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Merge pull request #6874 from froggleston/buy_reasons

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Buy reasons
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Matthias 2022-06-15 19:06:00 +02:00 committed by GitHub
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57
docs/advanced-backtesting.md
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@ -22,50 +22,79 @@ DataFrame of the candles that resulted in buy signals. Depending on how many buy
makes, this file may get quite large, so periodically check your `user_data/backtest_results`
folder to delete old exports.
To analyze the buy tags, we need to use the `buy_reasons.py` script from
[froggleston's repo](https://github.com/froggleston/freqtrade-buyreasons). Follow the instructions
in their README to copy the script into your `freqtrade/scripts/` folder.
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:
To analyze the entry/exit tags, we now need to use the `freqtrade backtesting-analysis` command
with `--analysis-groups` option provided with space-separated arguments (default `0 1 2`):
``` bash
python3 scripts/buy_reasons.py -c <config.json> -s <strategy_name> -t <timerange> -g0,1,2,3,4
freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 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.
This command will read from the last backtesting results. The `--analysis-groups` option is
used to specify the various tabular outputs showing the profit fo each group or trade,
ranging from the simplest (0) to the most detailed per pair, per buy and per sell tag (4):
* 1: profit summaries grouped by enter_tag
* 2: profit summaries grouped by enter_tag and exit_tag
* 3: profit summaries grouped by pair and enter_tag
* 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
More options are available by running with the `-h` option.
### Using export-filename
Normally, `backtesting-analysis` uses the latest backtest results, but if you wanted to go
back to a previous backtest output, you need to supply the `--export-filename` option.
You can supply the same parameter to `backtest-analysis` with the name of the final backtest
output file. This allows you to keep historical versions of backtest results and re-analyse
them at a later date:
``` bash
freqtrade backtesting -c <config.json> --timeframe <tf> --strategy <strategy_name> --timerange=<timerange> --export=signals --export-filename=/tmp/mystrat_backtest.json
```
You should see some output similar to below in the logs with the name of the timestamped
filename that was exported:
```
2022-06-14 16:28:32,698 - freqtrade.misc - INFO - dumping json to "/tmp/mystrat_backtest-2022-06-14_16-28-32.json"
```
You can then use that filename in `backtesting-analysis`:
```
freqtrade backtesting-analysis -c <config.json> --export-filename=/tmp/mystrat_backtest-2022-06-14_16-28-32.json
```
### 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:
```
--enter_reason_list : Comma separated list of enter signals to analyse. Default: "all"
--exit_reason_list : Comma separated list of exit signals to analyse. Default: "stop_loss,trailing_stop_loss"
--enter-reason-list : Space-separated list of enter signals to analyse. Default: "all"
--exit-reason-list : Space-separated list of exit signals to analyse. Default: "all"
```
For example:
```bash
python3 scripts/buy_reasons.py -c <config.json> -s <strategy_name> -t <timerange> -g0,1,2,3,4 --enter_reason_list "enter_tag_a,enter_tag_b" --exit_reason_list "roi,custom_exit_tag_a,stop_loss"
freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 2 --enter-reason-list enter_tag_a enter_tag_b --exit-reason-list roi custom_exit_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
The real power of `freqtrade backtesting-analysis` 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
python3 scripts/buy_reasons.py -c <config.json> -s <strategy_name> -t <timerange> -g0,1,2,3,4 --enter_reason_list "enter_tag_a,enter_tag_b" --exit_reason_list "roi,custom_exit_tag_a,stop_loss" --indicator_list "rsi,rsi_1h,bb_lowerband,ema_9,macd,macdsignal"
freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 2 --enter-reason-list enter_tag_a enter_tag_b --exit-reason-list roi custom_exit_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

