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Merge pull request #3558 from freqtrade/bt_add_maxdrawdown 

Revise backtesting export format, add some metrics
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Matthias 2020-08-19 06:39:47 +02:00 committed by GitHub
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81
docs/backtesting.md
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@ -157,17 +157,32 @@ A backtesting result will look like that:
| ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 | 0 | 0 | | ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 | 0 | 0 |
| LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 | 0 | 0 | | LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 | 0 | 0 |
| TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 | 0 | 0 | | TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 | 0 | 0 |
=============== SUMMARY METRICS ===============
| Metric | Value |
|-----------------------+---------------------|
| Backtesting from | 2019-01-01 00:00:00 |
| Backtesting to | 2019-05-01 00:00:00 |
| Total trades | 429 |
| First trade | 2019-01-01 18:30:00 |
| First trade Pair | EOS/USDT |
| Total Profit % | 152.41% |
| Trades per day | 3.575 |
| Best day | 25.27% |
| Worst day | -30.67% |
| Avg. Duration Winners | 4:23:00 |
| Avg. Duration Loser | 6:55:00 |
| | |
| Max Drawdown | 50.63% |
| Drawdown Start | 2019-02-15 14:10:00 |
| Drawdown End | 2019-04-11 18:15:00 |
| Market change | -5.88% |
===============================================
``` ```
### Backtesting report table
The 1st table contains all trades the bot made, including "left open trades". The 1st table contains all trades the bot made, including "left open trades".
The 2nd table contains a recap of sell reasons.
This table can tell you which area needs some additional work (i.e. all `sell_signal` trades are losses, so we should disable the sell-signal or work on improving that).
The 3rd table contains all trades the bot had to `forcesell` at the end of the backtest period to present a full picture.
This is necessary to simulate realistic behaviour, since the backtest period has to end at some point, while realistically, you could leave the bot running forever.
These trades are also included in the first table, but are extracted separately for clarity.
The last line will give you the overall performance of your strategy, The last line will give you the overall performance of your strategy,
here: here:
@ -196,6 +211,58 @@ On the other hand, if you set a too high `minimal_roi` like `"0": 0.55`
(55%), there is almost no chance that the bot will ever reach this profit. (55%), there is almost no chance that the bot will ever reach this profit.
Hence, keep in mind that your performance is an integral mix of all different elements of the strategy, your configuration, and the crypto-currency pairs you have set up. Hence, keep in mind that your performance is an integral mix of all different elements of the strategy, your configuration, and the crypto-currency pairs you have set up.
### Sell reasons table
The 2nd table contains a recap of sell reasons.
This table can tell you which area needs some additional work (e.g. all or many of the `sell_signal` trades are losses, so you should work on improving the sell signal, or consider disabling it).
### Left open trades table
The 3rd table contains all trades the bot had to `forcesell` at the end of the backtesting period to present you the full picture.
This is necessary to simulate realistic behavior, since the backtest period has to end at some point, while realistically, you could leave the bot running forever.
These trades are also included in the first table, but are also shown separately in this table for clarity.
### Summary metrics
The last element of the backtest report is the summary metrics table.
It contains some useful key metrics about performance of your strategy on backtesting data.
```
=============== SUMMARY METRICS ===============
| Metric | Value |
|-----------------------+---------------------|
| Backtesting from | 2019-01-01 00:00:00 |
| Backtesting to | 2019-05-01 00:00:00 |
| Total trades | 429 |
| First trade | 2019-01-01 18:30:00 |
| First trade Pair | EOS/USDT |
| Total Profit % | 152.41% |
| Trades per day | 3.575 |
| Best day | 25.27% |
| Worst day | -30.67% |
| Avg. Duration Winners | 4:23:00 |
| Avg. Duration Loser | 6:55:00 |
| | |
| Max Drawdown | 50.63% |
| Drawdown Start | 2019-02-15 14:10:00 |
| Drawdown End | 2019-04-11 18:15:00 |
| Market change | -5.88% |
===============================================
```
- `Total trades`: Identical to the total trades of the backtest output table.
- `First trade`: First trade entered.
- `First trade pair`: Which pair was part of the first trade.
- `Backtesting from` / `Backtesting to`: Backtesting range (usually defined with the `--timerange` option).
- `Total Profit %`: Total profit per stake amount. Aligned to the TOTAL column of the first table.
- `Trades per day`: Total trades divided by the backtesting duration in days (this will give you information about how many trades to expect from the strategy).
- `Best day` / `Worst day`: Best and worst day based on daily profit.
- `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
- `Max Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced).
- `Drawdown Start` / `Drawdown End`: Start and end datetimes for this largest drawdown (can also be visualized via the `plot-dataframe` sub-command).
