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Merge pull request #1635 from freqtrade/feat/btanlaysis 

BTAnalysis - simplify backtest result analysis
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Misagh 2019-03-16 21:16:59 +01:00 committed by GitHub
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20
docs/backtesting.md
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@ -245,6 +245,26 @@ On the other hand, if you set a too high `minimal_roi` like `"0": 0.55`
profit. Hence, keep in mind that your performance is a mix of your
strategies, your configuration, and the crypto-currency you have set up.
### Further backtest-result analysis
To further analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
You can then load the trades to perform further analysis.
A good way for this is using Jupyter (notebook or lab) - which provides an interactive environment to analyze the data.
Freqtrade provides an easy to load the backtest results, which is `load_backtest_data` - and takes a path to the backtest-results file.
``` python
from freqtrade.data.btanalysis import load_backtest_data
df = load_backtest_data("user_data/backtest-result.json")
# Show value-counts per pair
df.groupby("pair")["sell_reason"].value_counts()
```
This will allow you to drill deeper into your backtest results, and perform analysis which would make the regular backtest-output unreadable.
## Backtesting multiple strategies
To backtest multiple strategies, a list of Strategies can be provided.

67
freqtrade/data/btanalysis.py Normal file
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@ -0,0 +1,67 @@
"""
Helpers when analyzing backtest data
"""
from pathlib import Path
import numpy as np
import pandas as pd
from freqtrade.misc import json_load
# must align with columns in backtest.py
BT_DATA_COLUMNS = ["pair", "profitperc", "open_time", "close_time", "index", "duration",
"open_rate", "close_rate", "open_at_end", "sell_reason"]
def load_backtest_data(filename) -> pd.DataFrame:
"""
Load backtest data file.
:param filename: pathlib.Path object, or string pointing to the file.
:return a dataframe with the analysis results
"""
if isinstance(filename, str):
filename = Path(filename)
if not filename.is_file():
raise ValueError("File {filename} does not exist.")
with filename.open() as file:
data = json_load(file)
df = pd.DataFrame(data, columns=BT_DATA_COLUMNS)
df['open_time'] = pd.to_datetime(df['open_time'],
unit='s',
utc=True,
infer_datetime_format=True
)
df['close_time'] = pd.to_datetime(df['close_time'],
unit='s',
utc=True,
infer_datetime_format=True
)
df['profitabs'] = df['close_rate'] - df['open_rate']
df = df.sort_values("open_time").reset_index(drop=True)
return df
def evaluate_result_multi(results: pd.DataFrame, freq: str, max_open_trades: int) -> pd.DataFrame:
"""
Find overlapping trades by expanding each trade once per period it was open
and then counting overlaps
:param results: Results Dataframe - can be loaded
:param freq: Frequency used for the backtest
:param max_open_trades: parameter max_open_trades used during backtest run
:return: dataframe with open-counts per time-period in freq
"""
dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time, freq=freq))
for row in results[['open_time', 'close_time']].iterrows()]
deltas = [len(x) for x in dates]
dates = pd.Series(pd.concat(dates).values, name='date')
df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0), columns=results.columns)
df2 = df2.astype(dtype={"open_time": "datetime64", "close_time": "datetime64"})
df2 = pd.concat([dates, df2], axis=1)
df2 = df2.set_index('date')
df_final = df2.resample(freq)[['pair']].count()
return df_final[df_final['pair'] > max_open_trades]

21
freqtrade/tests/data/test_btanalysis.py Normal file
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@ -0,0 +1,21 @@
import pytest
from pandas import DataFrame
from freqtrade.data.btanalysis import BT_DATA_COLUMNS, load_backtest_data
from freqtrade.data.history import make_testdata_path
def test_load_backtest_data():
filename = make_testdata_path(None) / "backtest-result_test.json"
bt_data = load_backtest_data(filename)
assert isinstance(bt_data, DataFrame)
assert list(bt_data.columns) == BT_DATA_COLUMNS + ["profitabs"]
assert len(bt_data) == 179
# Test loading from string (must yield same result)
bt_data2 = load_backtest_data(str(filename))
assert bt_data.equals(bt_data2)
with pytest.raises(ValueError, match=r"File .* does not exist\."):
load_backtest_data(str("filename") + "nofile")