55
docs/utils.md
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@ -651,6 +651,61 @@ Common arguments:
```
## Detailed backtest analysis
Advanced backtest result analysis.
More details in the [Backtesting analysis](advanced-backtesting.md#analyze-the-buyentry-and-sellexit-tags) Section.
```
usage: freqtrade backtesting-analysis [-h] [-v] [--logfile FILE] [-V]
[-c PATH] [-d PATH] [--userdir PATH]
[--export-filename PATH]
[--analysis-groups {0,1,2,3,4} [{0,1,2,3,4} ...]]
[--enter-reason-list ENTER_REASON_LIST [ENTER_REASON_LIST ...]]
[--exit-reason-list EXIT_REASON_LIST [EXIT_REASON_LIST ...]]
[--indicator-list INDICATOR_LIST [INDICATOR_LIST ...]]
optional arguments:
-h, --help show this help message and exit
--export-filename PATH, --backtest-filename PATH
Use this filename for backtest results.Requires
`--export` to be set as well. Example: `--export-filen
ame=user_data/backtest_results/backtest_today.json`
--analysis-groups {0,1,2,3,4} [{0,1,2,3,4} ...]
grouping output - 0: simple wins/losses by enter tag,
1: by enter_tag, 2: by enter_tag and exit_tag, 3: by
pair and enter_tag, 4: by pair, enter_ and exit_tag
(this can get quite large)
--enter-reason-list ENTER_REASON_LIST [ENTER_REASON_LIST ...]
Comma separated list of entry signals to analyse.
Default: all. e.g. 'entry_tag_a,entry_tag_b'
--exit-reason-list EXIT_REASON_LIST [EXIT_REASON_LIST ...]
Comma separated list of exit signals to analyse.
Default: all. e.g.
'exit_tag_a,roi,stop_loss,trailing_stop_loss'
--indicator-list INDICATOR_LIST [INDICATOR_LIST ...]
Comma separated list of indicators to analyse. e.g.
'close,rsi,bb_lowerband,profit_abs'
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
## List Hyperopt results
You can list the hyperoptimization epochs the Hyperopt module evaluated previously with the `hyperopt-list` sub-command.

1
freqtrade/commands/__init__.py
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@ -6,6 +6,7 @@ Contains all start-commands, subcommands and CLI Interface creation.
Note: Be careful with file-scoped imports in these subfiles.
as they are parsed on startup, nothing containing optional modules should be loaded.
"""
from freqtrade.commands.analyze_commands import start_analysis_entries_exits
from freqtrade.commands.arguments import Arguments
from freqtrade.commands.build_config_commands import start_new_config
from freqtrade.commands.data_commands import (start_convert_data, start_convert_trades,

69
freqtrade/commands/analyze_commands.py Executable file
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@ -0,0 +1,69 @@
import logging
from pathlib import Path
from typing import Any, Dict
from freqtrade.configuration import setup_utils_configuration
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
def setup_analyze_configuration(args: Dict[str, Any], method: RunMode) -> Dict[str, Any]:
"""
Prepare the configuration for the entry/exit reason analysis module
:param args: Cli args from Arguments()
:param method: Bot running mode
:return: Configuration
"""
config = setup_utils_configuration(args, method)
no_unlimited_runmodes = {
RunMode.BACKTEST: 'backtesting',
}
if method in no_unlimited_runmodes.keys():
from freqtrade.data.btanalysis import get_latest_backtest_filename
if 'exportfilename' in config:
if config['exportfilename'].is_dir():
btfile = Path(get_latest_backtest_filename(config['exportfilename']))
signals_file = f"{config['exportfilename']}/{btfile.stem}_signals.pkl"
else:
if config['exportfilename'].exists():
btfile = Path(config['exportfilename'])
signals_file = f"{btfile.parent}/{btfile.stem}_signals.pkl"
else:
raise OperationalException(f"{config['exportfilename']} does not exist.")
else:
raise OperationalException('exportfilename not in config.')
if (not Path(signals_file).exists()):
raise OperationalException(
(f"Cannot find latest backtest signals file: {signals_file}."
"Run backtesting with `--export signals`.")
)
return config
def start_analysis_entries_exits(args: Dict[str, Any]) -> None:
"""
Start analysis script
:param args: Cli args from Arguments()
:return: None
"""
from freqtrade.data.entryexitanalysis import process_entry_exit_reasons
# Initialize configuration
config = setup_analyze_configuration(args, RunMode.BACKTEST)
logger.info('Starting freqtrade in analysis mode')
process_entry_exit_reasons(config['exportfilename'],
config['exchange']['pair_whitelist'],
config['analysis_groups'],
config['enter_reason_list'],
config['exit_reason_list'],
config['indicator_list']
)

