Merge pull request #6832 from freqtrade/feat/freqai
Freqai: an interface for users to build/train/backtest predictive models and run them live
This commit is contained in:
Matthias
GitHub
commit
09ee9089fb
5
.gitignore
vendored
5
.gitignore
vendored
@ -7,6 +7,10 @@ logfile.txt
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user_data/*
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!user_data/strategy/sample_strategy.py
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!user_data/notebooks
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!user_data/models
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!user_data/freqaimodels
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user_data/freqaimodels/*
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user_data/models/*
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user_data/notebooks/*
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freqtrade-plot.html
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freqtrade-profit-plot.html
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@ -108,3 +112,4 @@ target/
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!config_examples/config_ftx.example.json
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!config_examples/config_full.example.json
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!config_examples/config_kraken.example.json
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!config_examples/config_freqai.example.json
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@ -63,6 +63,7 @@ Please find the complete documentation on the [freqtrade website](https://www.fr
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- [x] **Dry-run**: Run the bot without paying money.
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- [x] **Backtesting**: Run a simulation of your buy/sell strategy.
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- [x] **Strategy Optimization by machine learning**: Use machine learning to optimize your buy/sell strategy parameters with real exchange data.
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- [X] **Adaptive prediction modeling**: Build a smart strategy with FreqAI that self-trains to the market via adaptive machine learning methods. [Learn more](https://www.freqtrade.io/en/stable/freqai/)
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- [x] **Edge position sizing** Calculate your win rate, risk reward ratio, the best stoploss and adjust your position size before taking a position for each specific market. [Learn more](https://www.freqtrade.io/en/stable/edge/).
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- [x] **Whitelist crypto-currencies**: Select which crypto-currency you want to trade or use dynamic whitelists.
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- [x] **Blacklist crypto-currencies**: Select which crypto-currency you want to avoid.
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96
config_examples/config_freqai.example.json
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config_examples/config_freqai.example.json
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@ -0,0 +1,96 @@
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{
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"trading_mode": "futures",
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"margin_mode": "isolated",
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"max_open_trades": 5,
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"stake_currency": "USDT",
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"stake_amount": 200,
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"tradable_balance_ratio": 1,
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"fiat_display_currency": "USD",
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"dry_run": true,
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"timeframe": "3m",
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"dry_run_wallet": 1000,
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"cancel_open_orders_on_exit": true,
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"unfilledtimeout": {
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"entry": 10,
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"exit": 30
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},
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"exchange": {
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"name": "binance",
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"key": "",
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"secret": "",
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"ccxt_config": {
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"enableRateLimit": true
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},
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"ccxt_async_config": {
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"enableRateLimit": true,
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"rateLimit": 200
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},
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"pair_whitelist": [
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"1INCH/USDT",
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"ALGO/USDT"
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],
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"pair_blacklist": []
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},
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"entry_pricing": {
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"price_side": "same",
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"use_order_book": true,
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"order_book_top": 1,
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"price_last_balance": 0.0,
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"check_depth_of_market": {
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"enabled": false,
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"bids_to_ask_delta": 1
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}
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},
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"exit_pricing": {
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"price_side": "other",
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"use_order_book": true,
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"order_book_top": 1
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},
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"pairlists": [
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{
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"method": "StaticPairList"
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}
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],
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"freqai": {
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"enabled": true,
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"startup_candles": 10000,
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"purge_old_models": true,
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"train_period_days": 15,
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"backtest_period_days": 7,
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"live_retrain_hours": 0,
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"identifier": "uniqe-id",
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"feature_parameters": {
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"include_timeframes": [
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"3m",
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"15m",
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"1h"
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],
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"include_corr_pairlist": [
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"BTC/USDT",
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"ETH/USDT"
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],
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"label_period_candles": 20,
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"include_shifted_candles": 2,
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"DI_threshold": 0.9,
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"weight_factor": 0.9,
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"principal_component_analysis": false,
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"use_SVM_to_remove_outliers": true,
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"stratify_training_data": 0,
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"indicator_max_period_candles": 20,
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"indicator_periods_candles": [10, 20]
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},
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"data_split_parameters": {
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"test_size": 0.33,
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"random_state": 1
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},
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"model_training_parameters": {
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"n_estimators": 1000
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}
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},
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"bot_name": "",
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"force_entry_enable": true,
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"initial_state": "running",
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"internals": {
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"process_throttle_secs": 5
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}
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}
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17
docker/Dockerfile.freqai
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docker/Dockerfile.freqai
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ARG sourceimage=freqtradeorg/freqtrade
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ARG sourcetag=develop
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FROM ${sourceimage}:${sourcetag}
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USER root
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RUN apt-get install -y libgomp1
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USER ftuser
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# Install dependencies
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COPY requirements-freqai.txt /freqtrade/
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RUN pip install -r requirements-freqai.txt --user --no-cache-dir
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# Temporary step - as the source image will contain the wrong (non-freqai) sourcecode
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COPY --chown=ftuser:ftuser . /freqtrade/
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BIN
docs/assets/freqai_algo.png
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docs/assets/freqai_doc_logo.svg
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docs/assets/freqai_doc_logo.svg
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docs/assets/weights_factor.png
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docs/freqai.md
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# FreqAI
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FreqAI is a module designed to automate a variety of tasks associated with training a predictive model to generate market forecasts given a set of input features.
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Among the the features included:
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* **Self-adaptive retraining**: retrain models during live deployments to self-adapt to the market in an unsupervised manner.
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* **Rapid feature engineering**: create large rich feature sets (10k+ features) based on simple user created strategies.
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* **High performance**: adaptive retraining occurs on separate thread (or on GPU if available) from inferencing and bot trade operations. Keep newest models and data in memory for rapid inferencing.
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* **Realistic backtesting**: emulate self-adaptive retraining with backtesting module that automates past retraining.
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* **Modifiable**: use the generalized and robust architecture for incorporating any machine learning library/method available in Python. Seven examples available.
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* **Smart outlier removal**: remove outliers from training and prediction sets using a variety of outlier detection techniques.
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* **Crash resilience**: model storage to disk to make reloading from a crash fast and easy (and purge obsolete files for sustained dry/live runs).
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* **Automated data normalization**: normalize the data in a smart and statistically safe way.
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* **Automatic data download**: compute the data download timerange and update historic data (in live deployments).
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* **Clean incoming data** safe NaN handling before training and prediction.
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* **Dimensionality reduction**: reduce the size of the training data via Principal Component Analysis.
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* **Deploy bot fleets**: set one bot to train models while a fleet of other bots inference into the models and handle trades.
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## Quick start
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The easiest way to quickly test FreqAI is to run it in dry run with the following command
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```bash
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freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates
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```
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where the user will see the boot-up process of auto-data downloading, followed by simultaneous training and trading.
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The example strategy, example prediction model, and example config can all be found in
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`freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMRegressor.py`,
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`config_examples/config_freqai.example.json`, respectively.
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## General approach
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The user provides FreqAI with a set of custom *base* indicators (created inside the strategy the same way
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a typical Freqtrade strategy is created) as well as target values which look into the future.
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FreqAI trains a model to predict the target value based on the input of custom indicators for each pair in the whitelist. These models are consistently retrained to adapt to market conditions. FreqAI offers the ability to both backtest strategies (emulating reality with periodic retraining) and deploy dry/live. In dry/live conditions, FreqAI can be set to constant retraining in a background thread in an effort to keep models as young as possible.
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An overview of the algorithm is shown here to help users understand the data processing pipeline and the model usage.
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## Background and vocabulary
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**Features** are the quantities with which a model is trained. $X_i$ represents the
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vector of all features for a single candle. In FreqAI, the user
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builds the features from anything they can construct in the strategy.
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**Labels** are the target values with which the weights inside a model are trained
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toward. Each set of features is associated with a single label, which is also
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defined within the strategy by the user. These labels intentionally look into the
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future, and are not available to the model during dryrun/live/backtesting.
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**Training** refers to the process of feeding individual feature sets into the
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model with associated labels with the goal of matching input feature sets to associated labels.
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**Train data** is a subset of the historic data which is fed to the model during
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training to adjust weights. This data directly influences weight connections in the model.
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**Test data** is a subset of the historic data which is used to evaluate the
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intermediate performance of the model during training. This data does not
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directly influence nodal weights within the model.
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## Install prerequisites
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The normal Freqtrade install process will ask the user if they wish to install FreqAI dependencies. The user should reply "yes" to this question if they wish to use FreqAI. If the user did not reply yes, they can manually install these dependencies after the install with:
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``` bash
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pip install -r requirements-freqai.txt
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```
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!!! Note
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Catboost will not be installed on arm devices (raspberry, Mac M1, ARM based VPS, ...), since Catboost does not provide wheels for this platform.
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## Configuring FreqAI
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### Parameter table
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The table below will list all configuration parameters available for FreqAI.
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Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways.
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| Parameter | Description |
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|------------|-------------|
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| `freqai` | **Required.** The parent dictionary containing all the parameters below for controlling FreqAI. <br> **Datatype:** dictionary.
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| `identifier` | **Required.** A unique name for the current model. This can be reused to reload pre-trained models/data. <br> **Datatype:** string.
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| `train_period_days` | **Required.** Number of days to use for the training data (width of the sliding window). <br> **Datatype:** positive integer.
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| `backtest_period_days` | **Required.** Number of days to inference into the trained model before sliding the window and retraining. This can be fractional days, but beware that the user provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest. <br> **Datatype:** Float.
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| `live_retrain_hours` | Frequency of retraining during dry/live runs. Default set to 0, which means it will retrain as often as possible. <br> **Datatype:** Float > 0.
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| `follow_mode` | If true, this instance of FreqAI will look for models associated with `identifier` and load those for inferencing. A `follower` will **not** train new models. `False` by default. <br> **Datatype:** boolean.
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| `startup_candles` | Number of candles needed for *backtesting only* to ensure all indicators are non NaNs at the start of the first train period. <br> **Datatype:** positive integer.
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| `fit_live_predictions_candles` | Computes target (label) statistics from prediction data, instead of from the training data set. Number of candles is the number of historical candles it uses to generate the statistics. <br> **Datatype:** positive integer.
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| `purge_old_models` | Tell FreqAI to delete obsolete models. Otherwise, all historic models will remain on disk. Defaults to `False`. <br> **Datatype:** boolean.
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| `expiration_hours` | Ask FreqAI to avoid making predictions if a model is more than `expiration_hours` old. Defaults to 0 which means models never expire. <br> **Datatype:** positive integer.
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| | **Feature Parameters**
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| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples shown [here](#feature-engineering) <br> **Datatype:** dictionary.
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| `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` will be created for each coin in this list, and that set of features is added to the base asset feature set. <br> **Datatype:** list of assets (strings).
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| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for and added as features to the base asset feature set. <br> **Datatype:** list of timeframes (strings).
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| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators`, refer to `templates/FreqaiExampleStrategy.py` for detailed usage. The user can create custom labels, making use of this parameter not. <br> **Datatype:** positive integer.
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| `include_shifted_candles` | Parameter used to add a sense of temporal recency to flattened regression type input data. `include_shifted_candles` takes all features, duplicates and shifts them by the number indicated by user. <br> **Datatype:** positive integer.
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| `DI_threshold` | Activates the Dissimilarity Index for outlier detection when above 0, explained in detail [here](#removing-outliers-with-the-dissimilarity-index). <br> **Datatype:** positive float (typically below 1).
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| `weight_factor` | Used to set weights for training data points according to their recency, see details and a figure of how it works [here](#controlling-the-model-learning-process). <br> **Datatype:** positive float (typically below 1).
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| `principal_component_analysis` | Ask FreqAI to automatically reduce the dimensionality of the data set using PCA. <br> **Datatype:** boolean.
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| `use_SVM_to_remove_outliers` | Ask FreqAI to train a support vector machine to detect and remove outliers from the training data set as well as from incoming data points. <br> **Datatype:** boolean.
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| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. E.g. `nu` *Very* broadly, is the percentage of data points that should be considered outliers. `shuffle` is by default false to maintain reproducibility. But these and all others can be added/changed in this dictionary. <br> **Datatype:** dictionary.
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| `stratify_training_data` | This value is used to indicate the stratification of the data. e.g. 2 would set every 2nd data point into a separate dataset to be pulled from during training/testing. <br> **Datatype:** positive integer.
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| `indicator_max_period_candles` | The maximum *period* used in `populate_any_indicators()` for indicator creation. FreqAI uses this information in combination with the maximum timeframe to calculate how many data points it should download so that the first data point does not have a NaN <br> **Datatype:** positive integer.
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| `indicator_periods_candles` | A list of integers used to duplicate all indicators according to a set of periods and add them to the feature set. <br> **Datatype:** list of positive integers.
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| `use_DBSCAN_to_remove_outliers` | Inactive by default. If true, FreqAI clusters data using DBSCAN to identify and remove outliers from training and prediction data. <br> **Datatype:** float (fraction of 1).
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| | **Data split parameters**
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| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) <br> **Datatype:** dictionary.
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| `test_size` | Fraction of data that should be used for testing instead of training. <br> **Datatype:** positive float below 1.
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| `shuffle` | Shuffle the training data points during training. Typically for time-series forecasting, this is set to False. <br> **Datatype:** boolean.
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| | **Model training parameters**
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| `model_training_parameters` | A flexible dictionary that includes all parameters available by the user selected library. For example, if the user uses `LightGBMRegressor`, then this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html). If the user selects a different model, then this dictionary can contain any parameter from that different model. <br> **Datatype:** dictionary.
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| `n_estimators` | A common parameter among regressors which sets the number of boosted trees to fit <br> **Datatype:** integer.
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| `learning_rate` | A common parameter among regressors which sets the boosting learning rate. <br> **Datatype:** float.
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| `n_jobs`, `thread_count`, `task_type` | Different libraries use different parameter names to control the number of threads used for parallel processing or whether or not it is a `task_type` of `gpu` or `cpu`. <br> **Datatype:** float.
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| | **Extraneous parameters**
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| `keras` | If your model makes use of keras (typical of Tensorflow based prediction models), activate this flag so that the model save/loading follows keras standards. Default value `false` <br> **Datatype:** boolean.
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| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for `shift` by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. Default value, 2 <br> **Datatype:** integer.
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### Important FreqAI dataframe key patterns
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Here are the values the user can expect to include/use inside the typical strategy dataframe (`df[]`):
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| DataFrame Key | Description |
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|------------|-------------|
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| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target inside FreqAI (typically following the naming convention `&-s*`). These same dataframe columns names are fed back to the user as the predictions. For example, the user wishes to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`) by setting `df['&-s_close']`. FreqAI makes the predictions and gives them back to the user under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** depends on the output of the model.
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| `df['&*_std/mean']` | The standard deviation and mean values of the user defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand rarity of prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` to evaluate how often a particular prediction was observed during training (or historically with `fit_live_predictions_candles`)<br> **Datatype:** float.
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| `df['do_predict']` | An indication of an outlier, this return value is integer between -1 and 2 which lets the user understand if the prediction is trustworthy or not. `do_predict==1` means the prediction is trustworthy. If the [Dissimilarity Index](#removing-outliers-with-the-dissimilarity-index) is above the user defined threshold, it will subtract 1 from `do_predict`. If `use_SVM_to_remove_outliers()` is active, then the Support Vector Machine (SVM) may also detect outliers in training and prediction data. In this case, the SVM will also subtract one from `do_predict`. A particular case is when `do_predict == 2`, it means that the model has expired due to `expired_hours`. <br> **Datatype:** integer between -1 and 2.
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| `df['DI_values']` | The raw Dissimilarity Index values to give the user a sense of confidence in the prediction. Lower DI means the data point is closer to the trained parameter space. <br> **Datatype:** float.
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| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature inside FreqAI. For example, the user can include the rsi in the training feature set (similar to `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](#building-the-feature-set). <br>**Note**: since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table.) these features are removed from the dataframe upon return from FreqAI. If the user wishes to keep a particular type of feature for plotting purposes, you can prepend it with `%%`. <br> **Datatype:** depends on the output of the model.
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### Example config file
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The user interface is isolated to the typical config file. A typical FreqAI config setup could include:
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```json
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"freqai": {
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"startup_candles": 10000,
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"purge_old_models": true,
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"train_period_days": 30,
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"backtest_period_days": 7,
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"identifier" : "unique-id",
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"feature_parameters" : {
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"include_timeframes": ["5m","15m","4h"],
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"include_corr_pairlist": [
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"ETH/USD",
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"LINK/USD",
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"BNB/USD"
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],
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"label_period_candles": 24,
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"include_shifted_candles": 2,
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"weight_factor": 0,
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"indicator_max_period_candles": 20,
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"indicator_periods_candles": [10, 20]
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},
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"data_split_parameters" : {
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||||
"test_size": 0.25,
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"random_state": 42
|
||||
},
|
||||
"model_training_parameters" : {
|
||||
"n_estimators": 100,
|
||||
"random_state": 42,
|
||||
"learning_rate": 0.02,
|
||||
"task_type": "CPU",
|
||||
},
|
||||
}
|
||||
```
|
||||
|
||||
### Feature engineering
|
||||
|
||||
Features are added by the user inside the `populate_any_indicators()` method of the strategy
|
||||
by prepending indicators with `%` and labels are added by prepending `&`.
|
||||
There are some important components/structures that the user *must* include when building their feature set.
|
||||
Another structure to consider is the location of the labels at the bottom of the example function (below `if set_generalized_indicators:`).
|
||||
This is where the user will add single features and labels to their feature set to avoid duplication from
|
||||
various configuration parameters which multiply the feature set such as `include_timeframes`.
|
||||
|
||||
```python
|
||||
def populate_any_indicators(
|
||||
self, pair, df, tf, informative=None, set_generalized_indicators=False
|
||||
):
|
||||
"""
|
||||
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 `'%-' + coin `
|
||||
(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
|
||||
:param coin: the name of the coin which will modify the feature names.
|
||||
"""
|
||||
|
||||
coint = pair.split('/')[0]
|
||||
|
||||
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"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
|
||||
|
||||
bollinger = qtpylib.bollinger_bands(
|
||||
qtpylib.typical_price(informative), window=t, stds=2.2
|
||||
)
|
||||
informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
|
||||
informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
|
||||
informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]
|
||||
|
||||
informative[f"%-{coin}bb_width-period_{t}"] = (
|
||||
informative[f"{coin}bb_upperband-period_{t}"]
|
||||
- informative[f"{coin}bb_lowerband-period_{t}"]
|
||||
) / informative[f"{coin}bb_middleband-period_{t}"]
|
||||
informative[f"%-{coin}close-bb_lower-period_{t}"] = (
|
||||
informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"]
|
||||
)
|
||||
|
||||
informative[f"%-{coin}relative_volume-period_{t}"] = (
|
||||
informative["volume"] / informative["volume"].rolling(t).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)
|
||||
|
||||
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
|
||||
|
||||
# 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
|
||||
)
|
||||
|
||||
return df
|
||||
```
|
||||
|
||||
The user of the present example does not wish to pass the `bb_lowerband` as a feature to the model,
|
||||
and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the
|
||||
model for training/prediction and has therefore prepended it with `%`.
|
||||
|
||||
The `include_timeframes` from the example config above are the timeframes (`tf`) of each call to `populate_any_indicators()`
|
||||
included metric for inclusion in the feature set. In the present 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.
|
||||
|
||||
In addition, the user can ask for each of these features to be included from
|
||||
informative pairs using the `include_corr_pairlist`. This means that the present feature
|
||||
set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of
|
||||
`ETH/USD`, `LINK/USD`, and `BNB/USD`.
|
||||
|
||||
`include_shifted_candles` is another user controlled parameter which indicates the number of previous
|
||||
candles to include in the present feature set. In other words, `include_shifted_candles: 2`, tells
|
||||
FreqAI to include the the past 2 candles for each of the features included in the dataset.
|
||||
|
||||
In total, the number of features the present user 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`
|
||||
$3 * 3 * 3 * 2 * 2 = 108$.
|
||||
|
||||
!!! Note
|
||||
Features **must** be defined in `populate_any_indicators()`. Making features in `populate_indicators()`
|
||||
will fail in live/dry mode. If the user wishes to add generalized features that are not associated with
|
||||
a specific pair or timeframe, they should use the following structure inside `populate_any_indicators()`
|
||||
(as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`:
|
||||
|
||||
```python
|
||||
def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin="", 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()`)
|
||||
|
||||
### Deciding the sliding training window and backtesting duration
|
||||
|
||||
Users define the backtesting timerange with the typical `--timerange` parameter in the user
|
||||
configuration file. `train_period_days` is the duration of the sliding training window, while
|
||||
`backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be
|
||||
a float to indicate sub daily retraining in live/dry mode). In the present example,
|
||||
the user is asking FreqAI to use a training period of 30 days and backtest the subsequent 7 days.