- `Market change`: Change of the market during the backtest period. Calculated as average of all pairs changes from the first to the last candle using the "close" column.
### Assumptions made by backtesting ### Assumptions made by backtesting
Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions: Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:

10
docs/plotting.md
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@ -224,7 +224,8 @@ Possible options for the `freqtrade plot-profit` subcommand:
``` ```
usage: freqtrade plot-profit [-h] [-v] [--logfile FILE] [-V] [-c PATH] usage: freqtrade plot-profit [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH] [-p PAIRS [PAIRS ...]] [-d PATH] [--userdir PATH] [-s NAME]
[--strategy-path PATH] [-p PAIRS [PAIRS ...]]
[--timerange TIMERANGE] [--export EXPORT] [--timerange TIMERANGE] [--export EXPORT]
[--export-filename PATH] [--db-url PATH] [--export-filename PATH] [--db-url PATH]
[--trade-source {DB,file}] [-i TIMEFRAME] [--trade-source {DB,file}] [-i TIMEFRAME]
@ -270,6 +271,11 @@ Common arguments:
--userdir PATH, --user-data-dir PATH --userdir PATH, --user-data-dir PATH
Path to userdata directory. Path to userdata directory.
Strategy arguments:
-s NAME, --strategy NAME
Specify strategy class name which will be used by the
bot.
--strategy-path PATH Specify additional strategy lookup path.
``` ```
The `-p/--pairs` argument, can be used to limit the pairs that are considered for this calculation. The `-p/--pairs` argument, can be used to limit the pairs that are considered for this calculation.
@ -279,7 +285,7 @@ Examples:
Use custom backtest-export file Use custom backtest-export file
``` bash ``` bash
freqtrade plot-profit -p LTC/BTC --export-filename user_data/backtest_results/backtest-result-Strategy005.json freqtrade plot-profit -p LTC/BTC --export-filename user_data/backtest_results/backtest-result.json
``` ```
Use custom database Use custom database

40
docs/strategy_analysis_example.md
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@ -85,10 +85,44 @@ Analyze a trades dataframe (also used below for plotting)
```python ```python
from freqtrade.data.btanalysis import load_backtest_data from freqtrade.data.btanalysis import load_backtest_data, load_backtest_stats
# Load backtest results # if backtest_dir points to a directory, it'll automatically load the last backtest file.
trades = load_backtest_data(config["user_data_dir"] / "backtest_results/backtest-result.json") backtest_dir = config["user_data_dir"] / "backtest_results"
# backtest_dir can also point to a specific file
# backtest_dir = config["user_data_dir"] / "backtest_results/backtest-result-2020-07-01_20-04-22.json"
```
```python
# You can get the full backtest statistics by using the following command.
# This contains all information used to generate the backtest result.
stats = load_backtest_stats(backtest_dir)
strategy = 'SampleStrategy'
# All statistics are available per strategy, so if `--strategy-list` was used during backtest, this will be reflected here as well.
# Example usages:
print(stats['strategy'][strategy]['results_per_pair'])
# Get pairlist used for this backtest
print(stats['strategy'][strategy]['pairlist'])
# Get market change (average change of all pairs from start to end of the backtest period)
print(stats['strategy'][strategy]['market_change'])
# Maximum drawdown ()
print(stats['strategy'][strategy]['max_drawdown'])
# Maximum drawdown start and end
print(stats['strategy'][strategy]['drawdown_start'])
print(stats['strategy'][strategy]['drawdown_end'])
# Get strategy comparison (only relevant if multiple strategies were compared)
print(stats['strategy_comparison'])
```
```python
# Load backtested trades as dataframe
trades = load_backtest_data(backtest_dir)
# Show value-counts per pair # Show value-counts per pair
trades.groupby("pair")["sell_reason"].value_counts() trades.groupby("pair")["sell_reason"].value_counts()

2
freqtrade/commands/arguments.py
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@ -366,7 +366,7 @@ class Arguments:
plot_profit_cmd = subparsers.add_parser( plot_profit_cmd = subparsers.add_parser(
'plot-profit', 'plot-profit',
help='Generate plot showing profits.', help='Generate plot showing profits.',
parents=[_common_parser], parents=[_common_parser, _strategy_parser],
) )
plot_profit_cmd.set_defaults(func=start_plot_profit) plot_profit_cmd.set_defaults(func=start_plot_profit)