15
freqtrade/commands/arguments.py
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@ -101,6 +101,9 @@ ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperop
"print_json", "hyperoptexportfilename", "hyperopt_show_no_header",
"disableparamexport", "backtest_breakdown"]
ARGS_ANALYZE_ENTRIES_EXITS = ["exportfilename", "analysis_groups", "enter_reason_list",
"exit_reason_list", "indicator_list"]
NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
"list-markets", "list-pairs", "list-strategies", "list-data",
"hyperopt-list", "hyperopt-show", "backtest-filter",
@ -182,8 +185,9 @@ class Arguments:
self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot')
self._build_args(optionlist=['version'], parser=self.parser)
from freqtrade.commands import (start_backtesting, start_backtesting_show,
start_convert_data, start_convert_db, start_convert_trades,
from freqtrade.commands import (start_analysis_entries_exits, start_backtesting,
start_backtesting_show, start_convert_data,
start_convert_db, start_convert_trades,
start_create_userdir, start_download_data, start_edge,
start_hyperopt, start_hyperopt_list, start_hyperopt_show,
start_install_ui, start_list_data, start_list_exchanges,
@ -283,6 +287,13 @@ class Arguments:
backtesting_show_cmd.set_defaults(func=start_backtesting_show)
self._build_args(optionlist=ARGS_BACKTEST_SHOW, parser=backtesting_show_cmd)
# Add backtesting analysis subcommand
analysis_cmd = subparsers.add_parser('backtesting-analysis',
help='Backtest Analysis module.',
parents=[_common_parser])
analysis_cmd.set_defaults(func=start_analysis_entries_exits)
self._build_args(optionlist=ARGS_ANALYZE_ENTRIES_EXITS, parser=analysis_cmd)
# Add edge subcommand
edge_cmd = subparsers.add_parser('edge', help='Edge module.',
parents=[_common_parser, _strategy_parser])

33
freqtrade/commands/cli_options.py
View File

@ -614,4 +614,37 @@ AVAILABLE_CLI_OPTIONS = {
"that do not contain any parameters."),
action="store_true",
),
"analysis_groups": Arg(
"--analysis-groups",
help=("grouping output - "
"0: simple wins/losses by enter tag, "
"1: by enter_tag, "
"2: by enter_tag and exit_tag, "
"3: by pair and enter_tag, "
"4: by pair, enter_ and exit_tag (this can get quite large)"),
nargs='+',
default=['0', '1', '2'],
choices=['0', '1', '2', '3', '4'],
),
"enter_reason_list": Arg(
"--enter-reason-list",
help=("Comma separated list of entry signals to analyse. Default: all. "
"e.g. 'entry_tag_a,entry_tag_b'"),
nargs='+',
default=['all'],
),
"exit_reason_list": Arg(
"--exit-reason-list",
help=("Comma separated list of exit signals to analyse. Default: all. "
"e.g. 'exit_tag_a,roi,stop_loss,trailing_stop_loss'"),
nargs='+',
default=['all'],
),
"indicator_list": Arg(
"--indicator-list",
help=("Comma separated list of indicators to analyse. "
"e.g. 'close,rsi,bb_lowerband,profit_abs'"),
nargs='+',
default=[],
),
}