|
||||
This means that if the user sets `--timerange 20210501-20210701`,
|
||||
FreqAI will train 8 separate models (because the full range comprises 8 weeks),
|
||||
and then backtest the subsequent week associated with each of the 8 training
|
||||
data set timerange months. Users can think of this as a "sliding window" which
|
||||
emulates FreqAI retraining itself once per week in live using the previous
|
||||
month of data.
|
||||
|
||||
In live, the required training data is automatically computed and downloaded. However, in backtesting
|
||||
the user must manually enter the required number of `startup_candles` in the config. This value
|
||||
is used to increase the available data to FreqAI and should be sufficient to enable all indicators
|
||||
to be NaN free at the beginning of the first training timerange. This boils down to identifying the
|
||||
highest timeframe (`4h` in present example) and the longest indicator period (25 in present example)
|
||||
and adding this to the `train_period_days`. The units need to be in the base candle time frame:
|
||||
|
||||
`startup_candles` = ( 4 hours * 25 max period * 60 minutes/hour + 30 day train_period_days * 1440 minutes per day ) / 5 min (base time frame) = 1488.
|
||||
|
||||
!!! Note
|
||||
In dry/live, this is all precomputed and handled automatically. Thus, `startup_candle` has no influence on dry/live.
|
||||
|
||||
!!! Note
|
||||
Although fractional `backtest_period_days` is allowed, the user should be ware that the `--timerange` is divided by this value to determine the number of models that FreqAI will need to train in order to backtest the full range. For example, if the user wants to set a `--timerange` of 10 days, and asks for a `backtest_period_days` of 0.1, FreqAI will need to train 100 models per pair to complete the full backtest. This is why it is physically impossible to truly backtest FreqAI adaptive training. The best way to fully test a model is to run it dry and let it constantly train. In this case, backtesting would take the exact same amount of time as a dry run.
|
||||
|
||||
## Running FreqAI
|
||||
|
||||
### Backtesting
|
||||
|
||||
The FreqAI backtesting module can be executed with the following command:
|
||||
|
||||
```bash
|
||||
freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701
|
||||
```
|
||||
|
||||
Backtesting mode requires the user to have the data pre-downloaded (unlike dry/live, where FreqAI automatically downloads the necessary data). The user should be careful to consider that the range of the downloaded data is more than the backtesting range. This is because FreqAI needs data prior to the desired backtesting range in order to train a model to be ready to make predictions on the first candle of the user set backtesting range. More details on how to calculate the data download timerange can be found [here](#deciding-the-sliding-training-window-and-backtesting-duration).
|
||||
|
||||
If this command has never been executed with the existing config file, then it will train a new model
|
||||
for each pair, for each backtesting window within the bigger `--timerange`.
|
||||
|
||||
!!! Note "Model reuse"
|
||||
Once the training is completed, the user can execute this again with the same config file and
|
||||
FreqAI will find the trained models and load them instead of spending time training. This is useful
|
||||
if the user wants to tweak (or even hyperopt) buy and sell criteria inside the strategy. IF the user
|
||||
*wants* to retrain a new model with the same config file, then he/she should simply change the `identifier`.
|
||||
This way, the user can return to using any model they wish by simply changing the `identifier`.
|
||||
|
||||
---
|
||||
|
||||
### Building a freqai strategy
|
||||
|
||||
The FreqAI strategy requires the user to include the following lines of code in the strategy:
|
||||
|
||||
```python
|
||||
|
||||
def informative_pairs(self):
|
||||
whitelist_pairs = self.dp.current_whitelist()
|
||||
corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
|
||||
informative_pairs = []
|
||||
for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
|
||||
for pair in whitelist_pairs:
|
||||
informative_pairs.append((pair, tf))
|
||||
for pair in corr_pairs:
|
||||
if pair in whitelist_pairs:
|
||||
continue # avoid duplication
|
||||
informative_pairs.append((pair, tf))
|
||||
return informative_pairs
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
# All indicators must be populated by populate_any_indicators() for live functionality
|
||||
# to work correctly.
|
||||
|
||||
# the model will return all labels created by user in `populate_any_indicators`
|
||||
# (& 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.
|
||||
|
||||
dataframe = self.freqai.start(dataframe, metadata, self)
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_any_indicators(
|
||||
self, pair, df, tf, informative=None, set_generalized_indicators=False
|
||||
):
|
||||
"""
|
||||
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 `'%-' + coin `
|
||||
(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
|
||||
:param coin: the name of the coin which will modify the feature names.
|
||||
"""
|
||||
|
||||
coin = pair.split('/')[0]
|
||||
|
||||
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"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
|
||||
|
||||
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:
|
||||
|
||||
# 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
|
||||
)
|
||||
|
||||
return df
|
||||
|
||||
|
||||
```
|
||||
|
||||
Notice how the `populate_any_indicators()` is where the user adds their own features and labels ([more information](#feature-engineering)). See a full example at `templates/FreqaiExampleStrategy.py`.
|
||||
|
||||
### Setting classifier targets
|
||||
|
||||
FreqAI includes a the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. Typically, the user would set the targets using strings:
|
||||
|
||||
```python
|
||||
df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
|
||||
```
|
||||
|
||||
### Running the model live
|
||||
|
||||
FreqAI can be run dry/live using the following command
|
||||
|
||||
```bash
|
||||
freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor
|
||||
```
|
||||
|
||||
By default, FreqAI will not find any existing models and will start by training a new one
|
||||
given the user configuration settings. Following training, it will use that model to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the pairs to try and keep all models equally "young." FreqAI will always use the newest trained model to make predictions on incoming live data. If users do not want FreqAI to retrain new models as often as possible, they can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before retraining a new model. Additionally, users can set `expired_hours` to tell FreqAI to avoid making predictions on models aged over this number of hours.
|
||||
|
||||
If the user wishes to start dry/live from a backtested saved model, the user only needs to reuse
|
||||
the same `identifier` parameter
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"identifier": "example",
|
||||
"live_retrain_hours": 1
|
||||
}
|
||||
```
|
||||
|
||||
In this case, although FreqAI will initiate with a
|
||||
pre-trained model, it will still check to see how much time has elapsed since the model was trained,
|
||||
and if a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will self retrain.
|
||||
|
||||
## Data analysis techniques
|
||||
|
||||
### Controlling the model learning process
|
||||
|
||||
Model training parameters are unique to the ML library used by the user. FreqAI allows users to set any parameter for any library using the `model_training_parameters` dictionary in the user configuration file. The example configuration files show some of the example parameters associated with `Catboost` and `LightGBM`, but users can add any parameters available in those libraries.
|
||||
|
||||
Data split parameters are defined in `data_split_parameters` which can be any parameters associated with `Sklearn`'s `train_test_split()` function. FreqAI includes some additional parameters such `weight_factor` which allows the user to weight more recent data more strongly
|
||||
than past data via an exponential function:
|
||||
|
||||
$$ W_i = \exp(\frac{-i}{\alpha*n}) $$
|
||||
|
||||
where $W_i$ is the weight of data point $i$ in a total set of $n$ data points.
|
||||
|
||||
|
||||
|
||||
`train_test_split()` has a parameters called `shuffle`, which users also have access to in FreqAI, that allows them to keep the data unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data.
|
||||
|
||||
Finally, `label_period_candles` defines the offset used for the `labels`. In the present example,
|
||||
the user is asking for `labels` that are 24 candles in the future.
|
||||
|
||||
### Removing outliers with the Dissimilarity Index
|
||||
|
||||
The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each
|
||||
prediction by the model. To do so, FreqAI measures the distance between each training
|
||||
data point and all other training data points:
|
||||
|
||||
$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$
|
||||
|
||||
where $d_{ab}$ is the distance between the normalized points $a$ and $b$. $p$
|
||||
is the number of features i.e. the length of the vector $X$.
|
||||
The characteristic distance, $\overline{d}$ for a set of training data points is simply the mean
|
||||
of the average distances:
|
||||
|
||||
$$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$
|
||||
|
||||
$\overline{d}$ quantifies the spread of the training data, which is compared to
|
||||
the distance between the new prediction feature vectors, $X_k$ and all the training
|
||||
data:
|
||||
|
||||
$$ d_k = \arg \min d_{k,i} $$
|
||||
|
||||
which enables the estimation of a Dissimilarity Index:
|
||||
|
||||
$$ DI_k = d_k/\overline{d} $$
|
||||
|
||||
Equity and crypto markets suffer from a high level of non-patterned noise in the
|
||||
form of outlier data points. The dissimilarity index allows predictions which
|
||||
are outliers and not existent in the model feature space, to be thrown out due
|
||||
to low levels of certainty. Activating the Dissimilarity Index can be achieved with:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"feature_parameters" : {
|
||||
"DI_threshold": 1
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
The user can tweak the DI with `DI_threshold` to increase or decrease the extrapolation of the trained model.
|
||||
|
||||
### Reducing data dimensionality with Principal Component Analysis
|
||||
|
||||
Users can reduce the dimensionality of their features by activating the `principal_component_analysis`:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"feature_parameters" : {
|
||||
"principal_component_analysis": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Which will perform PCA on the features and reduce the dimensionality of the data so that the explained
|
||||
variance of the data set is >= 0.999.
|
||||
|
||||
### Removing outliers using a Support Vector Machine (SVM)
|
||||
|
||||
The user can tell FreqAI to remove outlier data points from the training/test data sets by setting:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"feature_parameters" : {
|
||||
"use_SVM_to_remove_outliers": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
FreqAI will train an SVM on the training data (or components if the user activated
|
||||
`principal_component_analysis`) and remove any data point that it deems to be sitting beyond the feature space.
|
||||
|
||||
### Clustering the training data and removing outliers with DBSCAN
|
||||
|
||||
The user can configure FreqAI to use DBSCAN to cluster training data and remove outliers from the training data set. The user activates `use_DBSCAN_to_remove_outliers` to cluster training data for identification of outliers. Also used to detect incoming outliers for prediction data points.
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"feature_parameters" : {
|
||||
"use_DBSCAN_to_remove_outliers": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Stratifying the data
|
||||
|
||||
The user can stratify the training/testing data using:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"feature_parameters" : {
|
||||
"stratify_training_data": 3
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
which will split the data chronologically so that every Xth data points is a testing data point. In the
|
||||
present example, the user is asking for every third data point in the dataframe to be used for
|
||||
testing, the other points are used for training.
|
||||
|
||||
## Setting up a follower
|
||||
|
||||
The user can define:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"follow_mode": true,
|
||||
"identifier": "example"
|
||||
}
|
||||
```
|
||||
|
||||
to indicate to the bot that it should not train models, but instead should look for models trained
|
||||
by a leader with the same `identifier`. In this example, the user has a leader bot with the
|
||||
`identifier: "example"` already running or launching simultaneously as the present follower.
|
||||
The follower will load models created by the leader and inference them to obtain predictions.
|
||||
|
||||
## Purging old model data
|
||||
|
||||
FreqAI stores new model files each time it retrains. These files become obsolete as new models
|
||||
are trained and FreqAI adapts to the new market conditions. Users planning to leave FreqAI running
|
||||
for extended periods of time with high frequency retraining should set `purge_old_models` in their
|
||||
config:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"purge_old_models": true,
|
||||
}
|
||||
```
|
||||
|
||||
which will automatically purge all models older than the two most recently trained ones.
|
||||
|
||||
## Defining model expirations
|
||||
|
||||
During dry/live, FreqAI trains each pair sequentially (on separate threads/GPU from the main
|
||||
Freqtrade bot). This means there is always an age discrepancy between models. If a user is training
|
||||
on 50 pairs, and each pair requires 5 minutes to train, the oldest model will be over 4 hours old.
|
||||
This may be undesirable if the characteristic time scale (read trade duration target) for a strategy
|
||||
is much less than 4 hours. The user can decide to only make trade entries if the model is less than
|
||||
a certain number of hours in age by setting the `expiration_hours` in the config file:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"expiration_hours": 0.5,
|
||||
}
|
||||
```
|
||||
|
||||
In the present example, the user will only allow predictions on models that are less than 1/2 hours
|
||||
old.
|
||||
|
||||
## Choosing the calculation of the `target_roi`
|
||||
|
||||
As shown in `templates/FreqaiExampleStrategy.py`, the `target_roi` is based on two metrics computed
|
||||
by FreqAI: `label_mean` and `label_std`. These are the statistics associated with the labels used
|
||||
*during the most recent training*.
|
||||
This allows the model to know what magnitude of a target to be expecting since it is directly stemming from the training data.
|
||||
By default, FreqAI computes this based on training data and it assumes the labels are Gaussian distributed.
|
||||
These are big assumptions that the user should consider when creating their labels. If the user wants to consider the population
|
||||
of *historical predictions* for creating the dynamic target instead of the trained labels, the user
|
||||
can do so by setting `fit_live_prediction_candles` to the number of historical prediction candles
|
||||
the user wishes to use to generate target statistics.
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"fit_live_prediction_candles": 300,
|
||||
}
|
||||
```
|
||||
|
||||
If the user sets this value, FreqAI will initially use the predictions from the training data set
|
||||
and then subsequently begin introducing real prediction data as it is generated. FreqAI will save
|
||||
this historical data to be reloaded if the user stops and restarts with the same `identifier`.
|
||||
|
||||
## Extra returns per train
|
||||
|
||||
Users may find that there are some important metrics that they'd like to return to the strategy at the end of each retrain.
|
||||
Users can include these metrics by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside their custom prediction
|
||||
model class. FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the return dataframe to the strategy.
|
||||
The user can then use the value in the strategy with `dataframe['my_new_value']`. An example of how this is already used in FreqAI is
|
||||
the `&*_mean` and `&*_std` values, which indicate the mean and standard deviation of that particular label during the most recent training.
|
||||
Another example is shown below if the user wants to use live metrics from the trade database.
|
||||
|
||||
The user needs to set the standard dictionary in the config so FreqAI can return proper dataframe shapes:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"extra_returns_per_train": {"total_profit": 4}
|
||||
}
|
||||
```
|
||||
|
||||
These values will likely be overridden by the user prediction model, but in the case where the user model has yet to set them, or needs
|
||||
a default initial value - this is the value that will be returned.
|
||||
|
||||
## Building an IFreqaiModel
|
||||
|
||||
FreqAI has multiple example prediction model based libraries such as `Catboost` regression (`freqai/prediction_models/CatboostRegressor.py`) and `LightGBM` regression.
|
||||
However, users can customize and create their own prediction models using the `IFreqaiModel` class.
|
||||
Users are encouraged to inherit `train()` and `predict()` to let them customize various aspects of their training procedures.
|
||||
|
||||
## Additional information
|
||||
|
||||
### Common pitfalls
|
||||
|
||||
FreqAI cannot be combined with `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically).
|
||||
This is for performance reasons - FreqAI relies on making quick predictions/retrains. To do this effectively,
|
||||
it needs to download all the training data at the beginning of a dry/live instance. FreqAI stores and appends
|
||||
new candles automatically for future retrains. But this means that if new pairs arrive later in the dry run due
|
||||
to a volume pairlist, it will not have the data ready. FreqAI does work, however, with the `ShufflePairlist`.
|
||||
|
||||
### Feature normalization
|
||||
|
||||
The feature set created by the user is automatically normalized to the training data only.
|
||||
This includes all test data and unseen prediction data (dry/live/backtest).
|
||||
|
||||
### File structure
|
||||
|
||||
`user_data_dir/models/` contains all the data associated with the trainings and backtests.
|
||||
This file structure is heavily controlled and read by the `FreqaiDataKitchen()`
|
||||
and should therefore not be modified.
|
||||
|
||||
## Credits
|
||||
|
||||
FreqAI was developed by a group of individuals who all contributed specific skillsets to the project.
|
||||
|
||||
Conception and software development:
|
||||
Robert Caulk @robcaulk
|
||||
|
||||
Theoretical brainstorming:
|
||||
Elin Törnquist @thorntwig
|
||||
|
||||
Code review, software architecture brainstorming:
|
||||
@xmatthias
|
||||
|
||||
Beta testing and bug reporting:
|
||||
@bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm
|
||||
Juha Nykänen @suikula, Wagner Costa @wagnercosta
|
||||
@ -12,7 +12,8 @@ from freqtrade.constants import DEFAULT_CONFIG
|
||||
|
||||
ARGS_COMMON = ["verbosity", "logfile", "version", "config", "datadir", "user_data_dir"]
|
||||
|
||||
ARGS_STRATEGY = ["strategy", "strategy_path", "recursive_strategy_search"]
|
||||
ARGS_STRATEGY = ["strategy", "strategy_path", "recursive_strategy_search", "freqaimodel",
|
||||
"freqaimodel_path"]
|
||||
|
||||
ARGS_TRADE = ["db_url", "sd_notify", "dry_run", "dry_run_wallet", "fee"]
|
||||
|
||||
|
||||
@ -647,4 +647,14 @@ AVAILABLE_CLI_OPTIONS = {
|
||||
nargs='+',
|
||||
default=[],
|
||||
),
|
||||
"freqaimodel": Arg(
|
||||
'--freqaimodel',
|
||||
help='Specify a custom freqaimodels.',
|
||||
metavar='NAME',
|
||||
),
|
||||
"freqaimodel_path": Arg(
|
||||
'--freqaimodel-path',
|
||||
help='Specify additional lookup path for freqaimodels.',
|
||||
metavar='PATH',
|
||||
),
|
||||
}
|
||||
|
||||
@ -12,7 +12,7 @@ from freqtrade.enums import CandleType, RunMode, TradingMode
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.exchange import timeframe_to_minutes
|
||||
from freqtrade.exchange.exchange import market_is_active
|
||||
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
|
||||
from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist, expand_pairlist
|
||||
from freqtrade.resolvers import ExchangeResolver
|
||||
|
||||
|
||||
@ -50,7 +50,8 @@ def start_download_data(args: Dict[str, Any]) -> None:
|
||||
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
|
||||
markets = [p for p, m in exchange.markets.items() if market_is_active(m)
|
||||
or config.get('include_inactive')]
|
||||
expanded_pairs = expand_pairlist(config['pairs'], markets)
|
||||
|
||||
expanded_pairs = dynamic_expand_pairlist(config, markets)
|
||||
|
||||
# Manual validations of relevant settings
|
||||
if not config['exchange'].get('skip_pair_validation', False):
|
||||
|
||||
@ -97,6 +97,8 @@ class Configuration:
|
||||
|
||||
self._process_analyze_options(config)
|
||||
|
||||
self._process_freqai_options(config)
|
||||
|
||||
# Check if the exchange set by the user is supported
|
||||
check_exchange(config, config.get('experimental', {}).get('block_bad_exchanges', True))
|
||||
|
||||
@ -461,6 +463,16 @@ class Configuration:
|
||||
|
||||
config.update({'runmode': self.runmode})
|
||||
|
||||
def _process_freqai_options(self, config: Dict[str, Any]) -> None:
|
||||
|
||||
self._args_to_config(config, argname='freqaimodel',
|
||||
logstring='Using freqaimodel class name: {}')
|
||||
|
||||
self._args_to_config(config, argname='freqaimodel_path',
|
||||
logstring='Using freqaimodel path: {}')
|
||||
|
||||
return
|
||||
|
||||
def _args_to_config(self, config: Dict[str, Any], argname: str,
|
||||
logstring: str, logfun: Optional[Callable] = None,
|
||||
deprecated_msg: Optional[str] = None) -> None:
|
||||
|
||||
@ -55,6 +55,7 @@ FTHYPT_FILEVERSION = 'fthypt_fileversion'
|
||||
USERPATH_HYPEROPTS = 'hyperopts'
|
||||
USERPATH_STRATEGIES = 'strategies'
|
||||
USERPATH_NOTEBOOKS = 'notebooks'
|
||||
USERPATH_FREQAIMODELS = 'freqaimodels'
|
||||
|
||||
TELEGRAM_SETTING_OPTIONS = ['on', 'off', 'silent']
|
||||
WEBHOOK_FORMAT_OPTIONS = ['form', 'json', 'raw']
|
||||
@ -240,6 +241,7 @@ CONF_SCHEMA = {
|
||||
},
|
||||
'exchange': {'$ref': '#/definitions/exchange'},
|
||||
'edge': {'$ref': '#/definitions/edge'},
|
||||
'freqai': {'$ref': '#/definitions/freqai'},
|
||||
'experimental': {
|
||||
'type': 'object',
|
||||
'properties': {
|
||||
@ -480,7 +482,60 @@ CONF_SCHEMA = {
|
||||
'remove_pumps': {'type': 'boolean'}
|
||||
},
|
||||
'required': ['process_throttle_secs', 'allowed_risk']
|
||||
}
|
||||
},
|
||||
"freqai": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"enabled": {"type": "boolean", "default": False},
|
||||
"keras": {"type": "boolean", "default": False},
|
||||
"conv_width": {"type": "integer", "default": 2},
|
||||
"train_period_days": {"type": "integer", "default": 0},
|
||||
"backtest_period_days": {"type": "number", "default": 7},
|
||||
"identifier": {"type": "string", "default": "example"},
|
||||
"feature_parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"include_corr_pairlist": {"type": "array"},
|
||||
"include_timeframes": {"type": "array"},
|
||||
"label_period_candles": {"type": "integer"},
|
||||
"include_shifted_candles": {"type": "integer", "default": 0},
|
||||
"DI_threshold": {"type": "number", "default": 0},
|
||||
"weight_factor": {"type": "number", "default": 0},
|
||||
"principal_component_analysis": {"type": "boolean", "default": False},
|
||||
"use_SVM_to_remove_outliers": {"type": "boolean", "default": False},
|
||||
"svm_params": {"type": "object",
|
||||
"properties": {
|
||||
"shuffle": {"type": "boolean", "default": False},
|
||||
"nu": {"type": "number", "default": 0.1}
|
||||
},
|
||||
}
|
||||
},
|
||||
"required": ["include_timeframes", "include_corr_pairlist", ]
|
||||
},
|
||||
"data_split_parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"test_size": {"type": "number"},
|
||||
"random_state": {"type": "integer"},
|
||||
},
|
||||
},
|
||||
"model_training_parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"n_estimators": {"type": "integer", "default": 1000}
|
||||
},
|
||||
},
|
||||
},
|
||||
"required": [
|
||||
"enabled",
|
||||
"train_period_days",
|
||||
"backtest_period_days",
|
||||
"identifier",
|
||||
"feature_parameters",
|
||||
"data_split_parameters",
|
||||
"model_training_parameters"
|
||||
]
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
0
freqtrade/freqai/__init__.py
Normal file
0
freqtrade/freqai/__init__.py
Normal file
609
freqtrade/freqai/data_drawer.py
Normal file
609
freqtrade/freqai/data_drawer.py
Normal file
@ -0,0 +1,609 @@
|
||||
import collections
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
import shutil
|
||||
import threading
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, Tuple, TypedDict
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import rapidjson
|
||||
from joblib import dump, load
|
||||
from joblib.externals import cloudpickle
|
||||
from numpy.typing import NDArray
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade.configuration import TimeRange
|
||||
from freqtrade.data.history import load_pair_history
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from freqtrade.strategy.interface import IStrategy
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class pair_info(TypedDict):
|
||||
model_filename: str
|
||||
first: bool
|
||||
trained_timestamp: int
|
||||
priority: int
|
||||
data_path: str
|
||||
extras: dict
|
||||
|
||||
|
||||
class FreqaiDataDrawer:
|
||||
"""
|
||||
Class aimed at holding all pair models/info in memory for better inferencing/retrainig/saving
|
||||
/loading to/from disk.
