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Use joblib instead of pickle, add signal candle read/write test, move docs to new Advanced Backtesting doc

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froggleston 2022-04-20 13:38:52 +01:00
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commit b3cb722646
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75
docs/advanced-backtesting.md Normal file
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@ -0,0 +1,75 @@
# Advanced Backtesting Analysis
## Analyse the buy/entry and sell/exit tags
It can be helpful to understand how a strategy behaves according to the buy/entry tags used to
mark up different buy conditions. You might want to see more complex statistics about each buy and
sell condition above those provided by the default backtesting output. You may also want to
determine indicator values on the signal candle that resulted in a trade opening.
!!! Note
The following buy reason analysis is only available for backtesting, *not hyperopt*.
We first need to enable the exporting of trades from backtesting:
```bash
freqtrade backtesting -c <config.json> --timeframe <tf> --strategy <strategy_name> --timerange=<timerange> --export=trades --export-filename=user_data/backtest_results/<name>-<timerange>
```
To analyse the buy tags, we need to use the `freqtrade tag-analysis` command. We need the signal
candles for each opened trade so add the following option to your config file:
```
'backtest_signal_candle_export_enable': true,
```
This will tell freqtrade to output a pickled dictionary of strategy, pairs and corresponding
DataFrame of the candles that resulted in buy signals. Depending on how many buys your strategy
makes, this file may get quite large, so periodically check your `user_data/backtest_results`
folder to delete old exports.
Before running your next backtest, make sure you either delete your old backtest results or run
backtesting with the `--cache none` option to make sure no cached results are used.
If all goes well, you should now see a `backtest-result-{timestamp}_signals.pkl` file in the
`user_data/backtest_results` folder.
Now run the buy_reasons.py script, supplying a few options:
```bash
freqtrade tag-analysis -c <config.json> -s <strategy_name> -t <timerange> -g0,1,2,3,4
```
The `-g` option is used to specify the various tabular outputs, ranging from the simplest (0)
to the most detailed per pair, per buy and per sell tag (4). More options are available by
running with the `-h` option.
### Tuning the buy tags and sell tags to display
To show only certain buy and sell tags in the displayed output, use the following two options:
```
--buy_reason_list : Comma separated list of buy signals to analyse. Default: "all"
--sell_reason_list : Comma separated list of sell signals to analyse. Default: "stop_loss,trailing_stop_loss"
```
For example:
```bash
freqtrade tag-analysis -c <config.json> -s <strategy_name> -t <timerange> -g0,1,2,3,4 --buy_reason_list "buy_tag_a,buy_tag_b" --sell_reason_list "roi,custom_sell_tag_a,stop_loss"
```
### Outputting signal candle indicators
The real power of the buy_reasons.py script comes from the ability to print out the indicator
values present on signal candles to allow fine-grained investigation and tuning of buy signal
indicators. To print out a column for a given set of indicators, use the `--indicator-list`
option:
```bash
freqtrade tag-analysis -c <config.json> -s <strategy_name> -t <timerange> -g0,1,2,3,4 --buy_reason_list "buy_tag_a,buy_tag_b" --sell_reason_list "roi,custom_sell_tag_a,stop_loss" --indicator_list "rsi,rsi_1h,bb_lowerband,ema_9,macd,macdsignal"
```
The indicators have to be present in your strategy's main DataFrame (either for your main
timeframe or for informatives) otherwise they will simply be ignored in the script
output.

1
docs/data-analysis.md
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@ -122,5 +122,6 @@ Best avoid relative paths, since this starts at the storage location of the jupy
* [Strategy debugging](strategy_analysis_example.md) - also available as Jupyter notebook (`user_data/notebooks/strategy_analysis_example.ipynb`)
* [Plotting](plotting.md)
* [Tag Analysis](advanced-backtesting.md)
Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data.

72
docs/strategy_analysis_example.md
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@ -250,75 +250,3 @@ fig.show()
```
Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data.
## Analyse the buy/entry and sell/exit tags
It can be helpful to understand how a strategy behaves according to the buy/entry tags used to
mark up different buy conditions. You might want to see more complex statistics about each buy and
sell condition above those provided by the default backtesting output. You may also want to
determine indicator values on the signal candle that resulted in a trade opening.