15
freqtrade/configuration/configuration.py
View File

@ -95,6 +95,8 @@ class Configuration:
self._process_data_options(config)
self._process_analyze_options(config)
# Check if the exchange set by the user is supported
check_exchange(config, config.get('experimental', {}).get('block_bad_exchanges', True))
@ -433,6 +435,19 @@ class Configuration:
self._args_to_config(config, argname='candle_types',
logstring='Detected --candle-types: {}')
def _process_analyze_options(self, config: Dict[str, Any]) -> None:
self._args_to_config(config, argname='analysis_groups',
logstring='Analysis reason groups: {}')
self._args_to_config(config, argname='enter_reason_list',
logstring='Analysis enter tag list: {}')
self._args_to_config(config, argname='exit_reason_list',
logstring='Analysis exit tag list: {}')
self._args_to_config(config, argname='indicator_list',
logstring='Analysis indicator list: {}')
def _process_runmode(self, config: Dict[str, Any]) -> None:
self._args_to_config(config, argname='dry_run',

227
freqtrade/data/entryexitanalysis.py Executable file
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@ -0,0 +1,227 @@
import logging
from pathlib import Path
from typing import List, Optional
import joblib
import pandas as pd
from tabulate import tabulate
from freqtrade.data.btanalysis import (get_latest_backtest_filename, load_backtest_data,
load_backtest_stats)
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
def _load_signal_candles(backtest_dir: Path):
if backtest_dir.is_dir():
scpf = Path(backtest_dir,
Path(get_latest_backtest_filename(backtest_dir)).stem + "_signals.pkl"
)
else:
scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_signals.pkl")
try:
scp = open(scpf, "rb")
signal_candles = joblib.load(scp)
logger.info(f"Loaded signal candles: {str(scpf)}")
except Exception as e:
logger.error("Cannot load signal candles from pickled results: ", e)
return signal_candles
def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_candles):
analysed_trades_dict = {}
analysed_trades_dict[strategy_name] = {}
try:
logger.info(f"Processing {strategy_name} : {len(pairlist)} pairs")
for pair in pairlist:
if pair in signal_candles[strategy_name]:
analysed_trades_dict[strategy_name][pair] = _analyze_candles_and_indicators(
pair,
trades,
signal_candles[strategy_name][pair])
except Exception as e:
print(f"Cannot process entry/exit reasons for {strategy_name}: ", e)
return analysed_trades_dict
def _analyze_candles_and_indicators(pair, trades, signal_candles):
buyf = signal_candles
if len(buyf) > 0:
buyf = buyf.set_index('date', drop=False)
trades_red = trades.loc[trades['pair'] == pair].copy()
trades_inds = pd.DataFrame()
if trades_red.shape[0] > 0 and buyf.shape[0] > 0:
for t, v in trades_red.open_date.items():
allinds = buyf.loc[(buyf['date'] < v)]
if allinds.shape[0] > 0:
tmp_inds = allinds.iloc[[-1]]
trades_red.loc[t, 'signal_date'] = tmp_inds['date'].values[0]
trades_red.loc[t, 'enter_reason'] = trades_red.loc[t, 'enter_tag']
tmp_inds.index.rename('signal_date', inplace=True)
trades_inds = pd.concat([trades_inds, tmp_inds])
if 'signal_date' in trades_red:
trades_red['signal_date'] = pd.to_datetime(trades_red['signal_date'], utc=True)
trades_red.set_index('signal_date', inplace=True)
try:
trades_red = pd.merge(trades_red, trades_inds, on='signal_date', how='outer')
except Exception as e:
raise e
return trades_red
else:
return pd.DataFrame()
def _do_group_table_output(bigdf, glist):
for g in glist:
# 0: summary wins/losses grouped by enter tag
if g == "0":
group_mask = ['enter_reason']
wins = bigdf.loc[bigdf['profit_abs'] >= 0] \
.groupby(group_mask) \
.agg({'profit_abs': ['sum']})
wins.columns = ['profit_abs_wins']
loss = bigdf.loc[bigdf['profit_abs'] < 0] \
.groupby(group_mask) \
.agg({'profit_abs': ['sum']})
loss.columns = ['profit_abs_loss']