|
||||
This object remains persistent throughout live/dry.
|
||||
|
||||
Record of contribution:
|
||||
FreqAI was developed by a group of individuals who all contributed specific skillsets to the
|
||||
project.
|
||||
|
||||
Conception and software development:
|
||||
Robert Caulk @robcaulk
|
||||
|
||||
Theoretical brainstorming:
|
||||
Elin Törnquist @th0rntwig
|
||||
|
||||
Code review, software architecture brainstorming:
|
||||
@xmatthias
|
||||
|
||||
Beta testing and bug reporting:
|
||||
@bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm
|
||||
Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
|
||||
"""
|
||||
|
||||
def __init__(self, full_path: Path, config: dict, follow_mode: bool = False):
|
||||
|
||||
self.config = config
|
||||
self.freqai_info = config.get("freqai", {})
|
||||
# dictionary holding all pair metadata necessary to load in from disk
|
||||
self.pair_dict: Dict[str, pair_info] = {}
|
||||
# dictionary holding all actively inferenced models in memory given a model filename
|
||||
self.model_dictionary: Dict[str, Any] = {}
|
||||
self.model_return_values: Dict[str, DataFrame] = {}
|
||||
self.historic_data: Dict[str, Dict[str, DataFrame]] = {}
|
||||
self.historic_predictions: Dict[str, DataFrame] = {}
|
||||
self.follower_dict: Dict[str, pair_info] = {}
|
||||
self.full_path = full_path
|
||||
self.follower_name: str = self.config.get("bot_name", "follower1")
|
||||
self.follower_dict_path = Path(
|
||||
self.full_path / f"follower_dictionary-{self.follower_name}.json"
|
||||
)
|
||||
self.historic_predictions_path = Path(self.full_path / "historic_predictions.pkl")
|
||||
self.pair_dictionary_path = Path(self.full_path / "pair_dictionary.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.training_queue: Dict[str, int] = {}
|
||||
self.history_lock = threading.Lock()
|
||||
self.save_lock = threading.Lock()
|
||||
self.pair_dict_lock = threading.Lock()
|
||||
self.old_DBSCAN_eps: Dict[str, float] = {}
|
||||
self.empty_pair_dict: pair_info = {
|
||||
"model_filename": "", "trained_timestamp": 0,
|
||||
"priority": 1, "first": True, "data_path": "", "extras": {}}
|
||||
|
||||
def load_drawer_from_disk(self):
|
||||
"""
|
||||
Locate and load a previously saved data drawer full of all pair model metadata in
|
||||
present model folder.
|
||||
:return: bool - whether or not the drawer was located
|
||||
"""
|
||||
exists = self.pair_dictionary_path.is_file()
|
||||
if exists:
|
||||
with open(self.pair_dictionary_path, "r") as fp:
|
||||
self.pair_dict = json.load(fp)
|
||||
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"
|
||||
)
|
||||
|
||||
return exists
|
||||
|
||||
def load_historic_predictions_from_disk(self):
|
||||
"""
|
||||
Locate and load a previously saved historic predictions.
|
||||
:return: bool - whether or not the drawer was located
|
||||
"""
|
||||
exists = self.historic_predictions_path.is_file()
|
||||
if exists:
|
||||
with open(self.historic_predictions_path, "rb") as fp:
|
||||
self.historic_predictions = cloudpickle.load(fp)
|
||||
logger.info(
|
||||
f"Found existing historic predictions at {self.full_path}, but beware "
|
||||
"that statistics may be inaccurate if the bot has been offline for "
|
||||
"an extended period of time."
|
||||
)
|
||||
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"
|
||||
)
|
||||
|
||||
return exists
|
||||
|
||||
def save_historic_predictions_to_disk(self):
|
||||
"""
|
||||
Save data drawer full of all pair model metadata in present model folder.
|
||||
"""
|
||||
with open(self.historic_predictions_path, "wb") as fp:
|
||||
cloudpickle.dump(self.historic_predictions, fp, protocol=cloudpickle.DEFAULT_PROTOCOL)
|
||||
|
||||
def save_drawer_to_disk(self):
|
||||
"""
|
||||
Save data drawer full of all pair model metadata in present model folder.
|
||||
"""
|
||||
with self.save_lock:
|
||||
with open(self.pair_dictionary_path, 'w') as fp:
|
||||
rapidjson.dump(self.pair_dict, fp, default=self.np_encoder,
|
||||
number_mode=rapidjson.NM_NATIVE)
|
||||
|
||||
def save_follower_dict_to_disk(self):
|
||||
"""
|
||||
Save follower dictionary to disk (used by strategy for persistent prediction targets)
|
||||
"""
|
||||
with open(self.follower_dict_path, "w") as fp:
|
||||
rapidjson.dump(self.follower_dict, 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()
|
||||
|
||||
def get_pair_dict_info(self, pair: str) -> Tuple[str, int, bool]:
|
||||
"""
|
||||
Locate and load existing model metadata from persistent storage. If not located,
|
||||
create a new one and append the current pair to it and prepare it for its first
|
||||
training
|
||||
:param pair: str: pair to lookup
|
||||
:return:
|
||||
model_filename: str = unique filename used for loading persistent objects from disk
|
||||
trained_timestamp: int = the last time the coin was trained
|
||||
return_null_array: bool = Follower could not find pair metadata
|
||||
"""
|
||||
|
||||
pair_dict = self.pair_dict.get(pair)
|
||||
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:
|
||||
self.pair_dict[pair] = self.empty_pair_dict.copy()
|
||||
model_filename = ""
|
||||
trained_timestamp = 0
|
||||
self.pair_dict[pair]["priority"] = len(self.pair_dict)
|
||||
|
||||
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:
|
||||
pair_in_dict = self.pair_dict.get(metadata["pair"])
|
||||
if pair_in_dict:
|
||||
return
|
||||
else:
|
||||
self.pair_dict[metadata["pair"]] = self.empty_pair_dict.copy()
|
||||
self.pair_dict[metadata["pair"]]["priority"] = len(self.pair_dict)
|
||||
|
||||
return
|
||||
|
||||
def pair_to_end_of_training_queue(self, pair: str) -> None:
|
||||
# march all pairs up in the queue
|
||||
with self.pair_dict_lock:
|
||||
for p in self.pair_dict:
|
||||
self.pair_dict[p]["priority"] -= 1
|
||||
# send pair to end of queue
|
||||
self.pair_dict[pair]["priority"] = len(self.pair_dict)
|
||||
|
||||
def set_initial_return_values(self, pair: str, pred_df: DataFrame) -> None:
|
||||
"""
|
||||
Set the initial return values to the historical predictions dataframe. This avoids needing
|
||||
to repredict on historical candles, and also stores historical predictions despite
|
||||
retrainings (so stored predictions are true predictions, not just inferencing on trained
|
||||
data)
|
||||
"""
|
||||
|
||||
hist_df = self.historic_predictions
|
||||
len_diff = len(hist_df[pair].index) - len(pred_df.index)
|
||||
if len_diff < 0:
|
||||
df_concat = pd.concat([pred_df.iloc[:abs(len_diff)], hist_df[pair]],
|
||||
ignore_index=True, keys=hist_df[pair].keys())
|
||||
else:
|
||||
df_concat = hist_df[pair].tail(len(pred_df.index)).reset_index(drop=True)
|
||||
df_concat = df_concat.fillna(0)
|
||||
self.model_return_values[pair] = df_concat
|
||||
|
||||
def append_model_predictions(self, pair: str, predictions: DataFrame,
|
||||
do_preds: NDArray[np.int_],
|
||||
dk: FreqaiDataKitchen, len_df: int) -> None:
|
||||
"""
|
||||
Append model predictions to historic predictions dataframe, then set the
|
||||
strategy return dataframe to the tail of the historic predictions. The length of
|
||||
the tail is equivalent to the length of the dataframe that entered FreqAI from
|
||||
the strategy originally. Doing this allows FreqUI to always display the correct
|
||||
historic predictions.
|
||||
"""
|
||||
|
||||
index = self.historic_predictions[pair].index[-1:]
|
||||
columns = self.historic_predictions[pair].columns
|
||||
|
||||
nan_df = pd.DataFrame(np.nan, index=index, columns=columns)
|
||||
self.historic_predictions[pair] = pd.concat(
|
||||
[self.historic_predictions[pair], nan_df], ignore_index=True, axis=0)
|
||||
df = self.historic_predictions[pair]
|
||||
|
||||
# model outputs and associated statistics
|
||||
for label in predictions.columns:
|
||||
df[label].iloc[-1] = predictions[label].iloc[-1]
|
||||
if df[label].dtype == object:
|
||||
continue
|
||||
df[f"{label}_mean"].iloc[-1] = dk.data["labels_mean"][label]
|
||||
df[f"{label}_std"].iloc[-1] = dk.data["labels_std"][label]
|
||||
|
||||
# outlier indicators
|
||||
df["do_predict"].iloc[-1] = do_preds[-1]
|
||||
if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0:
|
||||
df["DI_values"].iloc[-1] = dk.DI_values[-1]
|
||||
|
||||
# extra values the user added within custom prediction model
|
||||
if dk.data['extra_returns_per_train']:
|
||||
rets = dk.data['extra_returns_per_train']
|
||||
for return_str in rets:
|
||||
df[return_str].iloc[-1] = rets[return_str]
|
||||
|
||||
self.model_return_values[pair] = df.tail(len_df).reset_index(drop=True)
|
||||
|
||||
def attach_return_values_to_return_dataframe(
|
||||
self, pair: str, dataframe: DataFrame) -> DataFrame:
|
||||
"""
|
||||
Attach the return values to the strat dataframe
|
||||
:param dataframe: DataFrame = strategy dataframe
|
||||
:return: DataFrame = strat dataframe with return values attached
|
||||
"""
|
||||
df = self.model_return_values[pair]
|
||||
to_keep = [col for col in dataframe.columns if not col.startswith("&")]
|
||||
dataframe = pd.concat([dataframe[to_keep], df], axis=1)
|
||||
return dataframe
|
||||
|
||||
def return_null_values_to_strategy(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> None:
|
||||
"""
|
||||
Build 0 filled dataframe to return to strategy
|
||||
"""
|
||||
|
||||
dk.find_features(dataframe)
|
||||
|
||||
full_labels = dk.label_list + dk.unique_class_list
|
||||
|
||||
for label in full_labels:
|
||||
dataframe[label] = 0
|
||||
dataframe[f"{label}_mean"] = 0
|
||||
dataframe[f"{label}_std"] = 0
|
||||
|
||||
dataframe["do_predict"] = 0
|
||||
|
||||
if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0:
|
||||
dataframe["DI_values"] = 0
|
||||
|
||||
if dk.data['extra_returns_per_train']:
|
||||
rets = dk.data['extra_returns_per_train']
|
||||
for return_str in rets:
|
||||
dataframe[return_str] = 0
|
||||
|
||||
dk.return_dataframe = dataframe
|
||||
|
||||
def purge_old_models(self) -> None:
|
||||
|
||||
model_folders = [x for x in self.full_path.iterdir() if x.is_dir()]
|
||||
|
||||
pattern = re.compile(r"sub-train-(\w+)_(\d{10})")
|
||||
|
||||
delete_dict: Dict[str, Any] = {}
|
||||
|
||||
for dir in model_folders:
|
||||
result = pattern.match(str(dir.name))
|
||||
if result is None:
|
||||
break
|
||||
coin = result.group(1)
|
||||
timestamp = result.group(2)
|
||||
|
||||
if coin not in delete_dict:
|
||||
delete_dict[coin] = {}
|
||||
delete_dict[coin]["num_folders"] = 1
|
||||
delete_dict[coin]["timestamps"] = {int(timestamp): dir}
|
||||
else:
|
||||
delete_dict[coin]["num_folders"] += 1
|
||||
delete_dict[coin]["timestamps"][int(timestamp)] = dir
|
||||
|
||||
for coin in delete_dict:
|
||||
if delete_dict[coin]["num_folders"] > 2:
|
||||
sorted_dict = collections.OrderedDict(
|
||||
sorted(delete_dict[coin]["timestamps"].items())
|
||||
)
|
||||
num_delete = len(sorted_dict) - 2
|
||||
deleted = 0
|
||||
for k, v in sorted_dict.items():
|
||||
if deleted >= num_delete:
|
||||
break
|
||||
logger.info(f"Freqai purging old model file {v}")
|
||||
shutil.rmtree(v)
|
||||
deleted += 1
|
||||
|
||||
def update_follower_metadata(self):
|
||||
# follower needs to load from disk to get any changes made by leader to pair_dict
|
||||
self.load_drawer_from_disk()
|
||||
if self.config.get("freqai", {}).get("purge_old_models", False):
|
||||
self.purge_old_models()
|
||||
|
||||
# Functions pulled back from FreqaiDataKitchen because they relied on DataDrawer
|
||||
|
||||
def save_data(self, model: Any, coin: str, dk: FreqaiDataKitchen) -> None:
|
||||
"""
|
||||
Saves all data associated with a model for a single sub-train time range
|
||||
:params:
|
||||
:model: User trained model which can be reused for inferencing to generate
|
||||
predictions
|
||||
"""
|
||||
|
||||
if not dk.data_path.is_dir():
|
||||
dk.data_path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
save_path = Path(dk.data_path)
|
||||
|
||||
# Save the trained model
|
||||
if not dk.keras:
|
||||
dump(model, save_path / f"{dk.model_filename}_model.joblib")
|
||||
else:
|
||||
model.save(save_path / f"{dk.model_filename}_model.h5")
|
||||
|
||||
if dk.svm_model is not None:
|
||||
dump(dk.svm_model, save_path / f"{dk.model_filename}_svm_model.joblib")
|
||||
|
||||
dk.data["data_path"] = str(dk.data_path)
|
||||
dk.data["model_filename"] = str(dk.model_filename)
|
||||
dk.data["training_features_list"] = list(dk.data_dictionary["train_features"].columns)
|
||||
dk.data["label_list"] = dk.label_list
|
||||
# store the metadata
|
||||
with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp:
|
||||
rapidjson.dump(dk.data, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE)
|
||||
|
||||
# save the train data to file so we can check preds for area of applicability later
|
||||
dk.data_dictionary["train_features"].to_pickle(
|
||||
save_path / f"{dk.model_filename}_trained_df.pkl"
|
||||
)
|
||||
|
||||
dk.data_dictionary["train_dates"].to_pickle(
|
||||
save_path / f"{dk.model_filename}_trained_dates_df.pkl"
|
||||
)
|
||||
|
||||
if self.freqai_info["feature_parameters"].get("principal_component_analysis"):
|
||||
cloudpickle.dump(
|
||||
dk.pca, open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "wb")
|
||||
)
|
||||
|
||||
# if self.live:
|
||||
self.model_dictionary[dk.model_filename] = model
|
||||
self.pair_dict[coin]["model_filename"] = dk.model_filename
|
||||
self.pair_dict[coin]["data_path"] = str(dk.data_path)
|
||||
self.save_drawer_to_disk()
|
||||
|
||||
return
|
||||
|
||||
def load_data(self, coin: str, dk: FreqaiDataKitchen) -> Any:
|
||||
"""
|
||||
loads all data required to make a prediction on a sub-train time range
|
||||
:returns:
|
||||
:model: User trained model which can be inferenced for new predictions
|
||||
"""
|
||||
|
||||
if not self.pair_dict[coin]["model_filename"]:
|
||||
return None
|
||||
|
||||
if dk.live:
|
||||
dk.model_filename = self.pair_dict[coin]["model_filename"]
|
||||
dk.data_path = Path(self.pair_dict[coin]["data_path"])
|
||||
if self.freqai_info.get("follow_mode", False):
|
||||
# follower can be on a different system which is rsynced from the leader:
|
||||
dk.data_path = Path(
|
||||
self.config["user_data_dir"]
|
||||
/ "models"
|
||||
/ dk.data_path.parts[-2]
|
||||
/ dk.data_path.parts[-1]
|
||||
)
|
||||
|
||||
with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
|
||||
dk.data = json.load(fp)
|
||||
dk.training_features_list = dk.data["training_features_list"]
|
||||
dk.label_list = dk.data["label_list"]
|
||||
|
||||
dk.data_dictionary["train_features"] = pd.read_pickle(
|
||||
dk.data_path / f"{dk.model_filename}_trained_df.pkl"
|
||||
)
|
||||
|
||||
# try to access model in memory instead of loading object from disk to save time
|
||||
if dk.live and dk.model_filename in self.model_dictionary:
|
||||
model = self.model_dictionary[dk.model_filename]
|
||||
elif not dk.keras:
|
||||
model = load(dk.data_path / f"{dk.model_filename}_model.joblib")
|
||||
else:
|
||||
from tensorflow import keras
|
||||
|
||||
model = keras.models.load_model(dk.data_path / f"{dk.model_filename}_model.h5")
|
||||
|
||||
if Path(dk.data_path / f"{dk.model_filename}_svm_model.joblib").is_file():
|
||||
dk.svm_model = load(dk.data_path / f"{dk.model_filename}_svm_model.joblib")
|
||||
|
||||
if not model:
|
||||
raise OperationalException(
|
||||
f"Unable to load model, ensure model exists at " f"{dk.data_path} "
|
||||
)
|
||||
|
||||
if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
|
||||
dk.pca = cloudpickle.load(
|
||||
open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "rb")
|
||||
)
|
||||
|
||||
return model
|
||||
|
||||
def update_historic_data(self, strategy: IStrategy, dk: FreqaiDataKitchen) -> None:
|
||||
"""
|
||||
Append new candles to our stores historic data (in memory) so that
|
||||
we do not need to load candle history from disk and we dont need to
|
||||
pinging exchange multiple times for the same candle.