self._build_args(optionlist=ARGS_PLOT_PROFIT, parser=plot_profit_cmd) self._build_args(optionlist=ARGS_PLOT_PROFIT, parser=plot_profit_cmd)

2
freqtrade/configuration/configuration.py
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@ -199,7 +199,7 @@ class Configuration:
config['exportfilename'] = Path(config['exportfilename']) config['exportfilename'] = Path(config['exportfilename'])
else: else:
config['exportfilename'] = (config['user_data_dir'] config['exportfilename'] = (config['user_data_dir']
/ 'backtest_results/backtest-result.json') / 'backtest_results')
def _process_optimize_options(self, config: Dict[str, Any]) -> None: def _process_optimize_options(self, config: Dict[str, Any]) -> None:

3
freqtrade/constants.py
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@ -26,12 +26,15 @@ AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
'ShuffleFilter', 'SpreadFilter'] 'ShuffleFilter', 'SpreadFilter']
AVAILABLE_DATAHANDLERS = ['json', 'jsongz'] AVAILABLE_DATAHANDLERS = ['json', 'jsongz']
DRY_RUN_WALLET = 1000 DRY_RUN_WALLET = 1000
DATETIME_PRINT_FORMAT = '%Y-%m-%d %H:%M:%S'
MATH_CLOSE_PREC = 1e-14 # Precision used for float comparisons MATH_CLOSE_PREC = 1e-14 # Precision used for float comparisons
DEFAULT_DATAFRAME_COLUMNS = ['date', 'open', 'high', 'low', 'close', 'volume'] DEFAULT_DATAFRAME_COLUMNS = ['date', 'open', 'high', 'low', 'close', 'volume']
# Don't modify sequence of DEFAULT_TRADES_COLUMNS # Don't modify sequence of DEFAULT_TRADES_COLUMNS
# it has wide consequences for stored trades files # it has wide consequences for stored trades files
DEFAULT_TRADES_COLUMNS = ['timestamp', 'id', 'type', 'side', 'price', 'amount', 'cost'] DEFAULT_TRADES_COLUMNS = ['timestamp', 'id', 'type', 'side', 'price', 'amount', 'cost']
LAST_BT_RESULT_FN = '.last_result.json'
USERPATH_HYPEROPTS = 'hyperopts' USERPATH_HYPEROPTS = 'hyperopts'
USERPATH_STRATEGIES = 'strategies' USERPATH_STRATEGIES = 'strategies'
USERPATH_NOTEBOOKS = 'notebooks' USERPATH_NOTEBOOKS = 'notebooks'

172
freqtrade/data/btanalysis.py
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@ -3,52 +3,123 @@ Helpers when analyzing backtest data
""" """
import logging import logging
from pathlib import Path from pathlib import Path
from typing import Dict, Union, Tuple from typing import Dict, Union, Tuple, Any, Optional
import numpy as np import numpy as np
import pandas as pd import pandas as pd
from datetime import timezone from datetime import timezone
from freqtrade import persistence from freqtrade import persistence
from freqtrade.constants import LAST_BT_RESULT_FN
from freqtrade.misc import json_load from freqtrade.misc import json_load
from freqtrade.persistence import Trade from freqtrade.persistence import Trade
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
# must align with columns in backtest.py # must align with columns in backtest.py
BT_DATA_COLUMNS = ["pair", "profit_percent", "open_time", "close_time", "index", "duration", BT_DATA_COLUMNS = ["pair", "profit_percent", "open_date", "close_date", "index", "trade_duration",
"open_rate", "close_rate", "open_at_end", "sell_reason"] "open_rate", "close_rate", "open_at_end", "sell_reason"]
def load_backtest_data(filename: Union[Path, str]) -> pd.DataFrame: def get_latest_backtest_filename(directory: Union[Path, str]) -> str:
""" """
Load backtest data file. Get latest backtest export based on '.last_result.json'.
:param filename: pathlib.Path object, or string pointing to the file. :param directory: Directory to search for last result
:return: a dataframe with the analysis results :return: string containing the filename of the latest backtest result
:raises: ValueError in the following cases:
* Directory does not exist
* `directory/.last_result.json` does not exist
* `directory/.last_result.json` has the wrong content
""" """
if isinstance(filename, str): if isinstance(directory, str):
filename = Path(filename) directory = Path(directory)
if not directory.is_dir():
raise ValueError(f"Directory '{directory}' does not exist.")
filename = directory / LAST_BT_RESULT_FN
if not filename.is_file(): if not filename.is_file():
raise ValueError(f"File {filename} does not exist.") raise ValueError(
f"Directory '{directory}' does not seem to contain backtest statistics yet.")
with filename.open() as file: with filename.open() as file:
data = json_load(file) data = json_load(file)
df = pd.DataFrame(data, columns=BT_DATA_COLUMNS) if 'latest_backtest' not in data:
raise ValueError(f"Invalid '{LAST_BT_RESULT_FN}' format.")