new = bigdf.groupby(group_mask).agg({'profit_abs': [
'count',
lambda x: sum(x > 0),
lambda x: sum(x <= 0)]})
new = pd.concat([new, wins, loss], axis=1).fillna(0)
new['profit_tot'] = new['profit_abs_wins'] - abs(new['profit_abs_loss'])
new['wl_ratio_pct'] = (new.iloc[:, 1] / new.iloc[:, 0] * 100).fillna(0)
new['avg_win'] = (new['profit_abs_wins'] / new.iloc[:, 1]).fillna(0)
new['avg_loss'] = (new['profit_abs_loss'] / new.iloc[:, 2]).fillna(0)
new.columns = ['total_num_buys', 'wins', 'losses', 'profit_abs_wins', 'profit_abs_loss',
'profit_tot', 'wl_ratio_pct', 'avg_win', 'avg_loss']
sortcols = ['total_num_buys']
_print_table(new, sortcols, show_index=True)
else:
agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'],
'profit_ratio': ['sum', 'median', 'mean']}
agg_cols = ['num_buys', 'profit_abs_sum', 'profit_abs_median',
'profit_abs_mean', 'median_profit_pct', 'mean_profit_pct',
'total_profit_pct']
sortcols = ['profit_abs_sum', 'enter_reason']
# 1: profit summaries grouped by enter_tag
if g == "1":
group_mask = ['enter_reason']
# 2: profit summaries grouped by enter_tag and exit_tag
if g == "2":
group_mask = ['enter_reason', 'exit_reason']
# 3: profit summaries grouped by pair and enter_tag
if g == "3":
group_mask = ['pair', 'enter_reason']
# 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
if g == "4":
group_mask = ['pair', 'enter_reason', 'exit_reason']
if group_mask:
new = bigdf.groupby(group_mask).agg(agg_mask).reset_index()
new.columns = group_mask + agg_cols
new['median_profit_pct'] = new['median_profit_pct'] * 100
new['mean_profit_pct'] = new['mean_profit_pct'] * 100
new['total_profit_pct'] = new['total_profit_pct'] * 100
_print_table(new, sortcols)
else:
logger.warning("Invalid group mask specified.")
def _print_results(analysed_trades, stratname, analysis_groups,
enter_reason_list, exit_reason_list,
indicator_list, columns=None):
if columns is None:
columns = ['pair', 'open_date', 'close_date', 'profit_abs', 'enter_reason', 'exit_reason']
bigdf = pd.DataFrame()
for pair, trades in analysed_trades[stratname].items():
bigdf = pd.concat([bigdf, trades], ignore_index=True)
if bigdf.shape[0] > 0 and ('enter_reason' in bigdf.columns):
if analysis_groups:
_do_group_table_output(bigdf, analysis_groups)
if enter_reason_list and "all" not in enter_reason_list:
bigdf = bigdf.loc[(bigdf['enter_reason'].isin(enter_reason_list))]
if exit_reason_list and "all" not in exit_reason_list:
bigdf = bigdf.loc[(bigdf['exit_reason'].isin(exit_reason_list))]
if "all" in indicator_list:
print(bigdf)
elif indicator_list is not None:
available_inds = []
for ind in indicator_list:
if ind in bigdf:
available_inds.append(ind)
ilist = ["pair", "enter_reason", "exit_reason"] + available_inds
_print_table(bigdf[ilist], sortcols=['exit_reason'], show_index=False)
else:
print("\\_ No trades to show")
def _print_table(df, sortcols=None, show_index=False):
if (sortcols is not None):
data = df.sort_values(sortcols)
else:
data = df
print(
tabulate(
data,
headers='keys',
tablefmt='psql',
showindex=show_index
)
)
def process_entry_exit_reasons(backtest_dir: Path,
pairlist: List[str],
analysis_groups: Optional[List[str]] = ["0", "1", "2"],
enter_reason_list: Optional[List[str]] = ["all"],
exit_reason_list: Optional[List[str]] = ["all"],
indicator_list: Optional[List[str]] = []):
try:
backtest_stats = load_backtest_stats(backtest_dir)
for strategy_name, results in backtest_stats['strategy'].items():
trades = load_backtest_data(backtest_dir, strategy_name)
if not trades.empty:
signal_candles = _load_signal_candles(backtest_dir)
analysed_trades_dict = _process_candles_and_indicators(pairlist, strategy_name,
trades, signal_candles)
_print_results(analysed_trades_dict,
strategy_name,
analysis_groups,
enter_reason_list,
exit_reason_list,
indicator_list)
except ValueError as e:
raise OperationalException(e) from e