|
||||
:params:
|
||||
dataframe: DataFrame = strategy provided dataframe
|
||||
"""
|
||||
feat_params = self.freqai_info["feature_parameters"]
|
||||
with self.history_lock:
|
||||
history_data = self.historic_data
|
||||
|
||||
for pair in dk.all_pairs:
|
||||
for tf in feat_params.get("include_timeframes"):
|
||||
|
||||
# check if newest candle is already appended
|
||||
df_dp = strategy.dp.get_pair_dataframe(pair, tf)
|
||||
if len(df_dp.index) == 0:
|
||||
continue
|
||||
if str(history_data[pair][tf].iloc[-1]["date"]) == str(
|
||||
df_dp.iloc[-1:]["date"].iloc[-1]
|
||||
):
|
||||
continue
|
||||
|
||||
try:
|
||||
index = (
|
||||
df_dp.loc[
|
||||
df_dp["date"] == history_data[pair][tf].iloc[-1]["date"]
|
||||
].index[0]
|
||||
+ 1
|
||||
)
|
||||
except IndexError:
|
||||
logger.warning(
|
||||
f"Unable to update pair history for {pair}. "
|
||||
"If this does not resolve itself after 1 additional candle, "
|
||||
"please report the error to #freqai discord channel"
|
||||
)
|
||||
return
|
||||
|
||||
history_data[pair][tf] = pd.concat(
|
||||
[
|
||||
history_data[pair][tf],
|
||||
df_dp.iloc[index:],
|
||||
],
|
||||
ignore_index=True,
|
||||
axis=0,
|
||||
)
|
||||
|
||||
def load_all_pair_histories(self, timerange: TimeRange, dk: FreqaiDataKitchen) -> None:
|
||||
"""
|
||||
Load pair histories for all whitelist and corr_pairlist pairs.
|
||||
Only called once upon startup of bot.
|
||||
:params:
|
||||
timerange: TimeRange = full timerange required to populate all indicators
|
||||
for training according to user defined train_period_days
|
||||
"""
|
||||
history_data = self.historic_data
|
||||
|
||||
for pair in dk.all_pairs:
|
||||
if pair not in history_data:
|
||||
history_data[pair] = {}
|
||||
for tf in self.freqai_info["feature_parameters"].get("include_timeframes"):
|
||||
history_data[pair][tf] = load_pair_history(
|
||||
datadir=self.config["datadir"],
|
||||
timeframe=tf,
|
||||
pair=pair,
|
||||
timerange=timerange,
|
||||
data_format=self.config.get("dataformat_ohlcv", "json"),
|
||||
candle_type=self.config.get("trading_mode", "spot"),
|
||||
)
|
||||
|
||||
def get_base_and_corr_dataframes(
|
||||
self, timerange: TimeRange, pair: str, dk: FreqaiDataKitchen
|
||||
) -> Tuple[Dict[Any, Any], Dict[Any, Any]]:
|
||||
"""
|
||||
Searches through our historic_data in memory and returns the dataframes relevant
|
||||
to the present pair.
|
||||
:params:
|
||||
timerange: TimeRange = full timerange required to populate all indicators
|
||||
for training according to user defined train_period_days
|
||||
metadata: dict = strategy furnished pair metadata
|
||||
"""
|
||||
|
||||
with self.history_lock:
|
||||
corr_dataframes: Dict[Any, Any] = {}
|
||||
base_dataframes: Dict[Any, Any] = {}
|
||||
historic_data = self.historic_data
|
||||
pairs = self.freqai_info["feature_parameters"].get(
|
||||
"include_corr_pairlist", []
|
||||
)
|
||||
|
||||
for tf in self.freqai_info["feature_parameters"].get("include_timeframes"):
|
||||
base_dataframes[tf] = dk.slice_dataframe(timerange, historic_data[pair][tf])
|
||||
if pairs:
|
||||
for p in pairs:
|
||||
if pair in p:
|
||||
continue # dont repeat anything from whitelist
|
||||
if p not in corr_dataframes:
|
||||
corr_dataframes[p] = {}
|
||||
corr_dataframes[p][tf] = dk.slice_dataframe(
|
||||
timerange, historic_data[p][tf]
|
||||
)
|
||||
|
||||
return corr_dataframes, base_dataframes
|
||||
|
||||
# to be used if we want to send predictions directly to the follower instead of forcing
|
||||
# follower to load models and inference
|
||||
# def save_model_return_values_to_disk(self) -> None:
|
||||
# with open(self.full_path / str('model_return_values.json'), "w") as fp:
|
||||
# json.dump(self.model_return_values, fp, default=self.np_encoder)
|
||||
|
||||
# def load_model_return_values_from_disk(self, dk: FreqaiDataKitchen) -> FreqaiDataKitchen:
|
||||
# exists = Path(self.full_path / str('model_return_values.json')).resolve().exists()
|
||||
# if exists:
|
||||
# with open(self.full_path / str('model_return_values.json'), "r") as fp:
|
||||
# self.model_return_values = json.load(fp)
|
||||
# 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')
|
||||
|
||||
# return exists, dk
|
||||
1029
freqtrade/freqai/data_kitchen.py
Normal file
1029
freqtrade/freqai/data_kitchen.py
Normal file
File diff suppressed because it is too large
Load Diff
653
freqtrade/freqai/freqai_interface.py
Normal file
653
freqtrade/freqai/freqai_interface.py
Normal file
@ -0,0 +1,653 @@
|
||||
# import contextlib
|
||||
import datetime
|
||||
import logging
|
||||
import shutil
|
||||
import threading
|
||||
import time
|
||||
from abc import ABC, abstractmethod
|
||||
from pathlib import Path
|
||||
from threading import Lock
|
||||
from typing import Any, Dict, Tuple
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from numpy.typing import NDArray
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade.configuration import TimeRange
|
||||
from freqtrade.enums import RunMode
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.exchange import timeframe_to_seconds
|
||||
from freqtrade.freqai.data_drawer import FreqaiDataDrawer
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from freqtrade.strategy.interface import IStrategy
|
||||
|
||||
|
||||
pd.options.mode.chained_assignment = None
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def threaded(fn):
|
||||
def wrapper(*args, **kwargs):
|
||||
threading.Thread(target=fn, args=args, kwargs=kwargs).start()
|
||||
|
||||
return wrapper
|
||||
|
||||
|
||||
class IFreqaiModel(ABC):
|
||||
"""
|
||||
Class containing all tools for training and prediction in the strategy.
|
||||
Base*PredictionModels inherit from this class.
|
||||
|
||||
Record of contribution:
|
||||
FreqAI was developed by a group of individuals who all contributed specific skillsets to the
|
||||
project.
|
||||
|
||||
Conception and software development:
|
||||
Robert Caulk @robcaulk
|
||||
|
||||
Theoretical brainstorming:
|
||||
Elin Törnquist @th0rntwig
|
||||
|
||||
Code review, software architecture brainstorming:
|
||||
@xmatthias
|
||||
|
||||
Beta testing and bug reporting:
|
||||
@bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm
|
||||
Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
|
||||
"""
|
||||
|
||||
def __init__(self, config: Dict[str, Any]) -> None:
|
||||
|
||||
self.config = config
|
||||
self.assert_config(self.config)
|
||||
self.freqai_info: Dict[str, Any] = config["freqai"]
|
||||
self.data_split_parameters: Dict[str, Any] = config.get("freqai", {}).get(
|
||||
"data_split_parameters", {})
|
||||
self.model_training_parameters: Dict[str, Any] = config.get("freqai", {}).get(
|
||||
"model_training_parameters", {})
|
||||
self.feature_parameters = config.get("freqai", {}).get("feature_parameters")
|
||||
self.retrain = False
|
||||
self.first = True
|
||||
self.set_full_path()
|
||||
self.follow_mode: bool = self.freqai_info.get("follow_mode", False)
|
||||
self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
|
||||
self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
|
||||
self.scanning = False
|
||||
self.keras: bool = self.freqai_info.get("keras", False)
|
||||
if self.keras and self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
|
||||
self.freqai_info["feature_parameters"]["DI_threshold"] = 0
|
||||
logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
|
||||
self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
|
||||
self.pair_it = 0
|
||||
self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
|
||||
self.last_trade_database_summary: DataFrame = {}
|
||||
self.current_trade_database_summary: DataFrame = {}
|
||||
self.analysis_lock = Lock()
|
||||
self.inference_time: float = 0
|
||||
self.begin_time: float = 0
|
||||
self.base_tf_seconds = timeframe_to_seconds(self.config['timeframe'])
|
||||
|
||||
def assert_config(self, config: Dict[str, Any]) -> None:
|
||||
|
||||
if not config.get("freqai", {}):
|
||||
raise OperationalException("No freqai parameters found in configuration file.")
|
||||
|
||||
def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame:
|
||||
"""
|
||||
Entry point to the FreqaiModel from a specific pair, it will train a new model if
|
||||
necessary before making the prediction.
|
||||
|
||||
:param dataframe: Full dataframe coming from strategy - it contains entire
|
||||
backtesting timerange + additional historical data necessary to train
|
||||
the model.
|
||||
:param metadata: pair metadata coming from strategy.
|
||||
:param strategy: Strategy to train on
|
||||
"""
|
||||
|
||||
self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
|
||||
self.dd.set_pair_dict_info(metadata)
|
||||
|
||||
if self.live:
|
||||
self.inference_timer('start')
|
||||
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
|
||||
dk = self.start_live(dataframe, metadata, strategy, self.dk)
|
||||
|
||||
# For backtesting, each pair enters and then gets trained for each window along the
|
||||
# sliding window defined by "train_period_days" (training window) and "live_retrain_hours"
|
||||
# (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:
|
||||
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
|
||||
logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
|
||||
with self.analysis_lock:
|
||||
dataframe = self.dk.use_strategy_to_populate_indicators(
|
||||
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
|
||||
)
|
||||
dk = self.start_backtesting(dataframe, metadata, self.dk)
|
||||
|
||||
dataframe = dk.remove_features_from_df(dk.return_dataframe)
|
||||
del dk
|
||||
if self.live:
|
||||
self.inference_timer('stop')
|
||||
return dataframe
|
||||
|
||||
@threaded
|
||||
def start_scanning(self, strategy: IStrategy) -> None:
|
||||
"""
|
||||
Function designed to constantly scan pairs for retraining on a separate thread (intracandle)
|
||||
to improve model youth. This function is agnostic to data preparation/collection/storage,
|
||||
it simply trains on what ever data is available in the self.dd.
|
||||
:param strategy: IStrategy = The user defined strategy class
|
||||
"""
|
||||
while 1:
|
||||
time.sleep(1)
|
||||
for pair in self.config.get("exchange", {}).get("pair_whitelist"):
|
||||
|
||||
(_, trained_timestamp, _) = self.dd.get_pair_dict_info(pair)
|
||||
|
||||
if self.dd.pair_dict[pair]["priority"] != 1:
|
||||
continue
|
||||
dk = FreqaiDataKitchen(self.config, self.live, pair)
|
||||
dk.set_paths(pair, trained_timestamp)
|
||||
(
|
||||
retrain,
|
||||
new_trained_timerange,
|
||||
data_load_timerange,
|
||||
) = dk.check_if_new_training_required(trained_timestamp)
|
||||
dk.set_paths(pair, new_trained_timerange.stopts)
|
||||
|
||||
if retrain:
|
||||
self.train_model_in_series(
|
||||
new_trained_timerange, pair, strategy, dk, data_load_timerange
|
||||
)
|
||||
|
||||
self.dd.save_historic_predictions_to_disk()
|
||||
|
||||
def start_backtesting(
|
||||
self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
|
||||
) -> FreqaiDataKitchen:
|
||||
"""
|
||||
The main broad execution for backtesting. For backtesting, each pair enters and then gets
|
||||
trained for each window along the sliding window defined by "train_period_days"
|
||||
(training window) and "backtest_period_days" (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.
|
||||
:param dataframe: DataFrame = strategy passed dataframe
|
||||
:param metadata: Dict = pair metadata
|
||||
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
|
||||
:return:
|
||||
FreqaiDataKitchen = Data management/analysis tool associated to present pair only
|
||||
"""
|
||||
|
||||
self.pair_it += 1
|
||||
train_it = 0
|
||||
# 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):
|
||||
(_, _, _) = self.dd.get_pair_dict_info(metadata["pair"])
|
||||
train_it += 1
|
||||
total_trains = len(dk.backtesting_timeranges)
|
||||
self.training_timerange = tr_train
|
||||
dataframe_train = dk.slice_dataframe(tr_train, dataframe)
|
||||
dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
|
||||
|
||||
trained_timestamp = tr_train
|
||||
tr_train_startts_str = datetime.datetime.utcfromtimestamp(tr_train.startts).strftime(
|
||||
"%Y-%m-%d %H:%M:%S"
|
||||
)
|
||||
tr_train_stopts_str = datetime.datetime.utcfromtimestamp(tr_train.stopts).strftime(
|
||||
"%Y-%m-%d %H:%M:%S"
|
||||
)
|
||||
logger.info(
|
||||
f"Training {metadata['pair']}, {self.pair_it}/{self.total_pairs} pairs"
|
||||
f" from {tr_train_startts_str} to {tr_train_stopts_str}, {train_it}/{total_trains} "
|
||||
"trains"
|
||||
)
|
||||
|
||||
dk.data_path = Path(
|
||||
dk.full_path
|
||||
/
|
||||
f"sub-train-{metadata['pair'].split('/')[0]}_{int(trained_timestamp.stopts)}"
|
||||
)
|
||||
if not self.model_exists(
|
||||
metadata["pair"], dk, trained_timestamp=int(trained_timestamp.stopts)
|
||||
):
|
||||
dk.find_features(dataframe_train)
|
||||
self.model = self.train(dataframe_train, metadata["pair"], dk)
|
||||
self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = int(
|
||||
trained_timestamp.stopts)
|
||||
dk.set_new_model_names(metadata["pair"], trained_timestamp)
|
||||
self.dd.save_data(self.model, metadata["pair"], dk)
|
||||
else:
|
||||
self.model = self.dd.load_data(metadata["pair"], dk)
|
||||
|
||||
self.check_if_feature_list_matches_strategy(dataframe_train, dk)
|
||||
|
||||
pred_df, do_preds = self.predict(dataframe_backtest, dk)
|
||||
|
||||
dk.append_predictions(pred_df, do_preds)
|
||||
|
||||
dk.fill_predictions(dataframe)
|
||||
|
||||
return dk
|
||||
|
||||
def start_live(
|
||||
self, dataframe: DataFrame, metadata: dict, strategy: IStrategy, dk: FreqaiDataKitchen
|
||||
) -> FreqaiDataKitchen:
|
||||
"""
|
||||
The main broad execution for dry/live. This function will check if a retraining should be
|
||||
performed, and if so, retrain and reset the model.
|
||||
:param dataframe: DataFrame = strategy passed dataframe
|
||||
:param metadata: Dict = pair metadata
|
||||
:param strategy: IStrategy = currently employed strategy
|
||||
dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
|
||||
:returns:
|
||||
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"]}')
|
||||
|
||||
if not self.follow_mode:
|
||||
|
||||
(_, new_trained_timerange, data_load_timerange) = dk.check_if_new_training_required(
|
||||
trained_timestamp
|
||||
)
|
||||
dk.set_paths(metadata["pair"], new_trained_timerange.stopts)
|
||||
|
||||
# download candle history if it is not already in memory
|
||||
if not self.dd.historic_data:
|
||||
logger.info(
|
||||
"Downloading all training data for all pairs in whitelist and "
|
||||
"corr_pairlist, this may take a while if you do not have the "
|
||||
"data saved"
|
||||
)
|
||||
dk.download_all_data_for_training(data_load_timerange, strategy.dp)
|
||||
self.dd.load_all_pair_histories(data_load_timerange, dk)
|
||||
|
||||
if not self.scanning:
|
||||
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)
|
||||
|
||||
with self.analysis_lock:
|
||||
dataframe = self.dk.use_strategy_to_populate_indicators(
|
||||
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
|
||||
)
|
||||
|
||||
if not self.model:
|
||||
logger.warning(
|
||||
f"No model ready for {metadata['pair']}, returning null values to strategy."
|
||||
)
|
||||
self.dd.return_null_values_to_strategy(dataframe, dk)
|
||||
return dk
|
||||
|
||||
# ensure user is feeding the correct indicators to the model
|
||||
self.check_if_feature_list_matches_strategy(dataframe, dk)
|
||||
|
||||
self.build_strategy_return_arrays(dataframe, dk, metadata["pair"], trained_timestamp)
|
||||
|
||||
return dk
|
||||
|
||||
def build_strategy_return_arrays(
|
||||
self, dataframe: DataFrame, dk: FreqaiDataKitchen, pair: str, trained_timestamp: int
|
||||
) -> None:
|
||||
|
||||
# hold the historical predictions in memory so we are sending back
|
||||
# correct array to strategy
|
||||
|
||||
if pair not in self.dd.model_return_values:
|
||||
# first predictions are made on entire historical candle set coming from strategy. This
|
||||
# allows FreqUI to show full return values.
|
||||
pred_df, do_preds = self.predict(dataframe, dk)
|
||||
if pair not in self.dd.historic_predictions:
|
||||
self.set_initial_historic_predictions(pred_df, dk, pair)
|
||||
self.dd.set_initial_return_values(pair, pred_df)
|
||||
|
||||
dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
|
||||
return
|
||||
elif self.dk.check_if_model_expired(trained_timestamp):
|
||||
pred_df = DataFrame(np.zeros((2, len(dk.label_list))), columns=dk.label_list)
|
||||
do_preds = np.ones(2, dtype=np.int_) * 2
|
||||
dk.DI_values = np.zeros(2)
|
||||
logger.warning(
|
||||
f"Model expired for {pair}, returning null values to strategy. Strategy "
|
||||
"construction should take care to consider this event with "
|
||||
"prediction == 0 and do_predict == 2"
|
||||
)
|
||||
else:
|
||||
# remaining predictions are made only on the most recent candles for performance and
|
||||
# historical accuracy reasons.
|
||||
pred_df, do_preds = self.predict(dataframe.iloc[-self.CONV_WIDTH:], dk, first=False)
|
||||
|
||||
if self.freqai_info.get('fit_live_predictions_candles', 0) and self.live:
|
||||
self.fit_live_predictions(dk, pair)
|
||||
self.dd.append_model_predictions(pair, pred_df, do_preds, dk, len(dataframe))
|
||||
dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
|
||||
|
||||
return
|
||||
|
||||
def check_if_feature_list_matches_strategy(
|
||||
self, dataframe: DataFrame, dk: FreqaiDataKitchen
|
||||
) -> None:
|
||||
"""
|
||||
Ensure user is passing the proper feature set if they are reusing an `identifier` pointing
|
||||
to a folder holding existing models.
|
||||
:param dataframe: DataFrame = strategy provided dataframe
|
||||
:param dk: FreqaiDataKitchen = non-persistent data container/analyzer for
|
||||
current coin/bot loop
|
||||
"""
|
||||
dk.find_features(dataframe)
|
||||
if "training_features_list_raw" in dk.data:
|
||||
feature_list = dk.data["training_features_list_raw"]
|
||||
else:
|
||||
feature_list = dk.training_features_list
|
||||
if dk.training_features_list != feature_list:
|
||||
raise OperationalException(
|
||||
"Trying to access pretrained model with `identifier` "
|
||||
"but found different features furnished by current strategy."
|
||||
"Change `identifier` to train from scratch, or ensure the"
|
||||
"strategy is furnishing the same features as the pretrained"
|
||||
"model"
|
||||
)
|
||||
|
||||
def data_cleaning_train(self, dk: FreqaiDataKitchen) -> None:
|
||||
"""
|
||||
Base data cleaning method for train
|
||||
Any function inside this method should drop training data points from the filtered_dataframe
|
||||
based on user decided logic. See FreqaiDataKitchen::use_SVM_to_remove_outliers() for an
|
||||
example of how outlier data points are dropped from the dataframe used for training.
|
||||
"""
|
||||
|
||||
if self.freqai_info["feature_parameters"].get(
|
||||
"principal_component_analysis", False
|
||||
):
|
||||
dk.principal_component_analysis()
|
||||
|
||||
if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False):
|
||||
dk.use_SVM_to_remove_outliers(predict=False)
|
||||
|
||||
if self.freqai_info["feature_parameters"].get("DI_threshold", 0):
|
||||
dk.data["avg_mean_dist"] = dk.compute_distances()
|
||||
|
||||
if self.freqai_info["feature_parameters"].get("use_DBSCAN_to_remove_outliers", False):
|
||||
if dk.pair in self.dd.old_DBSCAN_eps:
|
||||
eps = self.dd.old_DBSCAN_eps[dk.pair]
|
||||
else:
|
||||
eps = None
|
||||
dk.use_DBSCAN_to_remove_outliers(predict=False, eps=eps)
|
||||
self.dd.old_DBSCAN_eps[dk.pair] = dk.data['DBSCAN_eps']
|
||||
|
||||
def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None:
|
||||
"""
|
||||
Base data cleaning method for predict.
|
||||
These functions each modify dk.do_predict, which is a dataframe with equal length
|
||||
to the number of candles coming from and returning to the strategy. Inside do_predict,
|
||||
1 allows prediction and < 0 signals to the strategy that the model is not confident in
|
||||
the prediction.
