) iterator.
:param category:
@ -80,6 +81,8 @@ class HyperStrategyMixin:
self.stoploss = params.get('stoploss', {}).get(
'stoploss', getattr(self, 'stoploss', -0.1))
self.max_open_trades = params.get('max_open_trades', {}).get(
'max_open_trades', getattr(self, 'max_open_trades', -1))
trailing = params.get('trailing', {})
self.trailing_stop = trailing.get(
'trailing_stop', getattr(self, 'trailing_stop', False))

118
freqtrade/strategy/interface.py
View File

@ -10,7 +10,7 @@ from typing import Dict, List, Optional, Tuple, Union
import arrow
from pandas import DataFrame
from freqtrade.constants import Config, ListPairsWithTimeframes
from freqtrade.constants import Config, IntOrInf, ListPairsWithTimeframes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, RunMode, SignalDirection,
SignalTagType, SignalType, TradingMode)
@ -54,6 +54,9 @@ class IStrategy(ABC, HyperStrategyMixin):
# associated stoploss
stoploss: float
# max open trades for the strategy
max_open_trades: IntOrInf
# trailing stoploss
trailing_stop: bool = False
trailing_stop_positive: Optional[float] = None
@ -595,9 +598,10 @@ class IStrategy(ABC, HyperStrategyMixin):
return None
def populate_any_indicators(self, pair: str, df: DataFrame, tf: str,
informative: DataFrame = None,
informative: Optional[DataFrame] = None,
set_generalized_indicators: bool = False) -> DataFrame:
"""
DEPRECATED - USE FEATURE ENGINEERING FUNCTIONS INSTEAD
Function designed to automatically generate, name and merge features
from user indicated timeframes in the configuration file. User can add
additional features here, but must follow the naming convention.
@ -610,6 +614,102 @@ class IStrategy(ABC, HyperStrategyMixin):
"""
return df
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int,
metadata: Dict, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
`include_corr_pairs`. In other words, a single feature defined in this function
will automatically expand to a total of
`indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
`include_corr_pairs` numbers of features added to the model.
All features must be prepended with `%` to be recognized by FreqAI internals.
More details on how these config defined parameters accelerate feature engineering
in the documentation at:
https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param dataframe: strategy dataframe which will receive the features
:param period: period of the indicator - usage example:
:param metadata: metadata of current pair
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
"""
return dataframe
def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
In other words, a single feature defined in this function
will automatically expand to a total of
`include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
numbers of features added to the model.
Features defined here will *not* be automatically duplicated on user defined
`indicator_periods_candles`
All features must be prepended with `%` to be recognized by FreqAI internals.
More details on how these config defined parameters accelerate feature engineering
in the documentation at:
https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param dataframe: strategy dataframe which will receive the features
:param metadata: metadata of current pair
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
"""
return dataframe
def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This optional function will be called once with the dataframe of the base timeframe.
This is the final function to be called, which means that the dataframe entering this
function will contain all the features and columns created by all other
freqai_feature_engineering_* functions.
This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
This function is a good place for any feature that should not be auto-expanded upon
(e.g. day of the week).
All features must be prepended with `%` to be recognized by FreqAI internals.
More details about feature engineering available:
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param dataframe: strategy dataframe which will receive the features
:param metadata: metadata of current pair
usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
"""
return dataframe
def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
Required function to set the targets for the model.
All targets must be prepended with `&` to be recognized by the FreqAI internals.
More details about feature engineering available:
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param dataframe: strategy dataframe which will receive the targets
:param metadata: metadata of current pair
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
"""
return dataframe
###
# END - Intended to be overridden by strategy
###
@ -663,7 +763,8 @@ class IStrategy(ABC, HyperStrategyMixin):
"""
return self.__class__.__name__
def lock_pair(self, pair: str, until: datetime, reason: str = None, side: str = '*') -> None:
def lock_pair(self, pair: str, until: datetime,
reason: Optional[str] = None, side: str = '*') -> None:
"""
Locks pair until a given timestamp happens.
Locked pairs are not analyzed, and are prevented from opening new trades.
@ -695,7 +796,8 @@ class IStrategy(ABC, HyperStrategyMixin):
"""
PairLocks.unlock_reason(reason, datetime.now(timezone.utc))
def is_pair_locked(self, pair: str, *, candle_date: datetime = None, side: str = '*') -> bool:
def is_pair_locked(self, pair: str, *, candle_date: Optional[datetime] = None,
side: str = '*') -> bool:
"""
Checks if a pair is currently locked
The 2nd, optional parameter ensures that locks are applied until the new candle arrives,
@ -866,7 +968,7 @@ class IStrategy(ABC, HyperStrategyMixin):
pair: str,
timeframe: str,
dataframe: DataFrame,
is_short: bool = None
is_short: Optional[bool] = None
) -> Tuple[bool, bool, Optional[str]]:
"""
Calculates current exit signal based based on the dataframe
@ -965,7 +1067,7 @@ class IStrategy(ABC, HyperStrategyMixin):
def should_exit(self, trade: Trade, rate: float, current_time: datetime, *,
enter: bool, exit_: bool,
low: float = None, high: float = None,
low: Optional[float] = None, high: Optional[float] = None,
force_stoploss: float = 0) -> List[ExitCheckTuple]:
"""
This function evaluates if one of the conditions required to trigger an exit order
@ -1053,8 +1155,8 @@ class IStrategy(ABC, HyperStrategyMixin):
def stop_loss_reached(self, current_rate: float, trade: Trade,
current_time: datetime, current_profit: float,
force_stoploss: float, low: float = None,
high: float = None) -> ExitCheckTuple:
force_stoploss: float, low: Optional[float] = None,
high: Optional[float] = None) -> ExitCheckTuple:
"""
Based on current profit of the trade and configured (trailing) stoploss,
decides to exit or not