191
tests/data/test_entryexitanalysis.py Executable file
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@ -0,0 +1,191 @@
import logging
from unittest.mock import MagicMock, PropertyMock
import pandas as pd
import pytest
from freqtrade.commands.analyze_commands import start_analysis_entries_exits
from freqtrade.commands.optimize_commands import start_backtesting
from freqtrade.enums import ExitType
from freqtrade.optimize.backtesting import Backtesting
from tests.conftest import get_args, patch_exchange, patched_configuration_load_config_file
@pytest.fixture(autouse=True)
def entryexitanalysis_cleanup() -> None:
yield None
Backtesting.cleanup()
def test_backtest_analysis_nomock(default_conf, mocker, caplog, testdatadir, tmpdir, capsys):
caplog.set_level(logging.INFO)
default_conf.update({
"use_exit_signal": True,
"exit_profit_only": False,
"exit_profit_offset": 0.0,
"ignore_roi_if_entry_signal": False,
})
patch_exchange(mocker)
result1 = pd.DataFrame({'pair': ['ETH/BTC', 'LTC/BTC', 'ETH/BTC', 'LTC/BTC'],
'profit_ratio': [0.025, 0.05, -0.1, -0.05],
'profit_abs': [0.5, 2.0, -4.0, -2.0],
'open_date': pd.to_datetime(['2018-01-29 18:40:00',
'2018-01-30 03:30:00',
'2018-01-30 08:10:00',
'2018-01-31 13:30:00', ], utc=True
),
'close_date': pd.to_datetime(['2018-01-29 20:45:00',
'2018-01-30 05:35:00',
'2018-01-30 09:10:00',
'2018-01-31 15:00:00', ], utc=True),
'trade_duration': [235, 40, 60, 90],
'is_open': [False, False, False, False],
'stake_amount': [0.01, 0.01, 0.01, 0.01],
'open_rate': [0.104445, 0.10302485, 0.10302485, 0.10302485],
'close_rate': [0.104969, 0.103541, 0.102041, 0.102541],
"is_short": [False, False, False, False],
'enter_tag': ["enter_tag_long_a",
"enter_tag_long_b",
"enter_tag_long_a",
"enter_tag_long_b"],
'exit_reason': [ExitType.ROI,
ExitType.EXIT_SIGNAL,
ExitType.STOP_LOSS,
ExitType.TRAILING_STOP_LOSS]
})
backtestmock = MagicMock(side_effect=[
{
'results': result1,
'config': default_conf,
'locks': [],
'rejected_signals': 20,
'timedout_entry_orders': 0,
'timedout_exit_orders': 0,
'canceled_trade_entries': 0,
'canceled_entry_orders': 0,
'replaced_entry_orders': 0,
'final_balance': 1000,
}
])
mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
PropertyMock(return_value=['ETH/BTC', 'LTC/BTC', 'DASH/BTC']))
mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock)
patched_configuration_load_config_file(mocker, default_conf)
args = [
'backtesting',
'--config', 'config.json',
'--datadir', str(testdatadir),
'--user-data-dir', str(tmpdir),
'--timeframe', '5m',
'--timerange', '1515560100-1517287800',
'--export', 'signals',
'--cache', 'none',
]
args = get_args(args)
start_backtesting(args)
captured = capsys.readouterr()
assert 'BACKTESTING REPORT' in captured.out
assert 'EXIT REASON STATS' in captured.out
assert 'LEFT OPEN TRADES REPORT' in captured.out
base_args = [
'backtesting-analysis',
'--config', 'config.json',