We first need to enable the exporting of trades from backtesting:
```
freqtrade backtesting -c <config.json> --timeframe <tf> --strategy <strategy_name> --timerange=<timerange> --export=trades --export-filename=user_data/backtest_results/<name>-<timerange>
```
To analyse the buy tags, we need to use the buy_reasons.py script in the `scripts/`
folder. We need the signal candles for each opened trade so add the following option to your
config file:
```
'backtest_signal_candle_export_enable': true,
```
This will tell freqtrade to output a pickled dictionary of strategy, pairs and corresponding
DataFrame of the candles that resulted in buy signals. Depending on how many buys your strategy
makes, this file may get quite large, so periodically check your `user_data/backtest_results`
folder to delete old exports.
Before running your next backtest, make sure you either delete your old backtest results or run
backtesting with the `--cache none` option to make sure no cached results are used.
If all goes well, you should now see a `backtest-result-{timestamp}_signals.pkl` file in the
`user_data/backtest_results` folder.
Now run the buy_reasons.py script, supplying a few options:
```
./scripts/buy_reasons.py -c <config.json> -s <strategy_name> -t <timerange> -g0,1,2,3,4
```
The `-g` option is used to specify the various tabular outputs, ranging from the simplest (0)
to the most detailed per pair, per buy and per sell tag (4). More options are available by
running with the `-h` option.
### Tuning the buy tags and sell tags to display
To show only certain buy and sell tags in the displayed output, use the following two options:
```
--buy_reason_list : Comma separated list of buy signals to analyse. Default: "all"
--sell_reason_list : Comma separated list of sell signals to analyse. Default: "stop_loss,trailing_stop_loss"
```
For example:
```
./scripts/buy_reasons.py -c <config.json> -s <strategy_name> -t <timerange> -g0,1,2,3,4 --buy_reason_list "buy_tag_a,buy_tag_b" --sell_reason_list "roi,custom_sell_tag_a,stop_loss"
```
### Outputting signal candle indicators
The real power of the buy_reasons.py script comes from the ability to print out the indicator
values present on signal candles to allow fine-grained investigation and tuning of buy signal
indicators. To print out a column for a given set of indicators, use the `--indicator-list`
option:
```
./scripts/buy_reasons.py -c <config.json> -s <strategy_name> -t <timerange> -g0,1,2,3,4 --buy_reason_list "buy_tag_a,buy_tag_b" --sell_reason_list "roi,custom_sell_tag_a,stop_loss" --indicator_list "rsi,rsi_1h,bb_lowerband,ema_9,macd,macdsignal"
```
The indicators have to be present in your strategy's main dataframe (either for your main
timeframe or for informatives) otherwise they will simply be ignored in the script
output.

10
freqtrade/misc.py
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@ -4,7 +4,6 @@ Various tool function for Freqtrade and scripts
import gzip
import hashlib
import logging
import pickle
import re
from copy import deepcopy
from datetime import datetime
@ -13,6 +12,7 @@ from typing import Any, Iterator, List, Union
from typing.io import IO
from urllib.parse import urlparse
import joblib
import rapidjson
from freqtrade.constants import DECIMAL_PER_COIN_FALLBACK, DECIMALS_PER_COIN
@ -87,7 +87,7 @@ def file_dump_json(filename: Path, data: Any, is_zip: bool = False, log: bool =
logger.debug(f'done json to "{filename}"')
def file_dump_pickle(filename: Path, data: Any, log: bool = True) -> None:
def file_dump_joblib(filename: Path, data: Any, log: bool = True) -> None:
"""
Dump object data into a file
:param filename: file to create
@ -96,10 +96,10 @@ def file_dump_pickle(filename: Path, data: Any, log: bool = True) -> None:
"""
if log:
logger.info(f'dumping pickle to "{filename}"')
logger.info(f'dumping joblib to "{filename}"')
with open(filename, 'wb') as fp:
pickle.dump(data, fp)
logger.debug(f'done pickling to "{filename}"')
joblib.dump(data, fp)
logger.debug(f'done joblib dump to "{filename}"')
def json_load(datafile: IO) -> Any:

8
freqtrade/optimize/optimize_reports.py
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@ -11,7 +11,7 @@ from tabulate import tabulate
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT
from freqtrade.data.btanalysis import (calculate_csum, calculate_market_change,
calculate_max_drawdown)
from freqtrade.misc import (decimals_per_coin, file_dump_json, file_dump_pickle,
from freqtrade.misc import (decimals_per_coin, file_dump_joblib, file_dump_json,
get_backtest_metadata_filename, round_coin_value)
@ -45,7 +45,7 @@ def store_backtest_stats(recordfilename: Path, stats: Dict[str, DataFrame]) -> N
file_dump_json(latest_filename, {'latest_backtest': str(filename.name)})
def store_backtest_signal_candles(recordfilename: Path, candles: Dict[str, Dict]) -> None:
def store_backtest_signal_candles(recordfilename: Path, candles: Dict[str, Dict]) -> Path:
"""
Stores backtest trade signal candles
:param recordfilename: Path object, which can either be a filename or a directory.