|
||||
See FreqaiDataKitchen::remove_outliers() for an example
|
||||
of how the do_predict vector is modified. do_predict is ultimately passed back to strategy
|
||||
for buy signals.
|
||||
"""
|
||||
if self.freqai_info["feature_parameters"].get(
|
||||
"principal_component_analysis", False
|
||||
):
|
||||
dk.pca_transform(dataframe)
|
||||
|
||||
if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False):
|
||||
dk.use_SVM_to_remove_outliers(predict=True)
|
||||
|
||||
if self.freqai_info["feature_parameters"].get("DI_threshold", 0):
|
||||
dk.check_if_pred_in_training_spaces()
|
||||
|
||||
if self.freqai_info["feature_parameters"].get("use_DBSCAN_to_remove_outliers", False):
|
||||
dk.use_DBSCAN_to_remove_outliers(predict=True)
|
||||
|
||||
def model_exists(
|
||||
self,
|
||||
pair: str,
|
||||
dk: FreqaiDataKitchen,
|
||||
trained_timestamp: int = None,
|
||||
model_filename: str = "",
|
||||
scanning: bool = False,
|
||||
) -> bool:
|
||||
"""
|
||||
Given a pair and path, check if a model already exists
|
||||
:param pair: pair e.g. BTC/USD
|
||||
:param path: path to model
|
||||
:return:
|
||||
:boolean: whether the model file exists or not.
|
||||
"""
|
||||
coin, _ = pair.split("/")
|
||||
|
||||
if not self.live:
|
||||
dk.model_filename = model_filename = f"cb_{coin.lower()}_{trained_timestamp}"
|
||||
|
||||
path_to_modelfile = Path(dk.data_path / f"{model_filename}_model.joblib")
|
||||
file_exists = path_to_modelfile.is_file()
|
||||
if file_exists and not scanning:
|
||||
logger.info("Found model at %s", dk.data_path / dk.model_filename)
|
||||
elif not scanning:
|
||||
logger.info("Could not find model at %s", dk.data_path / dk.model_filename)
|
||||
return file_exists
|
||||
|
||||
def set_full_path(self) -> None:
|
||||
self.full_path = Path(
|
||||
self.config["user_data_dir"] / "models" / f"{self.freqai_info['identifier']}"
|
||||
)
|
||||
self.full_path.mkdir(parents=True, exist_ok=True)
|
||||
shutil.copy(
|
||||
self.config["config_files"][0],
|
||||
Path(self.full_path, Path(self.config["config_files"][0]).name),
|
||||
)
|
||||
|
||||
def train_model_in_series(
|
||||
self,
|
||||
new_trained_timerange: TimeRange,
|
||||
pair: str,
|
||||
strategy: IStrategy,
|
||||
dk: FreqaiDataKitchen,
|
||||
data_load_timerange: TimeRange,
|
||||
):
|
||||
"""
|
||||
Retrieve data and train model in single threaded mode (only used if model directory is empty
|
||||
upon startup for dry/live )
|
||||
:param new_trained_timerange: TimeRange = the timerange to train the model on
|
||||
:param metadata: dict = strategy provided metadata
|
||||
:param strategy: IStrategy = user defined strategy object
|
||||
:param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
|
||||
:param data_load_timerange: TimeRange = the amount of data to be loaded
|
||||
for populate_any_indicators
|
||||
(larger than new_trained_timerange so that
|
||||
new_trained_timerange does not contain any NaNs)
|
||||
"""
|
||||
|
||||
corr_dataframes, base_dataframes = self.dd.get_base_and_corr_dataframes(
|
||||
data_load_timerange, pair, dk
|
||||
)
|
||||
|
||||
with self.analysis_lock:
|
||||
unfiltered_dataframe = dk.use_strategy_to_populate_indicators(
|
||||
strategy, corr_dataframes, base_dataframes, pair
|
||||
)
|
||||
|
||||
unfiltered_dataframe = dk.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
|
||||
|
||||
# find the features indicated by strategy and store in datakitchen
|
||||
dk.find_features(unfiltered_dataframe)
|
||||
|
||||
model = self.train(unfiltered_dataframe, pair, dk)
|
||||
|
||||
self.dd.pair_dict[pair]["trained_timestamp"] = new_trained_timerange.stopts
|
||||
dk.set_new_model_names(pair, new_trained_timerange)
|
||||
self.dd.pair_dict[pair]["first"] = False
|
||||
if self.dd.pair_dict[pair]["priority"] == 1 and self.scanning:
|
||||
self.dd.pair_to_end_of_training_queue(pair)
|
||||
self.dd.save_data(model, pair, dk)
|
||||
|
||||
if self.freqai_info.get("purge_old_models", False):
|
||||
self.dd.purge_old_models()
|
||||
|
||||
def set_initial_historic_predictions(
|
||||
self, pred_df: DataFrame, dk: FreqaiDataKitchen, pair: str
|
||||
) -> None:
|
||||
"""
|
||||
This function is called only if the datadrawer failed to load an
|
||||
existing set of historic predictions. In this case, it builds
|
||||
the structure and sets fake predictions off the first training
|
||||
data. After that, FreqAI will append new real predictions to the
|
||||
set of historic predictions.
|
||||
|
||||
These values are used to generate live statistics which can be used
|
||||
in the strategy for adaptive values. E.g. &*_mean/std are quantities
|
||||
that can computed based on live predictions from the set of historical
|
||||
predictions. Those values can be used in the user strategy to better
|
||||
assess prediction rarity, and thus wait for probabilistically favorable
|
||||
entries relative to the live historical predictions.
|
||||
|
||||
If the user reuses an identifier on a subsequent instance,
|
||||
this function will not be called. In that case, "real" predictions
|
||||
will be appended to the loaded set of historic predictions.
|
||||
:param: df: DataFrame = the dataframe containing the training feature data
|
||||
:param: model: Any = A model which was `fit` using a common library such as
|
||||
catboost or lightgbm
|
||||
:param: dk: FreqaiDataKitchen = object containing methods for data analysis
|
||||
:param: pair: str = current pair
|
||||
"""
|
||||
|
||||
self.dd.historic_predictions[pair] = pred_df
|
||||
hist_preds_df = self.dd.historic_predictions[pair]
|
||||
|
||||
for label in hist_preds_df.columns:
|
||||
if hist_preds_df[label].dtype == object:
|
||||
continue
|
||||
hist_preds_df[f'{label}_mean'] = 0
|
||||
hist_preds_df[f'{label}_std'] = 0
|
||||
|
||||
hist_preds_df['do_predict'] = 0
|
||||
|
||||
if self.freqai_info['feature_parameters'].get('DI_threshold', 0) > 0:
|
||||
hist_preds_df['DI_values'] = 0
|
||||
|
||||
for return_str in dk.data['extra_returns_per_train']:
|
||||
hist_preds_df[return_str] = 0
|
||||
|
||||
# # for keras type models, the conv_window needs to be prepended so
|
||||
# # viewing is correct in frequi
|
||||
if self.freqai_info.get('keras', False):
|
||||
n_lost_points = self.freqai_info.get('conv_width', 2)
|
||||
zeros_df = DataFrame(np.zeros((n_lost_points, len(hist_preds_df.columns))),
|
||||
columns=hist_preds_df.columns)
|
||||
self.dd.historic_predictions[pair] = pd.concat(
|
||||
[zeros_df, hist_preds_df], axis=0, ignore_index=True)
|
||||
|
||||
def fit_live_predictions(self, dk: FreqaiDataKitchen, pair: str) -> None:
|
||||
"""
|
||||
Fit the labels with a gaussian distribution
|
||||
"""
|
||||
import scipy as spy
|
||||
|
||||
# add classes from classifier label types if used
|
||||
full_labels = dk.label_list + dk.unique_class_list
|
||||
|
||||
num_candles = self.freqai_info.get("fit_live_predictions_candles", 100)
|
||||
dk.data["labels_mean"], dk.data["labels_std"] = {}, {}
|
||||
for label in full_labels:
|
||||
if self.dd.historic_predictions[dk.pair][label].dtype == object:
|
||||
continue
|
||||
f = spy.stats.norm.fit(self.dd.historic_predictions[dk.pair][label].tail(num_candles))
|
||||
dk.data["labels_mean"][label], dk.data["labels_std"][label] = f[0], f[1]
|
||||
|
||||
return
|
||||
|
||||
def inference_timer(self, do='start'):
|
||||
"""
|
||||
Timer designed to track the cumulative time spent in FreqAI for one pass through
|
||||
the whitelist. This will check if the time spent is more than 1/4 the time
|
||||
of a single candle, and if so, it will warn the user of degraded performance
|
||||
"""
|
||||
if do == 'start':
|
||||
self.pair_it += 1
|
||||
self.begin_time = time.time()
|
||||
elif do == 'stop':
|
||||
end = time.time()
|
||||
self.inference_time += (end - self.begin_time)
|
||||
if self.pair_it == self.total_pairs:
|
||||
logger.info(
|
||||
f'Total time spent inferencing pairlist {self.inference_time:.2f} seconds')
|
||||
if self.inference_time > 0.25 * self.base_tf_seconds:
|
||||
logger.warning('Inference took over 25/% of the candle time. Reduce pairlist to'
|
||||
' avoid blinding open trades and degrading performance.')
|
||||
self.pair_it = 0
|
||||
self.inference_time = 0
|
||||
return
|
||||
|
||||
# Following methods which are overridden by user made prediction models.
|
||||
# See freqai/prediction_models/CatboostPredictionModel.py for an example.
|
||||
|
||||
@abstractmethod
|
||||
def train(self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen) -> Any:
|
||||
"""
|
||||
Filter the training data and train a model to it. Train makes heavy use of the datahandler
|
||||
for storing, saving, loading, and analyzing the data.
|
||||
:param unfiltered_dataframe: Full dataframe for the current training period
|
||||
:param metadata: pair metadata from strategy.
|
||||
:return: Trained model which can be used to inference (self.predict)
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def fit(self, data_dictionary: Dict[str, Any]) -> Any:
|
||||
"""
|
||||
Most regressors use the same function names and arguments e.g. user
|
||||
can drop in LGBMRegressor in place of CatBoostRegressor and all data
|
||||
management will be properly handled by Freqai.
|
||||
:param data_dictionary: Dict = the dictionary constructed by DataHandler to hold
|
||||
all the training and test data/labels.
|
||||
"""
|
||||
|
||||
return
|
||||
|
||||
@abstractmethod
|
||||
def predict(
|
||||
self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = True
|
||||
) -> Tuple[DataFrame, NDArray[np.int_]]:
|
||||
"""
|
||||
Filter the prediction features data and predict with it.
|
||||
:param unfiltered_dataframe: Full dataframe for the current backtest period.
|
||||
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
|
||||
:param first: boolean = whether this is the first prediction or not.
|
||||
:return:
|
||||
:predictions: np.array of predictions
|
||||
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
|
||||
data (NaNs) or felt uncertain about data (i.e. SVM and/or DI index)
|
||||
"""
|
||||
99
freqtrade/freqai/prediction_models/BaseClassifierModel.py
Normal file
99
freqtrade/freqai/prediction_models/BaseClassifierModel.py
Normal file
@ -0,0 +1,99 @@
|
||||
import logging
|
||||
from typing import Any, Tuple
|
||||
|
||||
import numpy as np
|
||||
import numpy.typing as npt
|
||||
import pandas as pd
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from freqtrade.freqai.freqai_interface import IFreqaiModel
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class BaseClassifierModel(IFreqaiModel):
|
||||
"""
|
||||
Base class for regression type models (e.g. Catboost, LightGBM, XGboost etc.).
|
||||
User *must* inherit from this class and set fit() and predict(). See example scripts
|
||||
such as prediction_models/CatboostPredictionModel.py for guidance.
|
||||
"""
|
||||
|
||||
def train(
|
||||
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
|
||||
) -> Any:
|
||||
"""
|
||||
Filter the training data and train a model to it. Train makes heavy use of the datakitchen
|
||||
for storing, saving, loading, and analyzing the data.
|
||||
:param unfiltered_dataframe: Full dataframe for the current training period
|
||||
:param metadata: pair metadata from strategy.
|
||||
:return:
|
||||
:model: Trained model which can be used to inference (self.predict)
|
||||
"""
|
||||
|
||||
logger.info("-------------------- Starting training " f"{pair} --------------------")
|
||||
|
||||
# filter the features requested by user in the configuration file and elegantly handle NaNs
|
||||
features_filtered, labels_filtered = dk.filter_features(
|
||||
unfiltered_dataframe,
|
||||
dk.training_features_list,
|
||||
dk.label_list,
|
||||
training_filter=True,
|
||||
)
|
||||
|
||||
start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d")
|
||||
end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d")
|
||||
logger.info(f"-------------------- Training on data from {start_date} to "
|
||||
f"{end_date}--------------------")
|
||||
# split data into train/test data.
|
||||
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
|
||||
if not self.freqai_info.get('fit_live_predictions', 0) or not self.live:
|
||||
dk.fit_labels()
|
||||
# normalize all data based on train_dataset only
|
||||
data_dictionary = dk.normalize_data(data_dictionary)
|
||||
|
||||
# optional additional data cleaning/analysis
|
||||
self.data_cleaning_train(dk)
|
||||
|
||||
logger.info(
|
||||
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
|
||||
)
|
||||
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
|
||||
|
||||
model = self.fit(data_dictionary)
|
||||
|
||||
logger.info(f"--------------------done training {pair}--------------------")
|
||||
|
||||
return model
|
||||
|
||||
def predict(
|
||||
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
|
||||
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
|
||||
"""
|
||||
Filter the prediction features data and predict with it.
|
||||
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
|
||||
:return:
|
||||
:pred_df: dataframe containing the predictions
|
||||
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
|
||||
data (NaNs) or felt uncertain about data (PCA and DI index)
|
||||
"""
|
||||
|
||||
dk.find_features(unfiltered_dataframe)
|
||||
filtered_dataframe, _ = dk.filter_features(
|
||||
unfiltered_dataframe, dk.training_features_list, training_filter=False
|
||||
)
|
||||
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
|
||||
dk.data_dictionary["prediction_features"] = filtered_dataframe
|
||||
|
||||
self.data_cleaning_predict(dk, filtered_dataframe)
|
||||
|
||||
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
|
||||
pred_df = DataFrame(predictions, columns=dk.label_list)
|
||||
|
||||
predictions_prob = self.model.predict_proba(dk.data_dictionary["prediction_features"])
|
||||
pred_df_prob = DataFrame(predictions_prob, columns=self.model.classes_)
|
||||
|
||||
pred_df = pd.concat([pred_df, pred_df_prob], axis=1)
|
||||
|
||||
return (pred_df, dk.do_predict)
|
||||
96
freqtrade/freqai/prediction_models/BaseRegressionModel.py
Normal file
96
freqtrade/freqai/prediction_models/BaseRegressionModel.py
Normal file
@ -0,0 +1,96 @@
|
||||
import logging
|
||||
from typing import Any, Tuple
|
||||
|
||||
import numpy as np
|
||||
import numpy.typing as npt
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from freqtrade.freqai.freqai_interface import IFreqaiModel
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class BaseRegressionModel(IFreqaiModel):
|
||||
"""
|
||||
Base class for regression type models (e.g. Catboost, LightGBM, XGboost etc.).
|
||||
User *must* inherit from this class and set fit() and predict(). See example scripts
|
||||
such as prediction_models/CatboostPredictionModel.py for guidance.
|
||||
"""
|
||||
|
||||
def train(
|
||||
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
|
||||
) -> Any:
|
||||
"""
|
||||
Filter the training data and train a model to it. Train makes heavy use of the datakitchen
|
||||
for storing, saving, loading, and analyzing the data.
|
||||
:param unfiltered_dataframe: Full dataframe for the current training period
|
||||
:param metadata: pair metadata from strategy.
|
||||
:return:
|
||||
:model: Trained model which can be used to inference (self.predict)
|
||||
"""
|
||||
|
||||
logger.info("-------------------- Starting training " f"{pair} --------------------")
|
||||
|
||||
# filter the features requested by user in the configuration file and elegantly handle NaNs
|
||||
features_filtered, labels_filtered = dk.filter_features(
|
||||
unfiltered_dataframe,
|
||||
dk.training_features_list,
|
||||
dk.label_list,
|
||||
training_filter=True,
|
||||
)
|
||||
|
||||
start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d")
|
||||
end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d")
|
||||
logger.info(f"-------------------- Training on data from {start_date} to "
|
||||
f"{end_date}--------------------")
|
||||
# split data into train/test data.
|
||||
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
|
||||
if not self.freqai_info.get('fit_live_predictions', 0) or not self.live:
|
||||
dk.fit_labels()
|
||||
# normalize all data based on train_dataset only
|
||||
data_dictionary = dk.normalize_data(data_dictionary)
|
||||
|
||||
# optional additional data cleaning/analysis
|
||||
self.data_cleaning_train(dk)
|
||||
|
||||
logger.info(
|
||||
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
|
||||
)
|
||||
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
|
||||
|
||||
model = self.fit(data_dictionary)
|
||||
|
||||
logger.info(f"--------------------done training {pair}--------------------")
|
||||
|
||||
return model
|
||||
|
||||
def predict(
|
||||
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
|
||||
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
|
||||
"""
|
||||
Filter the prediction features data and predict with it.
|
||||
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
|
||||
:return:
|
||||
:pred_df: dataframe containing the predictions
|
||||
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
|
||||
data (NaNs) or felt uncertain about data (PCA and DI index)
|
||||
"""
|
||||
|
||||
dk.find_features(unfiltered_dataframe)
|
||||
filtered_dataframe, _ = dk.filter_features(
|
||||
unfiltered_dataframe, dk.training_features_list, training_filter=False
|
||||
)
|
||||
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
|
||||
dk.data_dictionary["prediction_features"] = filtered_dataframe
|
||||
|
||||
# optional additional data cleaning/analysis
|
||||
self.data_cleaning_predict(dk, filtered_dataframe)
|
||||
|
||||
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
|
||||
pred_df = DataFrame(predictions, columns=dk.label_list)
|
||||
|
||||
pred_df = dk.denormalize_labels_from_metadata(pred_df)
|
||||
|
||||
return (pred_df, dk.do_predict)
|
||||
64
freqtrade/freqai/prediction_models/BaseTensorFlowModel.py
Normal file
64
freqtrade/freqai/prediction_models/BaseTensorFlowModel.py
Normal file
@ -0,0 +1,64 @@
|
||||
import logging
|
||||
from typing import Any
|
||||
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from freqtrade.freqai.freqai_interface import IFreqaiModel
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class BaseTensorFlowModel(IFreqaiModel):
|
||||
"""
|
||||
Base class for TensorFlow type models.
|
||||
User *must* inherit from this class and set fit() and predict().
|
||||
"""
|
||||
|
||||
def train(
|
||||
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
|
||||
) -> Any:
|
||||
"""
|
||||
Filter the training data and train a model to it. Train makes heavy use of the datakitchen
|
||||
for storing, saving, loading, and analyzing the data.
|
||||
:param unfiltered_dataframe: Full dataframe for the current training period
|
||||
:param metadata: pair metadata from strategy.
|
||||
:return:
|
||||
:model: Trained model which can be used to inference (self.predict)
|
||||
"""
|
||||
|
||||
logger.info("-------------------- Starting training " f"{pair} --------------------")
|
||||
|
||||
# filter the features requested by user in the configuration file and elegantly handle NaNs
|
||||
features_filtered, labels_filtered = dk.filter_features(
|
||||
unfiltered_dataframe,
|
||||
dk.training_features_list,
|
||||
dk.label_list,
|
||||
training_filter=True,
|
||||
)
|
||||
|
||||
start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d")
|
||||
end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d")
|
||||
logger.info(f"-------------------- Training on data from {start_date} to "
|
||||
f"{end_date}--------------------")
|
||||
# split data into train/test data.
|
||||
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
|
||||
if not self.freqai_info.get('fit_live_predictions', 0) or not self.live:
|
||||
dk.fit_labels()
|
||||
# normalize all data based on train_dataset only
|
||||
data_dictionary = dk.normalize_data(data_dictionary)
|
||||
|
||||
# optional additional data cleaning/analysis
|
||||
self.data_cleaning_train(dk)
|
||||
|
||||
logger.info(
|
||||
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
|
||||
)
|
||||
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
|
||||
|
||||
model = self.fit(data_dictionary)
|
||||
|
||||
logger.info(f"--------------------done training {pair}--------------------")
|
||||
|
||||
return model
|
||||
41
freqtrade/freqai/prediction_models/CatboostClassifier.py
Normal file
41
freqtrade/freqai/prediction_models/CatboostClassifier.py
Normal file
@ -0,0 +1,41 @@
|
||||
import logging
|
||||
from typing import Any, Dict
|
||||
|
||||
from catboost import CatBoostClassifier, Pool
|
||||
|
||||
from freqtrade.freqai.prediction_models.BaseClassifierModel import BaseClassifierModel
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class CatboostClassifier(BaseClassifierModel):
|
||||
"""
|
||||
User created prediction model. The class needs to override three necessary
|
||||
functions, predict(), train(), fit(). The class inherits ModelHandler which
|
||||
has its own DataHandler where data is held, saved, loaded, and managed.