'--datadir', str(testdatadir),
'--user-data-dir', str(tmpdir),
]
# test group 0 and indicator list
args = get_args(base_args +
['--analysis-groups', "0",
'--indicator-list', "close", "rsi", "profit_abs"]
)
start_analysis_entries_exits(args)
captured = capsys.readouterr()
assert 'LTC/BTC' in captured.out
assert 'ETH/BTC' in captured.out
assert 'enter_tag_long_a' in captured.out
assert 'enter_tag_long_b' in captured.out
assert 'exit_signal' in captured.out
assert 'roi' in captured.out
assert 'stop_loss' in captured.out
assert 'trailing_stop_loss' in captured.out
assert '0.5' in captured.out
assert '-4' in captured.out
assert '-2' in captured.out
assert '-3.5' in captured.out
assert '50' in captured.out
assert '0' in captured.out
assert '0.01616' in captured.out
assert '34.049' in captured.out
assert '0.104104' in captured.out
assert '47.0996' in captured.out
# test group 1
args = get_args(base_args + ['--analysis-groups', "1"])
start_analysis_entries_exits(args)
captured = capsys.readouterr()
assert 'enter_tag_long_a' in captured.out
assert 'enter_tag_long_b' in captured.out
assert 'total_profit_pct' in captured.out
assert '-3.5' in captured.out
assert '-1.75' in captured.out
assert '-7.5' in captured.out
assert '-3.75' in captured.out
assert '0' in captured.out
# test group 2
args = get_args(base_args + ['--analysis-groups', "2"])
start_analysis_entries_exits(args)
captured = capsys.readouterr()
assert 'enter_tag_long_a' in captured.out
assert 'enter_tag_long_b' in captured.out
assert 'exit_signal' in captured.out
assert 'roi' in captured.out
assert 'stop_loss' in captured.out
assert 'trailing_stop_loss' in captured.out
assert 'total_profit_pct' in captured.out
assert '-10' in captured.out
assert '-5' in captured.out
assert '2.5' in captured.out
# test group 3
args = get_args(base_args + ['--analysis-groups', "3"])
start_analysis_entries_exits(args)
captured = capsys.readouterr()
assert 'LTC/BTC' in captured.out
assert 'ETH/BTC' in captured.out
assert 'enter_tag_long_a' in captured.out
assert 'enter_tag_long_b' in captured.out
assert 'total_profit_pct' in captured.out
assert '-7.5' in captured.out
assert '-3.75' in captured.out
assert '-1.75' in captured.out
assert '0' in captured.out
assert '2' in captured.out
# test group 4
args = get_args(base_args + ['--analysis-groups', "4"])
start_analysis_entries_exits(args)
captured = capsys.readouterr()
assert 'LTC/BTC' in captured.out
assert 'ETH/BTC' in captured.out
assert 'enter_tag_long_a' in captured.out
assert 'enter_tag_long_b' in captured.out
assert 'exit_signal' in captured.out
assert 'roi' in captured.out
assert 'stop_loss' in captured.out
assert 'trailing_stop_loss' in captured.out
assert 'total_profit_pct' in captured.out
assert '-10' in captured.out
assert '-5' in captured.out
assert '-4' in captured.out
assert '0.5' in captured.out
assert '1' in captured.out
assert '2.5' in captured.out