@ -63,7 +63,9 @@ def store_backtest_signal_candles(recordfilename: Path, candles: Dict[str, Dict]
f'{recordfilename.stem}-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}_signals.pkl'
)
file_dump_pickle(filename, candles)
file_dump_joblib(filename, candles)
return filename
def _get_line_floatfmt(stake_currency: str) -> List[str]:

48
tests/optimize/test_optimize_reports.py
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@ -2,6 +2,7 @@ import re
from datetime import timedelta
from pathlib import Path
import joblib
import pandas as pd
import pytest
from arrow import Arrow
@ -204,23 +205,58 @@ def test_store_backtest_stats(testdatadir, mocker):
def test_store_backtest_candles(testdatadir, mocker):
dump_mock = mocker.patch('freqtrade.optimize.optimize_reports.file_dump_pickle')
dump_mock = mocker.patch('freqtrade.optimize.optimize_reports.file_dump_joblib')
# test directory exporting
store_backtest_signal_candles(testdatadir, {'DefStrat': {'UNITTEST/BTC': pd.DataFrame()}})
candle_dict = {'DefStrat': {'UNITTEST/BTC': pd.DataFrame()}}
# mock directory exporting
store_backtest_signal_candles(testdatadir, candle_dict)
assert dump_mock.call_count == 1
assert isinstance(dump_mock.call_args_list[0][0][0], Path)
assert str(dump_mock.call_args_list[0][0][0]).endswith(str('_signals.pkl'))
dump_mock.reset_mock()
# test file exporting
filename = testdatadir / 'testresult'
store_backtest_signal_candles(filename, {'DefStrat': {'UNITTEST/BTC': pd.DataFrame()}})
# mock file exporting
filename = Path(testdatadir / 'testresult')
store_backtest_signal_candles(filename, candle_dict)
assert dump_mock.call_count == 1
assert isinstance(dump_mock.call_args_list[0][0][0], Path)
# result will be testdatadir / testresult-<timestamp>_signals.pkl
assert str(dump_mock.call_args_list[0][0][0]).endswith(str('_signals.pkl'))
dump_mock.reset_mock()
def test_write_read_backtest_candles(tmpdir):
candle_dict = {'DefStrat': {'UNITTEST/BTC': pd.DataFrame()}}
# test directory exporting
stored_file = store_backtest_signal_candles(Path(tmpdir), candle_dict)
scp = open(stored_file, "rb")
pickled_signal_candles = joblib.load(scp)
scp.close()
assert pickled_signal_candles.keys() == candle_dict.keys()
assert pickled_signal_candles['DefStrat'].keys() == pickled_signal_candles['DefStrat'].keys()
assert pickled_signal_candles['DefStrat']['UNITTEST/BTC'] \
.equals(pickled_signal_candles['DefStrat']['UNITTEST/BTC'])
_clean_test_file(stored_file)
# test file exporting
filename = Path(tmpdir / 'testresult')
stored_file = store_backtest_signal_candles(filename, candle_dict)
scp = open(stored_file, "rb")
pickled_signal_candles = joblib.load(scp)
scp.close()
assert pickled_signal_candles.keys() == candle_dict.keys()
assert pickled_signal_candles['DefStrat'].keys() == pickled_signal_candles['DefStrat'].keys()
assert pickled_signal_candles['DefStrat']['UNITTEST/BTC'] \
.equals(pickled_signal_candles['DefStrat']['UNITTEST/BTC'])
_clean_test_file(stored_file)
def test_generate_pair_metrics():