|
||||
"""
|
||||
|
||||
def fit(self, data_dictionary: Dict) -> Any:
|
||||
"""
|
||||
User sets up the training and test data to fit their desired model here
|
||||
:params:
|
||||
:data_dictionary: the dictionary constructed by DataHandler to hold
|
||||
all the training and test data/labels.
|
||||
"""
|
||||
|
||||
train_data = Pool(
|
||||
data=data_dictionary["train_features"],
|
||||
label=data_dictionary["train_labels"],
|
||||
weight=data_dictionary["train_weights"],
|
||||
)
|
||||
|
||||
cbr = CatBoostClassifier(
|
||||
allow_writing_files=False,
|
||||
loss_function='MultiClass',
|
||||
**self.model_training_parameters,
|
||||
)
|
||||
|
||||
cbr.fit(train_data)
|
||||
|
||||
return cbr
|
||||
53
freqtrade/freqai/prediction_models/CatboostRegressor.py
Normal file
53
freqtrade/freqai/prediction_models/CatboostRegressor.py
Normal file
@ -0,0 +1,53 @@
|
||||
import gc
|
||||
import logging
|
||||
from typing import Any, Dict
|
||||
|
||||
from catboost import CatBoostRegressor, Pool
|
||||
|
||||
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class CatboostRegressor(BaseRegressionModel):
|
||||
"""
|
||||
User created prediction model. The class needs to override three necessary
|
||||
functions, predict(), train(), fit(). The class inherits ModelHandler which
|
||||
has its own DataHandler where data is held, saved, loaded, and managed.
|
||||
"""
|
||||
|
||||
def fit(self, data_dictionary: Dict) -> Any:
|
||||
"""
|
||||
User sets up the training and test data to fit their desired model here
|
||||
:param data_dictionary: the dictionary constructed by DataHandler to hold
|
||||
all the training and test data/labels.
|
||||
"""
|
||||
|
||||
train_data = Pool(
|
||||
data=data_dictionary["train_features"],
|
||||
label=data_dictionary["train_labels"],
|
||||
weight=data_dictionary["train_weights"],
|
||||
)
|
||||
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
|
||||
test_data = None
|
||||
else:
|
||||
test_data = Pool(
|
||||
data=data_dictionary["test_features"],
|
||||
label=data_dictionary["test_labels"],
|
||||
weight=data_dictionary["test_weights"],
|
||||
)
|
||||
|
||||
model = CatBoostRegressor(
|
||||
allow_writing_files=False,
|
||||
**self.model_training_parameters,
|
||||
)
|
||||
|
||||
model.fit(X=train_data, eval_set=test_data)
|
||||
|
||||
# some evidence that catboost pools have memory leaks:
|
||||
# https://github.com/catboost/catboost/issues/1835
|
||||
del train_data, test_data
|
||||
gc.collect()
|
||||
|
||||
return model
|
||||
@ -0,0 +1,44 @@
|
||||
import logging
|
||||
from typing import Any, Dict
|
||||
|
||||
from catboost import CatBoostRegressor # , Pool
|
||||
from sklearn.multioutput import MultiOutputRegressor
|
||||
|
||||
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class CatboostRegressorMultiTarget(BaseRegressionModel):
|
||||
"""
|
||||
User created prediction model. The class needs to override three necessary
|
||||
functions, predict(), train(), fit(). The class inherits ModelHandler which
|
||||
has its own DataHandler where data is held, saved, loaded, and managed.
|
||||
"""
|
||||
|
||||
def fit(self, data_dictionary: Dict) -> Any:
|
||||
"""
|
||||
User sets up the training and test data to fit their desired model here
|
||||
:param data_dictionary: the dictionary constructed by DataHandler to hold
|
||||
all the training and test data/labels.
|
||||
"""
|
||||
|
||||
cbr = CatBoostRegressor(
|
||||
allow_writing_files=False,
|
||||
**self.model_training_parameters,
|
||||
)
|
||||
|
||||
X = data_dictionary["train_features"]
|
||||
y = data_dictionary["train_labels"]
|
||||
eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
|
||||
sample_weight = data_dictionary["train_weights"]
|
||||
|
||||
model = MultiOutputRegressor(estimator=cbr)
|
||||
model.fit(X=X, y=y, sample_weight=sample_weight) # , eval_set=eval_set)
|
||||
|
||||
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
|
||||
train_score = model.score(X, y)
|
||||
test_score = model.score(*eval_set)
|
||||
logger.info(f"Train score {train_score}, Test score {test_score}")
|
||||
return model
|
||||
38
freqtrade/freqai/prediction_models/LightGBMClassifier.py
Normal file
38
freqtrade/freqai/prediction_models/LightGBMClassifier.py
Normal file
@ -0,0 +1,38 @@
|
||||
import logging
|
||||
from typing import Any, Dict
|
||||
|
||||
from lightgbm import LGBMClassifier
|
||||
|
||||
from freqtrade.freqai.prediction_models.BaseClassifierModel import BaseClassifierModel
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class LightGBMClassifier(BaseClassifierModel):
|
||||
"""
|
||||
User created prediction model. The class needs to override three necessary
|
||||
functions, predict(), train(), fit(). The class inherits ModelHandler which
|
||||
has its own DataHandler where data is held, saved, loaded, and managed.
|
||||
"""
|
||||
|
||||
def fit(self, data_dictionary: Dict) -> Any:
|
||||
"""
|
||||
User sets up the training and test data to fit their desired model here
|
||||
:params:
|
||||
:data_dictionary: the dictionary constructed by DataHandler to hold
|
||||
all the training and test data/labels.
|
||||
"""
|
||||
|
||||
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
|
||||
eval_set = None
|
||||
else:
|
||||
eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
|
||||
X = data_dictionary["train_features"]
|
||||
y = data_dictionary["train_labels"]
|
||||
|
||||
model = LGBMClassifier(**self.model_training_parameters)
|
||||
|
||||
model.fit(X=X, y=y, eval_set=eval_set)
|
||||
|
||||
return model
|
||||
39
freqtrade/freqai/prediction_models/LightGBMRegressor.py
Normal file
39
freqtrade/freqai/prediction_models/LightGBMRegressor.py
Normal file
@ -0,0 +1,39 @@
|
||||
import logging
|
||||
from typing import Any, Dict
|
||||
|
||||
from lightgbm import LGBMRegressor
|
||||
|
||||
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class LightGBMRegressor(BaseRegressionModel):
|
||||
"""
|
||||
User created prediction model. The class needs to override three necessary
|
||||
functions, predict(), train(), fit(). The class inherits ModelHandler which
|
||||
has its own DataHandler where data is held, saved, loaded, and managed.
|
||||
"""
|
||||
|
||||
def fit(self, data_dictionary: Dict) -> Any:
|
||||
"""
|
||||
Most regressors use the same function names and arguments e.g. user
|
||||
can drop in LGBMRegressor in place of CatBoostRegressor and all data
|
||||
management will be properly handled by Freqai.
|
||||
:param data_dictionary: the dictionary constructed by DataHandler to hold
|
||||
all the training and test data/labels.
|
||||
"""
|
||||
|
||||
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
|
||||
eval_set = None
|
||||
else:
|
||||
eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
|
||||
X = data_dictionary["train_features"]
|
||||
y = data_dictionary["train_labels"]
|
||||
|
||||
model = LGBMRegressor(**self.model_training_parameters)
|
||||
|
||||
model.fit(X=X, y=y, eval_set=eval_set)
|
||||
|
||||
return model
|
||||
@ -0,0 +1,39 @@
|
||||
import logging
|
||||
from typing import Any, Dict
|
||||
|
||||
from lightgbm import LGBMRegressor
|
||||
from sklearn.multioutput import MultiOutputRegressor
|
||||
|
||||
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class LightGBMRegressorMultiTarget(BaseRegressionModel):
|
||||
"""
|
||||
User created prediction model. The class needs to override three necessary
|
||||
functions, predict(), train(), fit(). The class inherits ModelHandler which
|
||||
has its own DataHandler where data is held, saved, loaded, and managed.
|
||||
"""
|
||||
|
||||
def fit(self, data_dictionary: Dict) -> Any:
|
||||
"""
|
||||
User sets up the training and test data to fit their desired model here
|
||||
:param data_dictionary: the dictionary constructed by DataHandler to hold
|
||||
all the training and test data/labels.
|
||||
"""
|
||||
|
||||
lgb = LGBMRegressor(**self.model_training_parameters)
|
||||
|
||||
X = data_dictionary["train_features"]
|
||||
y = data_dictionary["train_labels"]
|
||||
eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
|
||||
sample_weight = data_dictionary["train_weights"]
|
||||
|
||||
model = MultiOutputRegressor(estimator=lgb)
|
||||
model.fit(X=X, y=y, sample_weight=sample_weight) # , eval_set=eval_set)
|
||||
train_score = model.score(X, y)
|
||||
test_score = model.score(*eval_set)
|
||||
logger.info(f"Train score {train_score}, Test score {test_score}")
|
||||
return model
|
||||
0
freqtrade/freqai/prediction_models/__init__.py
Normal file
0
freqtrade/freqai/prediction_models/__init__.py
Normal file
@ -89,6 +89,9 @@ class Backtesting:
|
||||
self.dataprovider = DataProvider(self.config, self.exchange)
|
||||
|
||||
if self.config.get('strategy_list'):
|
||||
if self.config.get('freqai', {}).get('enabled', False):
|
||||
raise OperationalException(
|
||||
"You can't use strategy_list and freqai at the same time.")
|
||||
for strat in list(self.config['strategy_list']):
|
||||
stratconf = deepcopy(self.config)
|
||||
stratconf['strategy'] = strat
|
||||
@ -207,6 +210,15 @@ class Backtesting:
|
||||
"""
|
||||
self.progress.init_step(BacktestState.DATALOAD, 1)
|
||||
|
||||
if self.config.get('freqai', {}).get('enabled', False):
|
||||
startup_candles = int(self.config.get('freqai', {}).get('startup_candles', 0))
|
||||
if not startup_candles:
|
||||
raise OperationalException('FreqAI backtesting module requires user set '
|
||||
'startup_candles in config.')
|
||||
self.required_startup += int(self.config.get('freqai', {}).get('startup_candles', 0))
|
||||
logger.info(f'Increasing startup_candle_count for freqai to {self.required_startup}')
|
||||
self.config['startup_candle_count'] = self.required_startup
|
||||
|
||||
data = history.load_data(
|
||||
datadir=self.config['datadir'],
|
||||
pairs=self.pairlists.whitelist,
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
import re
|
||||
from typing import List
|
||||
from typing import Any, Dict, List
|
||||
|
||||
|
||||
def expand_pairlist(wildcardpl: List[str], available_pairs: List[str],
|
||||
@ -40,3 +40,13 @@ def expand_pairlist(wildcardpl: List[str], available_pairs: List[str],
|
||||
except re.error as err:
|
||||
raise ValueError(f"Wildcard error in {pair_wc}, {err}")
|
||||
return result
|
||||
|
||||
|
||||
def dynamic_expand_pairlist(config: Dict[str, Any], markets: List[str]) -> List[str]:
|
||||
expanded_pairs = expand_pairlist(config['pairs'], markets)
|
||||
if config.get('freqai', {}).get('enabled', False):
|
||||
corr_pairlist = config['freqai']['feature_parameters']['include_corr_pairlist']
|
||||
expanded_pairs += [pair for pair in corr_pairlist
|
||||
if pair not in config['pairs']]
|
||||
|
||||
return expanded_pairs
|
||||
|
||||
57
freqtrade/resolvers/freqaimodel_resolver.py
Normal file
57
freqtrade/resolvers/freqaimodel_resolver.py
Normal file
@ -0,0 +1,57 @@
|
||||
# pragma pylint: disable=attribute-defined-outside-init
|
||||
|
||||
"""
|
||||
This module load a custom model for freqai
|
||||
"""
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import Dict
|
||||
|
||||
from freqtrade.constants import USERPATH_FREQAIMODELS
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.freqai.freqai_interface import IFreqaiModel
|
||||
from freqtrade.resolvers import IResolver
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class FreqaiModelResolver(IResolver):
|
||||
"""
|
||||
This class contains all the logic to load custom hyperopt loss class
|
||||
"""
|
||||
|
||||
object_type = IFreqaiModel
|
||||
object_type_str = "FreqaiModel"
|
||||
user_subdir = USERPATH_FREQAIMODELS
|
||||
initial_search_path = (
|
||||
Path(__file__).parent.parent.joinpath("freqai/prediction_models").resolve()
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def load_freqaimodel(config: Dict) -> IFreqaiModel:
|
||||
"""
|
||||
Load the custom class from config parameter
|
||||
:param config: configuration dictionary
|
||||
"""
|
||||
disallowed_models = ["BaseRegressionModel", "BaseTensorFlowModel"]
|
||||
|
||||
freqaimodel_name = config.get("freqaimodel")
|
||||
if not freqaimodel_name:
|
||||
raise OperationalException(
|
||||
"No freqaimodel set. Please use `--freqaimodel` to "
|
||||
"specify the FreqaiModel class to use.\n"
|
||||
)
|
||||
if freqaimodel_name in disallowed_models:
|
||||
raise OperationalException(
|
||||
f"{freqaimodel_name} is a baseclass and cannot be used directly. Please choose "
|
||||
"an existing child class or inherit from this baseclass.\n"
|
||||
)
|
||||
freqaimodel = FreqaiModelResolver.load_object(
|
||||
freqaimodel_name,
|
||||
config,
|
||||
kwargs={"config": config},
|
||||
extra_dir=config.get("freqaimodel_path"),
|
||||
)
|
||||
|
||||
return freqaimodel
|
||||
@ -145,11 +145,29 @@ class IStrategy(ABC, HyperStrategyMixin):
|
||||
informative_data.candle_type = config['candle_type_def']
|
||||
self._ft_informative.append((informative_data, cls_method))
|
||||
|
||||
def load_freqAI_model(self) -> None:
|
||||
if self.config.get('freqai', {}).get('enabled', False):
|
||||
# Import here to avoid importing this if freqAI is disabled
|
||||
from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
|
||||
|
||||
self.freqai = FreqaiModelResolver.load_freqaimodel(self.config)
|
||||
self.freqai_info = self.config["freqai"]
|
||||
else:
|
||||
# Gracious failures if freqAI is disabled but "start" is called.
|
||||
class DummyClass():
|
||||
def start(self, *args, **kwargs):
|
||||
raise OperationalException(
|
||||
'freqAI is not enabled. '
|
||||
'Please enable it in your config to use this strategy.')
|
||||
self.freqai = DummyClass() # type: ignore
|
||||
|
||||
def ft_bot_start(self, **kwargs) -> None:
|
||||
"""
|
||||
Strategy init - runs after dataprovider has been added.
|
||||
Must call bot_start()
|
||||
"""
|
||||
self.load_freqAI_model()
|
||||
|
||||
strategy_safe_wrapper(self.bot_start)()
|
||||
|
||||
self.ft_load_hyper_params(self.config.get('runmode') == RunMode.HYPEROPT)
|
||||
@ -557,6 +575,22 @@ class IStrategy(ABC, HyperStrategyMixin):
|
||||
"""
|
||||
return None
|
||||
|
||||
def populate_any_indicators(self, pair: str, df: DataFrame, tf: str,
|
||||
informative: DataFrame = None,
|
||||
set_generalized_indicators: bool = False) -> DataFrame:
|
||||
"""
|
||||
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.
|
||||
This method is *only* used in FreqaiDataKitchen class and therefore
|
||||
it is only called if FreqAI is active.
|
||||
: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
|
||||
"""
|
||||
return df
|
||||
|
||||
###
|
||||
# END - Intended to be overridden by strategy
|
||||
###
|
||||
|
||||
328
freqtrade/templates/FreqaiExampleStrategy.py
Normal file
328
freqtrade/templates/FreqaiExampleStrategy.py
Normal file
@ -0,0 +1,328 @@
|
||||
import logging
|
||||
from functools import reduce
|
||||
|
||||
import pandas as pd
|
||||
import talib.abstract as ta
|
||||
from pandas import DataFrame
|
||||
from technical import qtpylib
|
||||
|
||||
from freqtrade.exchange import timeframe_to_prev_date
|
||||
from freqtrade.persistence import Trade
|
||||
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class FreqaiExampleStrategy(IStrategy):
|
||||
"""
|
||||
Example strategy showing how the user connects their own
|
||||
IFreqaiModel to the strategy. Namely, the user uses:
|
||||
self.freqai.start(dataframe, metadata)
|
||||
|
||||
to make predictions on their data. populate_any_indicators() automatically
|
||||
generates the variety of features indicated by the user in the
|
||||
canonical freqtrade configuration file under config['freqai'].
|
||||
"""
|
||||
|
||||
minimal_roi = {"0": 0.1, "240": -1}
|
||||
|
||||
plot_config = {
|
||||
"main_plot": {},
|
||||
"subplots": {
|
||||
"prediction": {"prediction": {"color": "blue"}},
|
||||
"target_roi": {
|
||||
"target_roi": {"color": "brown"},
|
||||
},
|
||||
"do_predict": {
|
||||
"do_predict": {"color": "brown"},
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
process_only_new_candles = True
|
||||
stoploss = -0.05
|
||||
use_exit_signal = True
|
||||
startup_candle_count: int = 300
|
||||
can_short = False
|
||||
|
||||
linear_roi_offset = DecimalParameter(
|
||||
0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
|
||||
)
|
||||
max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
|
||||
|
||||
def informative_pairs(self):
|
||||
whitelist_pairs = self.dp.current_whitelist()
|
||||
corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
|
||||
informative_pairs = []
|
||||
for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
|
||||
for pair in whitelist_pairs:
|
||||
informative_pairs.append((pair, tf))
|
||||
for pair in corr_pairs:
|
||||
if pair in whitelist_pairs:
|
||||
continue # avoid duplication
|
||||
informative_pairs.append((pair, tf))
|
||||
return informative_pairs
|
||||
|
||||
def populate_any_indicators(
|
||||
self, pair, df, tf, informative=None, set_generalized_indicators=False
|
||||
):
|
||||
"""
|
||||
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 `'%-' + coin `
|
||||
(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
|
||||
"""
|
||||
|
||||
coin = pair.split('/')[0]
|
||||
|
||||
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"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
|
||||
informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
|
||||
informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
|
||||
|
||||
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
|
||||
|
||||
bollinger = qtpylib.bollinger_bands(
|
||||
qtpylib.typical_price(informative), window=t, stds=2.2
|
||||
)
|
||||
informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
|
||||
informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
|
||||
informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]
|
||||
|
||||
informative[f"%-{coin}bb_width-period_{t}"] = (
|
||||
informative[f"{coin}bb_upperband-period_{t}"]
|
||||
- informative[f"{coin}bb_lowerband-period_{t}"]
|
||||
) / informative[f"{coin}bb_middleband-period_{t}"]
|
||||
informative[f"%-{coin}close-bb_lower-period_{t}"] = (
|
||||
informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"]
|
||||
)
|
||||
|
||||
informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
|
||||
|
||||
informative[f"%-{coin}relative_volume-period_{t}"] = (
|
||||
informative["volume"] / informative["volume"].rolling(t).mean()
|
||||
)
|
||||
|
||||
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
|
||||
informative[f"%-{coin}raw_volume"] = informative["volume"]
|
||||
informative[f"%-{coin}raw_price"] = informative["close"]
|
||||
|
||||
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
|
||||
|
||||
# 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
|
||||
)