4
tests/rpc/test_rpc_apiserver.py
View File

@ -1384,12 +1384,14 @@ def test_api_strategies(botclient):
rc = client_get(client, f"{BASE_URI}/strategies")
assert_response(rc)
assert rc.json() == {'strategies': [
'HyperoptableStrategy',
'InformativeDecoratorTest',
'StrategyTestV2',
'StrategyTestV3',
'StrategyTestV3Futures',
'StrategyTestV3Analysis',
'StrategyTestV3Futures'
]}

175
tests/strategy/strats/strategy_test_v3_analysis.py Normal file
View File

@ -0,0 +1,175 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
import talib.abstract as ta
from pandas import DataFrame
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy import (BooleanParameter, DecimalParameter, IntParameter, IStrategy,
RealParameter)
class StrategyTestV3Analysis(IStrategy):
"""
Strategy used by tests freqtrade bot.
Please do not modify this strategy, it's intended for internal use only.
Please look at the SampleStrategy in the user_data/strategy directory
or strategy repository https://github.com/freqtrade/freqtrade-strategies
for samples and inspiration.
"""
INTERFACE_VERSION = 3
# Minimal ROI designed for the strategy
minimal_roi = {
"40": 0.0,
"30": 0.01,
"20": 0.02,
"0": 0.04
}
# Optimal stoploss designed for the strategy
stoploss = -0.10
# Optimal timeframe for the strategy
timeframe = '5m'
# Optional order type mapping
order_types = {
'entry': 'limit',
'exit': 'limit',
'stoploss': 'limit',
'stoploss_on_exchange': False
}
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 20
# Optional time in force for orders
order_time_in_force = {
'entry': 'gtc',
'exit': 'gtc',
}
buy_params = {
'buy_rsi': 35,
# Intentionally not specified, so "default" is tested
# 'buy_plusdi': 0.4
}
sell_params = {
'sell_rsi': 74,
'sell_minusdi': 0.4
}
buy_rsi = IntParameter([0, 50], default=30, space='buy')
buy_plusdi = RealParameter(low=0, high=1, default=0.5, space='buy')
sell_rsi = IntParameter(low=50, high=100, default=70, space='sell')
sell_minusdi = DecimalParameter(low=0, high=1, default=0.5001, decimals=3, space='sell',
load=False)
protection_enabled = BooleanParameter(default=True)
protection_cooldown_lookback = IntParameter([0, 50], default=30)
# TODO: Can this work with protection tests? (replace HyperoptableStrategy implicitly ... )
# @property
# def protections(self):
# prot = []
# if self.protection_enabled.value:
# prot.append({
# "method": "CooldownPeriod",
# "stop_duration_candles": self.protection_cooldown_lookback.value
# })
# return prot
bot_started = False
def bot_start(self):
self.bot_started = True
def informative_pairs(self):
return []
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Momentum Indicator
# ------------------------------------
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# Minus Directional Indicator / Movement
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Plus Directional Indicator / Movement
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
# EMA - Exponential Moving Average
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['rsi'] < self.buy_rsi.value) &
(dataframe['fastd'] < 35) &
(dataframe['adx'] > 30) &
(dataframe['plus_di'] > self.buy_plusdi.value)
) |
(
(dataframe['adx'] > 65) &
(dataframe['plus_di'] > self.buy_plusdi.value)
),
['enter_long', 'enter_tag']] = 1, 'enter_tag_long'
dataframe.loc[
(
qtpylib.crossed_below(dataframe['rsi'], self.sell_rsi.value)
),
['enter_short', 'enter_tag']] = 1, 'enter_tag_short'
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
(qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) |
(qtpylib.crossed_above(dataframe['fastd'], 70))
) &
(dataframe['adx'] > 10) &
(dataframe['minus_di'] > 0)
) |
(
(dataframe['adx'] > 70) &
(dataframe['minus_di'] > self.sell_minusdi.value)
),
['exit_long', 'exit_tag']] = 1, 'exit_tag_long'
dataframe.loc[
(
qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)
),
['exit_long', 'exit_tag']] = 1, 'exit_tag_short'
return dataframe

6
tests/strategy/test_strategy_loading.py
View File

@ -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) == 5
assert len(strategies) == 6
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) == 6
assert len(strategies) == 7
# 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]) == 5
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 None]) == 1