16
freqtrade/tests/optimize/test_backtesting.py
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@ -14,6 +14,7 @@ from arrow import Arrow
from freqtrade import DependencyException, constants
from freqtrade.arguments import Arguments, TimeRange
from freqtrade.data import history
from freqtrade.data.btanalysis import evaluate_result_multi
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.optimize import get_timeframe
from freqtrade.optimize.backtesting import (Backtesting, setup_configuration,
@ -684,21 +685,6 @@ def test_backtest_alternate_buy_sell(default_conf, fee, mocker):
def test_backtest_multi_pair(default_conf, fee, mocker):
def evaluate_result_multi(results, freq, max_open_trades):
# Find overlapping trades by expanding each trade once per period
# and then counting overlaps
dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time, freq=freq))
for row in results[['open_time', 'close_time']].iterrows()]
deltas = [len(x) for x in dates]
dates = pd.Series(pd.concat(dates).values, name='date')
df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0), columns=results.columns)
df2 = df2.astype(dtype={"open_time": "datetime64", "close_time": "datetime64"})
df2 = pd.concat([dates, df2], axis=1)
df2 = df2.set_index('date')
df_final = df2.resample(freq)[['pair']].count()
return df_final[df_final['pair'] > max_open_trades]
def _trend_alternate_hold(dataframe=None, metadata=None):
"""
Buy every 8th candle - sell every other 8th -2 (hold on to pairs a bit)

1
freqtrade/tests/testdata/backtest-result_test.json vendored Normal file
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40
scripts/plot_dataframe.py
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@ -41,6 +41,7 @@ from plotly.offline import plot
from freqtrade import persistence
from freqtrade.arguments import Arguments, TimeRange
from freqtrade.data import history
from freqtrade.data.btanalysis import load_backtest_data, BT_DATA_COLUMNS
from freqtrade.exchange import Exchange
from freqtrade.optimize.backtesting import setup_configuration
from freqtrade.persistence import Trade
@ -56,7 +57,8 @@ def load_trades(args: Namespace, pair: str, timerange: TimeRange) -> pd.DataFram
trades: pd.DataFrame = pd.DataFrame()
if args.db_url:
persistence.init(_CONF)
columns = ["pair", "profit", "opents", "closets", "open_rate", "close_rate", "duration"]
columns = ["pair", "profit", "open_time", "close_time",
"open_rate", "close_rate", "duration"]
for x in Trade.query.all():
print("date: {}".format(x.open_date))
@ -71,33 +73,13 @@ def load_trades(args: Namespace, pair: str, timerange: TimeRange) -> pd.DataFram
columns=columns)
elif args.exportfilename:
file = Path(args.exportfilename)
# must align with columns in backtest.py
columns = ["pair", "profit", "opents", "closets", "index", "duration",
"open_rate", "close_rate", "open_at_end", "sell_reason"]
if file.exists():
with file.open() as f:
data = json.load(f)
trades = pd.DataFrame(data, columns=columns)
trades = trades.loc[trades["pair"] == pair]
if timerange:
if timerange.starttype == 'date':
trades = trades.loc[trades["opents"] >= timerange.startts]
if timerange.stoptype == 'date':
trades = trades.loc[trades["opents"] <= timerange.stopts]
trades['opents'] = pd.to_datetime(
trades['opents'],
unit='s',
utc=True,
infer_datetime_format=True)
trades['closets'] = pd.to_datetime(
trades['closets'],
unit='s',
utc=True,
infer_datetime_format=True)
file = Path(args.exportfilename)
if file.exists():
load_backtest_data(file)
else:
trades = pd.DataFrame([], columns=columns)
trades = pd.DataFrame([], columns=BT_DATA_COLUMNS)
return trades
@ -206,7 +188,7 @@ def extract_trades_of_period(dataframe, trades) -> pd.DataFrame:
Compare trades and backtested pair DataFrames to get trades performed on backtested period
:return: the DataFrame of a trades of period
"""
trades = trades.loc[trades['opents'] >= dataframe.iloc[0]['date']]
trades = trades.loc[trades['open_time'] >= dataframe.iloc[0]['date']]
return trades
@ -279,7 +261,7 @@ def generate_graph(
)
trade_buys = go.Scattergl(
x=trades["opents"],
x=trades["open_time"],
y=trades["open_rate"],
mode='markers',
name='trade_buy',
@ -291,7 +273,7 @@ def generate_graph(
)
)
trade_sells = go.Scattergl(
x=trades["closets"],
x=trades["close_time"],
y=trades["close_rate"],
mode='markers',
name='trade_sell',