|
||||
|
||||
# Classifiers are typically set up with strings as targets:
|
||||
# df['&s-up_or_down'] = np.where( df["close"].shift(-100) >
|
||||
# df["close"], 'up', 'down')
|
||||
|
||||
# If user wishes to use multiple targets, they can add more by
|
||||
# appending more columns with '&'. User should keep in mind that multi targets
|
||||
# requires a multioutput prediction model such as
|
||||
# templates/CatboostPredictionMultiModel.py,
|
||||
|
||||
# df["&-s_range"] = (
|
||||
# df["close"]
|
||||
# .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
# .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
# .max()
|
||||
# -
|
||||
# df["close"]
|
||||
# .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
# .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
# .min()
|
||||
# )
|
||||
|
||||
return df
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
# All indicators must be populated by populate_any_indicators() for live functionality
|
||||
# to work correctly.
|
||||
|
||||
# the model will return all labels created by user in `populate_any_indicators`
|
||||
# (& 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.
|
||||
|
||||
dataframe = self.freqai.start(dataframe, metadata, self)
|
||||
|
||||
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
|
||||
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]]
|
||||
|
||||
if enter_long_conditions:
|
||||
df.loc[
|
||||
reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
|
||||
] = (1, "long")
|
||||
|
||||
enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"]]
|
||||
|
||||
if enter_short_conditions:
|
||||
df.loc[
|
||||
reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
|
||||
] = (1, "short")
|
||||
|
||||
return df
|
||||
|
||||
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"] * 0.25]
|
||||
if exit_long_conditions:
|
||||
df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
|
||||
|
||||
exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"] * 0.25]
|
||||
if exit_short_conditions:
|
||||
df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
|
||||
|
||||
return df
|
||||
|
||||
def get_ticker_indicator(self):
|
||||
return int(self.config["timeframe"][:-1])
|
||||
|
||||
def custom_exit(
|
||||
self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs
|
||||
):
|
||||
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
|
||||
|
||||
trade_date = timeframe_to_prev_date(self.config["timeframe"], trade.open_date_utc)
|
||||
trade_candle = dataframe.loc[(dataframe["date"] == trade_date)]
|
||||
|
||||
if trade_candle.empty:
|
||||
return None
|
||||
trade_candle = trade_candle.squeeze()
|
||||
|
||||
follow_mode = self.config.get("freqai", {}).get("follow_mode", False)
|
||||
|
||||
if not follow_mode:
|
||||
pair_dict = self.freqai.dd.pair_dict
|
||||
else:
|
||||
pair_dict = self.freqai.dd.follower_dict
|
||||
|
||||
entry_tag = trade.enter_tag
|
||||
|
||||
if (
|
||||
"prediction" + entry_tag not in pair_dict[pair]
|
||||
or pair_dict[pair]['extras']["prediction" + entry_tag] == 0
|
||||
):
|
||||
pair_dict[pair]['extras']["prediction" + entry_tag] = abs(trade_candle["&-s_close"])
|
||||
if not follow_mode:
|
||||
self.freqai.dd.save_drawer_to_disk()
|
||||
else:
|
||||
self.freqai.dd.save_follower_dict_to_disk()
|
||||
|
||||
roi_price = pair_dict[pair]['extras']["prediction" + entry_tag]
|
||||
roi_time = self.max_roi_time_long.value
|
||||
|
||||
roi_decay = roi_price * (
|
||||
1 - ((current_time - trade.open_date_utc).seconds) / (roi_time * 60)
|
||||
)
|
||||
if roi_decay < 0:
|
||||
roi_decay = self.linear_roi_offset.value
|
||||
else:
|
||||
roi_decay += self.linear_roi_offset.value
|
||||
|
||||
if current_profit > roi_decay:
|
||||
return "roi_custom_win"
|
||||
|
||||
if current_profit < -roi_decay:
|
||||
return "roi_custom_loss"
|
||||
|
||||
def confirm_trade_exit(
|
||||
self,
|
||||
pair: str,
|
||||
trade: Trade,
|
||||
order_type: str,
|
||||
amount: float,
|
||||
rate: float,
|
||||
time_in_force: str,
|
||||
exit_reason: str,
|
||||
current_time,
|
||||
**kwargs,
|
||||
) -> bool:
|
||||
|
||||
entry_tag = trade.enter_tag
|
||||
follow_mode = self.config.get("freqai", {}).get("follow_mode", False)
|
||||
if not follow_mode:
|
||||
pair_dict = self.freqai.dd.pair_dict
|
||||
else:
|
||||
pair_dict = self.freqai.dd.follower_dict
|
||||
|
||||
pair_dict[pair]['extras']["prediction" + entry_tag] = 0
|
||||
if not follow_mode:
|
||||
self.freqai.dd.save_drawer_to_disk()
|
||||
else:
|
||||
self.freqai.dd.save_follower_dict_to_disk()
|
||||
|
||||
return True
|
||||
|
||||
def confirm_trade_entry(
|
||||
self,
|
||||
pair: str,
|
||||
order_type: str,
|
||||
amount: float,
|
||||
rate: float,
|
||||
time_in_force: str,
|
||||
current_time,
|
||||
entry_tag,
|
||||
side: str,
|
||||
**kwargs,
|
||||
) -> bool:
|
||||
|
||||
df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
last_candle = df.iloc[-1].squeeze()
|
||||
|
||||
if side == "long":
|
||||
if rate > (last_candle["close"] * (1 + 0.0025)):
|
||||
return False
|
||||
else:
|
||||
if rate < (last_candle["close"] * (1 - 0.0025)):
|
||||
return False
|
||||
|
||||
return True
|
||||
@ -33,9 +33,10 @@ nav:
|
||||
- Backtest analysis: advanced-backtesting.md
|
||||
- Advanced Topics:
|
||||
- Advanced Post-installation Tasks: advanced-setup.md
|
||||
- Edge Positioning: edge.md
|
||||
- Advanced Strategy: strategy-advanced.md
|
||||
- Advanced Hyperopt: advanced-hyperopt.md
|
||||
- FreqAI: freqai.md
|
||||
- Edge Positioning: edge.md
|
||||
- Sandbox Testing: sandbox-testing.md
|
||||
- FAQ: faq.md
|
||||
- SQL Cheat-sheet: sql_cheatsheet.md
|
||||
|
||||
@ -2,6 +2,7 @@
|
||||
-r requirements.txt
|
||||
-r requirements-plot.txt
|
||||
-r requirements-hyperopt.txt
|
||||
-r requirements-freqai.txt
|
||||
-r docs/requirements-docs.txt
|
||||
|
||||
coveralls==3.3.1
|
||||
|
||||
8
requirements-freqai.txt
Normal file
8
requirements-freqai.txt
Normal file
@ -0,0 +1,8 @@
|
||||
# Include all requirements to run the bot.
|
||||
-r requirements.txt
|
||||
|
||||
# Required for freqai
|
||||
scikit-learn==1.1.2
|
||||
joblib==1.1.0
|
||||
catboost==1.0.6; platform_machine != 'aarch64'
|
||||
lightgbm==3.3.2
|
||||
10
setup.py
10
setup.py
@ -12,6 +12,13 @@ hyperopt = [
|
||||
'progressbar2',
|
||||
]
|
||||
|
||||
freqai = [
|
||||
'scikit-learn',
|
||||
'joblib',
|
||||
'catboost; platform_machine != "aarch64"',
|
||||
'lightgbm',
|
||||
]
|
||||
|
||||
develop = [
|
||||
'coveralls',
|
||||
'flake8',
|
||||
@ -31,7 +38,7 @@ jupyter = [
|
||||
'nbconvert',
|
||||
]
|
||||
|
||||
all_extra = plot + develop + jupyter + hyperopt
|
||||
all_extra = plot + develop + jupyter + hyperopt + freqai
|
||||
|
||||
setup(
|
||||
tests_require=[
|
||||
@ -79,6 +86,7 @@ setup(
|
||||
'plot': plot,
|
||||
'jupyter': jupyter,
|
||||
'hyperopt': hyperopt,
|
||||
'freqai': freqai,
|
||||
'all': all_extra,
|
||||
},
|
||||
)
|
||||
|
||||
10
setup.sh
10
setup.sh
@ -77,7 +77,15 @@ function updateenv() {
|
||||
fi
|
||||
fi
|
||||
|
||||
${PYTHON} -m pip install --upgrade -r ${REQUIREMENTS} ${REQUIREMENTS_HYPEROPT} ${REQUIREMENTS_PLOT}
|
||||
REQUIREMENTS_FREQAI=""
|
||||
read -p "Do you want to install dependencies for freqai [y/N]? "
|
||||
dev=$REPLY
|
||||
if [[ $REPLY =~ ^[Yy]$ ]]
|
||||
then
|
||||
REQUIREMENTS_FREQAI="-r requirements-freqai.txt"
|
||||
fi
|
||||
|
||||
${PYTHON} -m pip install --upgrade -r ${REQUIREMENTS} ${REQUIREMENTS_HYPEROPT} ${REQUIREMENTS_PLOT} ${REQUIREMENTS_FREQAI}
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "Failed installing dependencies"
|
||||
exit 1
|
||||
|
||||
126
tests/freqai/conftest.py
Normal file
126
tests/freqai/conftest.py
Normal file
@ -0,0 +1,126 @@
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
from freqtrade.configuration import TimeRange
|
||||
from freqtrade.data.dataprovider import DataProvider
|
||||
from freqtrade.freqai.data_drawer import FreqaiDataDrawer
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from freqtrade.resolvers import StrategyResolver
|
||||
from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
|
||||
from tests.conftest import get_patched_exchange
|
||||
|
||||
|
||||
@pytest.fixture(scope="function")
|
||||
def freqai_conf(default_conf, tmpdir):
|
||||
freqaiconf = deepcopy(default_conf)
|
||||
freqaiconf.update(
|
||||
{
|
||||
"datadir": Path(default_conf["datadir"]),
|
||||
"strategy": "freqai_test_strat",
|
||||
"user_data_dir": Path(tmpdir),
|
||||
"strategy-path": "freqtrade/tests/strategy/strats",
|
||||
"freqaimodel": "LightGBMRegressor",
|
||||
"freqaimodel_path": "freqai/prediction_models",
|
||||
"timerange": "20180110-20180115",
|
||||
"freqai": {
|
||||
"enabled": True,
|
||||
"startup_candles": 10000,
|
||||
"purge_old_models": True,
|
||||
"train_period_days": 5,
|
||||
"backtest_period_days": 2,
|
||||
"live_retrain_hours": 0,
|
||||
"expiration_hours": 1,
|
||||
"identifier": "uniqe-id100",
|
||||
"live_trained_timestamp": 0,
|
||||
"feature_parameters": {
|
||||
"include_timeframes": ["5m"],
|
||||
"include_corr_pairlist": ["ADA/BTC", "DASH/BTC"],
|
||||
"label_period_candles": 20,
|
||||
"include_shifted_candles": 1,
|
||||
"DI_threshold": 0.9,
|
||||
"weight_factor": 0.9,
|
||||
"principal_component_analysis": False,
|
||||
"use_SVM_to_remove_outliers": True,
|
||||
"stratify_training_data": 0,
|
||||
"indicator_max_period_candles": 10,
|
||||
"indicator_periods_candles": [10],
|
||||
},
|
||||
"data_split_parameters": {"test_size": 0.33, "random_state": 1},
|
||||
"model_training_parameters": {"n_estimators": 100},
|
||||
},
|
||||
"config_files": [Path('config_examples', 'config_freqai.example.json')]
|
||||
}
|
||||
)
|
||||
freqaiconf['exchange'].update({'pair_whitelist': ['ADA/BTC', 'DASH/BTC', 'ETH/BTC', 'LTC/BTC']})
|
||||
return freqaiconf
|
||||
|
||||
|
||||
def get_patched_data_kitchen(mocker, freqaiconf):
|
||||
dk = FreqaiDataKitchen(freqaiconf)
|
||||
return dk
|
||||
|
||||
|
||||
def get_patched_data_drawer(mocker, freqaiconf):
|
||||
# dd = mocker.patch('freqtrade.freqai.data_drawer', MagicMock())
|
||||
dd = FreqaiDataDrawer(freqaiconf)
|
||||
return dd
|
||||
|
||||
|
||||
def get_patched_freqai_strategy(mocker, freqaiconf):
|
||||
strategy = StrategyResolver.load_strategy(freqaiconf)
|
||||
strategy.ft_bot_start()
|
||||
|
||||
return strategy
|
||||
|
||||
|
||||
def get_patched_freqaimodel(mocker, freqaiconf):
|
||||
freqaimodel = FreqaiModelResolver.load_freqaimodel(freqaiconf)
|
||||
|
||||
return freqaimodel
|
||||
|
||||
|
||||
def get_freqai_live_analyzed_dataframe(mocker, freqaiconf):
|
||||
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
|
||||
exchange = get_patched_exchange(mocker, freqaiconf)
|
||||
strategy.dp = DataProvider(freqaiconf, exchange)
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180114")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
|
||||
strategy.analyze_pair('ADA/BTC', '5m')
|
||||
return strategy.dp.get_analyzed_dataframe('ADA/BTC', '5m')
|
||||
|
||||
|
||||
def get_freqai_analyzed_dataframe(mocker, freqaiconf):
|
||||
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
|
||||
exchange = get_patched_exchange(mocker, freqaiconf)
|
||||
strategy.dp = DataProvider(freqaiconf, exchange)
|
||||
strategy.freqai_info = freqaiconf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180114")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
sub_timerange = TimeRange.parse_timerange("20180111-20180114")
|
||||
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
|
||||
|
||||
return freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, 'LTC/BTC')
|
||||
|
||||
|
||||
def get_ready_to_train(mocker, freqaiconf):
|
||||
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
|
||||
exchange = get_patched_exchange(mocker, freqaiconf)
|
||||
strategy.dp = DataProvider(freqaiconf, exchange)
|
||||
strategy.freqai_info = freqaiconf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180114")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
sub_timerange = TimeRange.parse_timerange("20180111-20180114")
|
||||
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
|
||||
return corr_df, base_df, freqai, strategy
|
||||
57
tests/freqai/test_freqai_backtesting.py
Normal file
57
tests/freqai/test_freqai_backtesting.py
Normal file
@ -0,0 +1,57 @@
|
||||
from copy import deepcopy
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
from unittest.mock import PropertyMock
|
||||
|
||||
import pytest
|
||||
|
||||
from freqtrade.commands.optimize_commands import start_backtesting
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.optimize.backtesting import Backtesting
|
||||
from tests.conftest import (CURRENT_TEST_STRATEGY, get_args, log_has_re, patch_exchange,
|
||||
patched_configuration_load_config_file)
|
||||
|
||||
|
||||
def test_freqai_backtest_start_backtest_list(freqai_conf, mocker, testdatadir):
|
||||
patch_exchange(mocker)
|
||||
|
||||
mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
|
||||
PropertyMock(return_value=['HULUMULU/USDT', 'XRP/USDT']))
|
||||
# mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock)
|
||||
|
||||
patched_configuration_load_config_file(mocker, freqai_conf)
|
||||
|
||||
args = [
|
||||
'backtesting',
|
||||
'--config', 'config.json',
|
||||
'--datadir', str(testdatadir),
|
||||
'--strategy-path', str(Path(__file__).parents[1] / 'strategy/strats'),
|
||||
'--timeframe', '1h',
|
||||
'--strategy-list', CURRENT_TEST_STRATEGY
|
||||
]
|
||||
args = get_args(args)
|
||||
with pytest.raises(OperationalException,
|
||||
match=r"You can't use strategy_list and freqai at the same time\."):
|
||||
start_backtesting(args)
|
||||
|
||||
|
||||
def test_freqai_backtest_load_data(freqai_conf, mocker, caplog):
|
||||
patch_exchange(mocker)
|
||||
|
||||
now = datetime.now(timezone.utc)
|
||||
mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
|
||||
PropertyMock(return_value=['HULUMULU/USDT', 'XRP/USDT']))
|
||||
mocker.patch('freqtrade.optimize.backtesting.history.load_data')
|
||||
mocker.patch('freqtrade.optimize.backtesting.history.get_timerange', return_value=(now, now))
|
||||
backtesting = Backtesting(deepcopy(freqai_conf))
|
||||
backtesting.load_bt_data()
|
||||
|
||||
assert log_has_re('Increasing startup_candle_count for freqai to.*', caplog)
|
||||
|
||||
del freqai_conf['freqai']['startup_candles']
|
||||
backtesting = Backtesting(freqai_conf)
|
||||
with pytest.raises(OperationalException,
|
||||
match=r'FreqAI backtesting module.*startup_candles in config.'):
|
||||
backtesting.load_bt_data()
|
||||
|
||||
Backtesting.cleanup()
|
||||
94
tests/freqai/test_freqai_datadrawer.py
Normal file
94
tests/freqai/test_freqai_datadrawer.py
Normal file
@ -0,0 +1,94 @@
|
||||
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
from freqtrade.configuration import TimeRange
|
||||
from freqtrade.data.dataprovider import DataProvider
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from tests.conftest import get_patched_exchange
|
||||
from tests.freqai.conftest import get_patched_freqai_strategy
|
||||
|
||||
|
||||
def test_update_historic_data(mocker, freqai_conf):
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180114")
|
||||
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
|
||||
dp_candles = len(strategy.dp.get_pair_dataframe("ADA/BTC", "5m"))
|
||||
candle_difference = dp_candles - historic_candles
|
||||
freqai.dd.update_historic_data(strategy, freqai.dk)
|
||||
|
||||
updated_historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
|
||||
|
||||
assert updated_historic_candles - historic_candles == candle_difference
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_load_all_pairs_histories(mocker, freqai_conf):
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180114")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
|
||||
assert len(freqai.dd.historic_data.keys()) == len(
|
||||
freqai_conf.get("exchange", {}).get("pair_whitelist")
|
||||
)
|
||||
assert len(freqai.dd.historic_data["ADA/BTC"]) == len(
|
||||
freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes")
|
||||
)
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_get_base_and_corr_dataframes(mocker, freqai_conf):
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180114")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
sub_timerange = TimeRange.parse_timerange("20180111-20180114")
|
||||
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
|
||||
|
||||
num_tfs = len(
|
||||
freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes")
|
||||
)
|
||||
|
||||
assert len(base_df.keys()) == num_tfs
|
||||
|
||||
assert len(corr_df.keys()) == len(
|
||||
freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_corr_pairlist")
|
||||
)
|
||||
|
||||
assert len(corr_df["ADA/BTC"].keys()) == num_tfs
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_use_strategy_to_populate_indicators(mocker, freqai_conf):
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180114")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
sub_timerange = TimeRange.parse_timerange("20180111-20180114")
|
||||
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
|
||||
|
||||
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, 'LTC/BTC')
|
||||
|
||||
assert len(df.columns) == 45
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
68
tests/freqai/test_freqai_datakitchen.py
Normal file
68
tests/freqai/test_freqai_datakitchen.py
Normal file
@ -0,0 +1,68 @@
|
||||
import datetime
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from tests.freqai.conftest import get_patched_data_kitchen
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"timerange, train_period_days, expected_result",
|
||||
[
|
||||
("20220101-20220201", 30, "20211202-20220201"),
|
||||
("20220301-20220401", 15, "20220214-20220401"),
|
||||
],
|
||||
)
|
||||
def test_create_fulltimerange(
|
||||
timerange, train_period_days, expected_result, freqai_conf, mocker, caplog
|
||||
):
|
||||
dk = get_patched_data_kitchen(mocker, freqai_conf)
|
||||
assert dk.create_fulltimerange(timerange, train_period_days) == expected_result
|
||||
shutil.rmtree(Path(dk.full_path))
|
||||
|
||||
|
||||
def test_create_fulltimerange_incorrect_backtest_period(mocker, freqai_conf):
|
||||
dk = get_patched_data_kitchen(mocker, freqai_conf)
|
||||
with pytest.raises(OperationalException, match=r"backtest_period_days must be an integer"):
|
||||
dk.create_fulltimerange("20220101-20220201", 0.5)
|
||||
with pytest.raises(OperationalException, match=r"backtest_period_days must be positive"):
|
||||
dk.create_fulltimerange("20220101-20220201", -1)
|
||||
shutil.rmtree(Path(dk.full_path))
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"timerange, train_period_days, backtest_period_days, expected_result",
|
||||
[
|
||||
("20220101-20220201", 30, 7, 9),
|
||||
("20220101-20220201", 30, 0.5, 120),
|
||||
("20220101-20220201", 10, 1, 80),
|
||||
],
|
||||
)
|
||||
def test_split_timerange(
|
||||
mocker, freqai_conf, timerange, train_period_days, backtest_period_days, expected_result
|
||||
):
|
||||
freqai_conf.update({"timerange": "20220101-20220401"})
|
||||
dk = get_patched_data_kitchen(mocker, freqai_conf)
|
||||
tr_list, bt_list = dk.split_timerange(timerange, train_period_days, backtest_period_days)
|
||||
assert len(tr_list) == len(bt_list) == expected_result
|
||||
|
||||
with pytest.raises(
|
||||
OperationalException, match=r"train_period_days must be an integer greater than 0."