169
freqtrade/templates/FreqaiExampleHybridStrategy.py
View File

@ -1,4 +1,5 @@
import logging
from typing import Dict
import numpy as np
import pandas as pd
@ -95,65 +96,137 @@ class FreqaiExampleHybridStrategy(IStrategy):
short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
# FreqAI required function, user can add or remove indicators, but general structure
# must stay the same.
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int,
metadata: Dict, **kwargs):
"""
User feeds these indicators to FreqAI to train a classifier to decide
if the market will go up or down.
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
`include_corr_pairs`. In other words, a single feature defined in this function
will automatically expand to a total of
`indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
`include_corr_pairs` numbers of features added to the model.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
All features must be prepended with `%` to be recognized by FreqAI internals.
More details on how these config defined parameters accelerate feature engineering
in the documentation at:
https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param dataframe: strategy dataframe which will receive the features
:param period: period of the indicator - usage example:
:param metadata: metadata of current pair
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
"""
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe), window=period, stds=2.2
)
dataframe["bb_lowerband-period"] = bollinger["lower"]
dataframe["bb_middleband-period"] = bollinger["mid"]
dataframe["bb_upperband-period"] = bollinger["upper"]
t = int(t)
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
informative[f"%-{pair}relative_volume-period_{t}"] = (
informative["volume"] / informative["volume"].rolling(t).mean()
dataframe["%-bb_width-period"] = (
dataframe["bb_upperband-period"]
- dataframe["bb_lowerband-period"]
) / dataframe["bb_middleband-period"]
dataframe["%-close-bb_lower-period"] = (
dataframe["close"] / dataframe["bb_lowerband-period"]
)
# FreqAI needs the following lines in order to detect features and automatically
# expand upon them.
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
dataframe["%-relative_volume-period"] = (
dataframe["volume"] / dataframe["volume"].rolling(period).mean()
)
# User can set the "target" here (in present case it is the
# "up" or "down")
if set_generalized_indicators:
# User "looks into the future" here to figure out if the future
# will be "up" or "down". This same column name is available to
# the user
df['&s-up_or_down'] = np.where(df["close"].shift(-50) >
df["close"], 'up', 'down')
return dataframe
return df
def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
In other words, a single feature defined in this function
will automatically expand to a total of
`include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
numbers of features added to the model.
Features defined here will *not* be automatically duplicated on user defined
`indicator_periods_candles`
All features must be prepended with `%` to be recognized by FreqAI internals.
More details on how these config defined parameters accelerate feature engineering
in the documentation at:
https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param dataframe: strategy dataframe which will receive the features
:param metadata: metadata of current pair
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
"""
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"]
dataframe["%-raw_price"] = dataframe["close"]
return dataframe
def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This optional function will be called once with the dataframe of the base timeframe.
This is the final function to be called, which means that the dataframe entering this
function will contain all the features and columns created by all other
freqai_feature_engineering_* functions.
This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
This function is a good place for any feature that should not be auto-expanded upon
(e.g. day of the week).
All features must be prepended with `%` to be recognized by FreqAI internals.
More details about feature engineering available:
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param dataframe: strategy dataframe which will receive the features
:param metadata: metadata of current pair
usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
"""
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
return dataframe
def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
Required function to set the targets for the model.
All targets must be prepended with `&` to be recognized by the FreqAI internals.
More details about feature engineering available:
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param dataframe: strategy dataframe which will receive the targets
:param metadata: metadata of current pair
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
"""
dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-50) >
dataframe["close"], 'up', 'down')
return dataframe
# flake8: noqa: C901
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

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