|
||||
):
|
||||
dk.split_timerange("20220101-20220201", -1, 0.5)
|
||||
shutil.rmtree(Path(dk.full_path))
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"timestamp, expected",
|
||||
[
|
||||
(datetime.datetime.now(tz=datetime.timezone.utc).timestamp() - 7200, True),
|
||||
(datetime.datetime.now(tz=datetime.timezone.utc).timestamp(), False),
|
||||
],
|
||||
)
|
||||
def test_check_if_model_expired(mocker, freqai_conf, timestamp, expected):
|
||||
dk = get_patched_data_kitchen(mocker, freqai_conf)
|
||||
assert dk.check_if_model_expired(timestamp) == expected
|
||||
shutil.rmtree(Path(dk.full_path))
|
||||
345
tests/freqai/test_freqai_interface.py
Normal file
345
tests/freqai/test_freqai_interface.py
Normal file
@ -0,0 +1,345 @@
|
||||
import platform
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
import pytest
|
||||
|
||||
from freqtrade.configuration import TimeRange
|
||||
from freqtrade.data.dataprovider import DataProvider
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from tests.conftest import get_patched_exchange, log_has_re
|
||||
from tests.freqai.conftest import get_patched_freqai_strategy
|
||||
|
||||
|
||||
def is_arm() -> bool:
|
||||
machine = platform.machine()
|
||||
return "arm" in machine or "aarch64" in machine
|
||||
|
||||
|
||||
def test_train_model_in_series_LightGBM(mocker, freqai_conf):
|
||||
freqai_conf.update({"timerange": "20180110-20180130"})
|
||||
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
|
||||
freqai.dd.pair_dict = MagicMock()
|
||||
|
||||
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
new_timerange = TimeRange.parse_timerange("20180120-20180130")
|
||||
|
||||
freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
|
||||
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file()
|
||||
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_train_model_in_series_LightGBMMultiModel(mocker, freqai_conf):
|
||||
freqai_conf.update({"timerange": "20180110-20180130"})
|
||||
freqai_conf.update({"strategy": "freqai_test_multimodel_strat"})
|
||||
freqai_conf.update({"freqaimodel": "LightGBMRegressorMultiTarget"})
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
|
||||
freqai.dd.pair_dict = MagicMock()
|
||||
|
||||
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
new_timerange = TimeRange.parse_timerange("20180120-20180130")
|
||||
|
||||
freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
|
||||
|
||||
assert len(freqai.dk.label_list) == 2
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file()
|
||||
assert len(freqai.dk.data['training_features_list']) == 26
|
||||
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
@pytest.mark.skipif(is_arm(), reason="no ARM for Catboost ...")
|
||||
def test_train_model_in_series_Catboost(mocker, freqai_conf):
|
||||
freqai_conf.update({"timerange": "20180110-20180130"})
|
||||
freqai_conf.update({"freqaimodel": "CatboostRegressor"})
|
||||
# freqai_conf.get('freqai', {}).update(
|
||||
# {'model_training_parameters': {"n_estimators": 100, "verbose": 0}})
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
|
||||
freqai.dd.pair_dict = MagicMock()
|
||||
|
||||
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
new_timerange = TimeRange.parse_timerange("20180120-20180130")
|
||||
|
||||
freqai.train_model_in_series(new_timerange, "ADA/BTC",
|
||||
strategy, freqai.dk, data_load_timerange)
|
||||
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").exists()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").exists()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").exists()
|
||||
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
@pytest.mark.skipif(is_arm(), reason="no ARM for Catboost ...")
|
||||
def test_train_model_in_series_CatboostClassifier(mocker, freqai_conf):
|
||||
freqai_conf.update({"timerange": "20180110-20180130"})
|
||||
freqai_conf.update({"freqaimodel": "CatboostClassifier"})
|
||||
freqai_conf.update({"strategy": "freqai_test_classifier"})
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
|
||||
freqai.dd.pair_dict = MagicMock()
|
||||
|
||||
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
new_timerange = TimeRange.parse_timerange("20180120-20180130")
|
||||
|
||||
freqai.train_model_in_series(new_timerange, "ADA/BTC",
|
||||
strategy, freqai.dk, data_load_timerange)
|
||||
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").exists()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").exists()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").exists()
|
||||
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_train_model_in_series_LightGBMClassifier(mocker, freqai_conf):
|
||||
freqai_conf.update({"timerange": "20180110-20180130"})
|
||||
freqai_conf.update({"freqaimodel": "LightGBMClassifier"})
|
||||
freqai_conf.update({"strategy": "freqai_test_classifier"})
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
|
||||
freqai.dd.pair_dict = MagicMock()
|
||||
|
||||
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
new_timerange = TimeRange.parse_timerange("20180120-20180130")
|
||||
|
||||
freqai.train_model_in_series(new_timerange, "ADA/BTC",
|
||||
strategy, freqai.dk, data_load_timerange)
|
||||
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").exists()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").exists()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").exists()
|
||||
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_start_backtesting(mocker, freqai_conf):
|
||||
freqai_conf.update({"timerange": "20180120-20180130"})
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = False
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
|
||||
|
||||
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
|
||||
|
||||
metadata = {"pair": "LTC/BTC"}
|
||||
freqai.start_backtesting(df, metadata, freqai.dk)
|
||||
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
|
||||
|
||||
assert len(model_folders) == 5
|
||||
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
|
||||
freqai_conf.update({"timerange": "20180120-20180124"})
|
||||
freqai_conf.get("freqai", {}).update({"backtest_period_days": 0.5})
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = False
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
|
||||
|
||||
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
|
||||
|
||||
metadata = {"pair": "LTC/BTC"}
|
||||
freqai.start_backtesting(df, metadata, freqai.dk)
|
||||
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
|
||||
assert len(model_folders) == 8
|
||||
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
|
||||
freqai_conf.update({"timerange": "20180120-20180130"})
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = False
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
|
||||
|
||||
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
|
||||
|
||||
metadata = {"pair": "ADA/BTC"}
|
||||
freqai.start_backtesting(df, metadata, freqai.dk)
|
||||
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
|
||||
|
||||
assert len(model_folders) == 5
|
||||
|
||||
# without deleting the exiting folder structure, re-run
|
||||
|
||||
freqai_conf.update({"timerange": "20180120-20180130"})
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = False
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
|
||||
|
||||
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
|
||||
freqai.start_backtesting(df, metadata, freqai.dk)
|
||||
|
||||
assert log_has_re(
|
||||
"Found model at ",
|
||||
caplog,
|
||||
)
|
||||
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_follow_mode(mocker, freqai_conf):
|
||||
freqai_conf.update({"timerange": "20180110-20180130"})
|
||||
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
|
||||
metadata = {"pair": "ADA/BTC"}
|
||||
freqai.dd.set_pair_dict_info(metadata)
|
||||
|
||||
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
new_timerange = TimeRange.parse_timerange("20180120-20180130")
|
||||
|
||||
freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
|
||||
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file()
|
||||
|
||||
# start the follower and ask it to predict on existing files
|
||||
|
||||
freqai_conf.get("freqai", {}).update({"follow_mode": "true"})
|
||||
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.live)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
|
||||
df = strategy.dp.get_pair_dataframe('ADA/BTC', '5m')
|
||||
freqai.start_live(df, metadata, strategy, freqai.dk)
|
||||
|
||||
assert len(freqai.dk.return_dataframe.index) == 5702
|
||||
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_principal_component_analysis(mocker, freqai_conf):
|
||||
freqai_conf.update({"timerange": "20180110-20180130"})
|
||||
freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
|
||||
{"princpial_component_analysis": "true"})
|
||||
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
|
||||
freqai.dd.pair_dict = MagicMock()
|
||||
|
||||
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
new_timerange = TimeRange.parse_timerange("20180120-20180130")
|
||||
|
||||
freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
|
||||
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_pca_object.pkl")
|
||||
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
@ -12,7 +12,7 @@ from freqtrade.constants import AVAILABLE_PAIRLISTS
|
||||
from freqtrade.enums import CandleType, RunMode
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.persistence import Trade
|
||||
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
|
||||
from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist, expand_pairlist
|
||||
from freqtrade.plugins.pairlistmanager import PairListManager
|
||||
from freqtrade.resolvers import PairListResolver
|
||||
from tests.conftest import (create_mock_trades_usdt, get_patched_exchange, get_patched_freqtradebot,
|
||||
@ -1282,6 +1282,22 @@ def test_expand_pairlist(wildcardlist, pairs, expected):
|
||||
expand_pairlist(wildcardlist, pairs)
|
||||
else:
|
||||
assert sorted(expand_pairlist(wildcardlist, pairs)) == sorted(expected)
|
||||
conf = {
|
||||
'pairs': wildcardlist,
|
||||
'freqai': {
|
||||
"enabled": True,
|
||||
"feature_parameters": {
|
||||
"include_corr_pairlist": [
|
||||
"BTC/USDT:USDT",
|
||||
"XRP/BUSD",
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
assert sorted(dynamic_expand_pairlist(conf, pairs)) == sorted(expected + [
|
||||
"BTC/USDT:USDT",
|
||||
"XRP/BUSD",
|
||||
])
|
||||
|
||||
|
||||
@pytest.mark.parametrize('wildcardlist,pairs,expected', [
|
||||
|
||||
@ -1420,7 +1420,10 @@ def test_api_strategies(botclient):
|
||||
'InformativeDecoratorTest',
|
||||
'StrategyTestV2',
|
||||
'StrategyTestV3',
|
||||
'StrategyTestV3Futures'
|
||||
'StrategyTestV3Futures',
|
||||
'freqai_test_classifier',
|
||||
'freqai_test_multimodel_strat',
|
||||
'freqai_test_strat'
|
||||
]}
|
||||
|
||||
|
||||
|
||||
138
tests/strategy/strats/freqai_test_classifier.py
Normal file
138
tests/strategy/strats/freqai_test_classifier.py
Normal file
@ -0,0 +1,138 @@
|
||||
import logging
|
||||
from functools import reduce
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import talib.abstract as ta
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class freqai_test_classifier(IStrategy):
|
||||
"""
|
||||
Test strategy - used for testing freqAI functionalities.
|
||||
DO not use in production.
|
||||
"""
|
||||
|
||||
minimal_roi = {"0": 0.1, "240": -1}
|
||||
|
||||
plot_config = {
|
||||
"main_plot": {},
|
||||
"subplots": {
|
||||
"prediction": {"prediction": {"color": "blue"}},
|
||||
"target_roi": {
|
||||
"target_roi": {"color": "brown"},
|
||||
},
|
||||
"do_predict": {
|
||||
"do_predict": {"color": "brown"},
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
process_only_new_candles = True
|
||||
stoploss = -0.05
|
||||
use_exit_signal = True
|
||||
startup_candle_count: int = 300
|
||||
can_short = False
|
||||
|
||||
linear_roi_offset = DecimalParameter(
|
||||
0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
|
||||
)
|
||||
max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
|
||||
|
||||
def informative_pairs(self):
|
||||
whitelist_pairs = self.dp.current_whitelist()
|
||||
corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
|
||||
informative_pairs = []
|
||||
for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
|
||||
for pair in whitelist_pairs:
|
||||
informative_pairs.append((pair, tf))
|
||||
for pair in corr_pairs:
|
||||
if pair in whitelist_pairs:
|
||||
continue # avoid duplication
|
||||
informative_pairs.append((pair, tf))
|
||||
return informative_pairs
|
||||
|
||||
def populate_any_indicators(
|
||||
self, pair, df, tf, informative=None, set_generalized_indicators=False
|
||||
):
|
||||
|
||||
coin = pair.split('/')[0]
|
||||
|
||||
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"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
|
||||
|
||||
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
|
||||
informative[f"%-{coin}raw_volume"] = informative["volume"]
|
||||
informative[f"%-{coin}raw_price"] = informative["close"]
|
||||
|
||||
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
|
||||
|
||||
# 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-up_or_down'] = np.where(df["close"].shift(-100) > df["close"], 'up', 'down')
|
||||
|
||||
return df
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
self.freqai_info = self.config["freqai"]
|
||||
|
||||
dataframe = self.freqai.start(dataframe, metadata, self)
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
enter_long_conditions = [df['&s-up_or_down'] == 'up']
|
||||
|
||||
if enter_long_conditions:
|
||||
df.loc[
|
||||
reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
|
||||
] = (1, "long")
|
||||
|
||||
enter_short_conditions = [df['&s-up_or_down'] == 'down']
|
||||
|
||||
if enter_short_conditions:
|
||||
df.loc[
|
||||
reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
|
||||
] = (1, "short")
|
||||
|
||||
return df
|
||||
|
||||
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
return df
|
||||
165
tests/strategy/strats/freqai_test_multimodel_strat.py
Normal file
165
tests/strategy/strats/freqai_test_multimodel_strat.py
Normal file
@ -0,0 +1,165 @@
|
||||
import logging
|
||||
from functools import reduce
|
||||
|
||||
import pandas as pd
|
||||
import talib.abstract as ta
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class freqai_test_multimodel_strat(IStrategy):
|
||||
"""
|
||||
Test strategy - used for testing freqAI multimodel functionalities.
|
||||
DO not use in production.
|
||||
"""
|
||||
|
||||
minimal_roi = {"0": 0.1, "240": -1}
|
||||
|
||||
plot_config = {
|
||||
"main_plot": {},
|
||||
"subplots": {
|
||||
"prediction": {"prediction": {"color": "blue"}},
|
||||
"target_roi": {
|
||||
"target_roi": {"color": "brown"},
|
||||
},
|
||||
"do_predict": {
|
||||
"do_predict": {"color": "brown"},
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
process_only_new_candles = True
|
||||
stoploss = -0.05
|
||||
use_exit_signal = True
|
||||
startup_candle_count: int = 300
|
||||
can_short = False
|
||||
|
||||
linear_roi_offset = DecimalParameter(
|
||||
0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
|
||||
)
|
||||
max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
|
||||
|
||||
def informative_pairs(self):
|
||||
whitelist_pairs = self.dp.current_whitelist()
|
||||
corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
|
||||
informative_pairs = []
|
||||
for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
|
||||
for pair in whitelist_pairs:
|
||||
informative_pairs.append((pair, tf))
|
||||
for pair in corr_pairs:
|
||||
if pair in whitelist_pairs:
|
||||
continue # avoid duplication
|
||||
informative_pairs.append((pair, tf))
|
||||
return informative_pairs
|
||||
|
||||
def populate_any_indicators(
|
||||
self, pair, df, tf, informative=None, set_generalized_indicators=False
|
||||
):
|
||||
|
||||
coin = pair.split('/')[0]
|
||||
|
||||
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"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
|
||||
|
||||
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
|
||||
informative[f"%-{coin}raw_volume"] = informative["volume"]
|
||||
informative[f"%-{coin}raw_price"] = informative["close"]
|
||||
|
||||
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
|
||||
|
||||
# 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
|
||||
)
|
||||
|
||||
df["&-s_range"] = (
|
||||
df["close"]
|
||||
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.max()
|
||||
-
|
||||
df["close"]
|
||||
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.min()
|
||||
)
|
||||
|
||||
return df
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
self.freqai_info = self.config["freqai"]
|
||||
|
||||
dataframe = self.freqai.start(dataframe, metadata, self)
|
||||
|
||||
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
|
||||
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]]
|
||||
|
||||
if enter_long_conditions:
|
||||
df.loc[
|
||||
reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
|
||||
] = (1, "long")
|
||||
|
||||
enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"]]
|
||||
|
||||
if enter_short_conditions:
|
||||
df.loc[
|
||||
reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
|
||||
] = (1, "short")
|
||||
|
||||
return df
|
||||
|
||||
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"] * 0.25]
|
||||
if exit_long_conditions:
|
||||
df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
|
||||
|
||||
exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"] * 0.25]
|
||||
if exit_short_conditions:
|
||||
df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
|
||||
|
||||
return df
|
||||
153
tests/strategy/strats/freqai_test_strat.py
Normal file
153
tests/strategy/strats/freqai_test_strat.py
Normal file
@ -0,0 +1,153 @@
|
||||
import logging
|
||||
from functools import reduce
|
||||
|
||||
import pandas as pd
|
||||
import talib.abstract as ta
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class freqai_test_strat(IStrategy):
|
||||
"""
|
||||
Test strategy - used for testing freqAI functionalities.
|
||||
DO not use in production.
|
||||
"""
|
||||
|
||||
minimal_roi = {"0": 0.1, "240": -1}
|
||||
|
||||
plot_config = {
|
||||
"main_plot": {},
|
||||
"subplots": {
|
||||
"prediction": {"prediction": {"color": "blue"}},
|
||||
"target_roi": {
|
||||
"target_roi": {"color": "brown"},
|
||||
},
|
||||
"do_predict": {
|
||||
"do_predict": {"color": "brown"},
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
process_only_new_candles = True
|
||||
stoploss = -0.05
|
||||
use_exit_signal = True
|
||||
startup_candle_count: int = 300
|
||||
can_short = False
|
||||
|
||||
linear_roi_offset = DecimalParameter(
|
||||
0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
|
||||
)
|
||||
max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
|
||||
|
||||
def informative_pairs(self):
|
||||
whitelist_pairs = self.dp.current_whitelist()
|
||||
corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
|
||||
informative_pairs = []
|
||||
for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
|
||||
for pair in whitelist_pairs:
|
||||
informative_pairs.append((pair, tf))
|
||||
for pair in corr_pairs:
|
||||
if pair in whitelist_pairs:
|
||||
continue # avoid duplication
|
||||
informative_pairs.append((pair, tf))
|
||||
return informative_pairs
|
||||
|
||||
def populate_any_indicators(
|
||||
self, pair, df, tf, informative=None, set_generalized_indicators=False
|
||||
):
|
||||
|
||||
coin = pair.split('/')[0]
|
||||
|
||||
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"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
|
||||
|
||||
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
|
||||
informative[f"%-{coin}raw_volume"] = informative["volume"]
|
||||
informative[f"%-{coin}raw_price"] = informative["close"]
|
||||
|
||||
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
|
||||
|
||||
# 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
|
||||
)
|
||||
|
||||
return df
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
self.freqai_info = self.config["freqai"]
|
||||
|
||||
dataframe = self.freqai.start(dataframe, metadata, self)
|
||||
|
||||
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
|
||||
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]]
|
||||
|
||||
if enter_long_conditions:
|
||||
df.loc[
|
||||
reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
|
||||
] = (1, "long")
|
||||
|
||||
enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"]]
|
||||
|
||||
if enter_short_conditions:
|
||||
df.loc[
|
||||
reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
|
||||
] = (1, "short")
|
||||
|
||||
return df
|
||||
|
||||
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"] * 0.25]
|
||||
if exit_long_conditions:
|
||||
df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
|
||||
|
||||
exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"] * 0.25]
|
||||
if exit_short_conditions:
|
||||
df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
|
||||
|
||||
return df
|
||||
@ -290,6 +290,25 @@ def test_advise_all_indicators(default_conf, testdatadir) -> None:
|
||||
assert len(processed['UNITTEST/BTC']) == 102 # partial candle was removed
|
||||
|
||||
|
||||
def test_populate_any_indicators(default_conf, testdatadir) -> None:
|
||||
strategy = StrategyResolver.load_strategy(default_conf)
|
||||
|
||||
timerange = TimeRange.parse_timerange('1510694220-1510700340')
|
||||
data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange,
|
||||
fill_up_missing=True)
|
||||
processed = strategy.populate_any_indicators('UNITTEST/BTC', data, '5m')
|
||||
assert processed == data
|
||||
assert id(processed) == id(data)
|
||||
assert len(processed['UNITTEST/BTC']) == 102 # partial candle was removed
|
||||
|
||||
|
||||
def test_freqai_not_initialized(default_conf) -> None:
|
||||
strategy = StrategyResolver.load_strategy(default_conf)
|
||||
strategy.ft_bot_start()
|
||||
with pytest.raises(OperationalException, match=r'freqAI is not enabled\.'):
|
||||
strategy.freqai.start()
|
||||
|
||||
|
||||
def test_advise_all_indicators_copy(mocker, default_conf, testdatadir) -> None:
|
||||
strategy = StrategyResolver.load_strategy(default_conf)
|
||||
aimock = mocker.patch('freqtrade.strategy.interface.IStrategy.advise_indicators')
|
||||
|
||||
@ -34,7 +34,7 @@ def test_search_all_strategies_no_failed():
|
||||
directory = Path(__file__).parent / "strats"
|
||||
strategies = StrategyResolver.search_all_objects(directory, enum_failed=False)
|
||||
assert isinstance(strategies, list)
|
||||
assert len(strategies) == 6
|
||||
assert len(strategies) == 9
|
||||
assert isinstance(strategies[0], dict)
|
||||
|
||||
|
||||
@ -42,10 +42,10 @@ def test_search_all_strategies_with_failed():
|
||||
directory = Path(__file__).parent / "strats"
|
||||
strategies = StrategyResolver.search_all_objects(directory, enum_failed=True)
|
||||
assert isinstance(strategies, list)
|
||||
assert len(strategies) == 7
|
||||
assert len(strategies) == 10
|
||||
# with enum_failed=True search_all_objects() shall find 2 good strategies
|
||||
# and 1 which fails to load
|
||||
assert len([x for x in strategies if x['class'] is not None]) == 6
|
||||
assert len([x for x in strategies if x['class'] is not None]) == 9
|
||||
assert len([x for x in strategies if x['class'] is None]) == 1
|
||||
|
||||
|
||||
|
||||
0
user_data/freqaimodels/.gitkeep
Normal file
0
user_data/freqaimodels/.gitkeep
Normal file
Loading…
Reference in New Issue
Block a user