df['open_time'] = pd.to_datetime(df['open_time'], return data['latest_backtest']
unit='s',
utc=True,
infer_datetime_format=True def load_backtest_stats(filename: Union[Path, str]) -> Dict[str, Any]:
) """
df['close_time'] = pd.to_datetime(df['close_time'], Load backtest statistics file.
unit='s', :param filename: pathlib.Path object, or string pointing to the file.
utc=True, :return: a dictionary containing the resulting file.
infer_datetime_format=True """
) if isinstance(filename, str):
df['profit'] = df['close_rate'] - df['open_rate'] filename = Path(filename)
df = df.sort_values("open_time").reset_index(drop=True) if filename.is_dir():
filename = filename / get_latest_backtest_filename(filename)
if not filename.is_file():
raise ValueError(f"File {filename} does not exist.")
logger.info(f"Loading backtest result from {filename}")
with filename.open() as file:
data = json_load(file)
return data
def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = None) -> pd.DataFrame:
"""
Load backtest data file.
:param filename: pathlib.Path object, or string pointing to a file or directory
:param strategy: Strategy to load - mainly relevant for multi-strategy backtests
Can also serve as protection to load the correct result.
:return: a dataframe with the analysis results
:raise: ValueError if loading goes wrong.
"""
data = load_backtest_stats(filename)
if not isinstance(data, list):
# new, nested format
if 'strategy' not in data:
raise ValueError("Unknown dataformat.")
if not strategy:
if len(data['strategy']) == 1:
strategy = list(data['strategy'].keys())[0]
else:
raise ValueError("Detected backtest result with more than one strategy. "
"Please specify a strategy.")
if strategy not in data['strategy']:
raise ValueError(f"Strategy {strategy} not available in the backtest result.")
data = data['strategy'][strategy]['trades']
df = pd.DataFrame(data)
df['open_date'] = pd.to_datetime(df['open_date'],
utc=True,
infer_datetime_format=True
)
df['close_date'] = pd.to_datetime(df['close_date'],
utc=True,
infer_datetime_format=True
)
else:
# old format - only with lists.
df = pd.DataFrame(data, columns=BT_DATA_COLUMNS)
df['open_date'] = pd.to_datetime(df['open_date'],
unit='s',
utc=True,
infer_datetime_format=True
)
df['close_date'] = pd.to_datetime(df['close_date'],
unit='s',
utc=True,
infer_datetime_format=True
)
df['profit_abs'] = df['close_rate'] - df['open_rate']
df = df.sort_values("open_date").reset_index(drop=True)
return df return df
@ -62,9 +133,9 @@ def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataF
""" """
from freqtrade.exchange import timeframe_to_minutes from freqtrade.exchange import timeframe_to_minutes
timeframe_min = timeframe_to_minutes(timeframe) timeframe_min = timeframe_to_minutes(timeframe)
dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time, dates = [pd.Series(pd.date_range(row[1]['open_date'], row[1]['close_date'],
freq=f"{timeframe_min}min")) freq=f"{timeframe_min}min"))
for row in results[['open_time', 'close_time']].iterrows()] for row in results[['open_date', 'close_date']].iterrows()]
deltas = [len(x) for x in dates] deltas = [len(x) for x in dates]
dates = pd.Series(pd.concat(dates).values, name='date') dates = pd.Series(pd.concat(dates).values, name='date')
df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0), columns=results.columns) df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0), columns=results.columns)
@ -90,21 +161,26 @@ def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
return df_final[df_final['open_trades'] > max_open_trades] return df_final[df_final['open_trades'] > max_open_trades]
def load_trades_from_db(db_url: str) -> pd.DataFrame: def load_trades_from_db(db_url: str, strategy: Optional[str] = None) -> pd.DataFrame:
""" """
Load trades from a DB (using dburl) Load trades from a DB (using dburl)
:param db_url: Sqlite url (default format sqlite:///tradesv3.dry-run.sqlite) :param db_url: Sqlite url (default format sqlite:///tradesv3.dry-run.sqlite)
:param strategy: Strategy to load - mainly relevant for multi-strategy backtests
Can also serve as protection to load the correct result.
:return: Dataframe containing Trades :return: Dataframe containing Trades
""" """
trades: pd.DataFrame = pd.DataFrame([], columns=BT_DATA_COLUMNS)
persistence.init(db_url, clean_open_orders=False) persistence.init(db_url, clean_open_orders=False)
columns = ["pair", "open_time", "close_time", "profit", "profit_percent", columns = ["pair", "open_date", "close_date", "profit", "profit_percent",
"open_rate", "close_rate", "amount", "duration", "sell_reason", "open_rate", "close_rate", "amount", "trade_duration", "sell_reason",
"fee_open", "fee_close", "open_rate_requested", "close_rate_requested", "fee_open", "fee_close", "open_rate_requested", "close_rate_requested",
"stake_amount", "max_rate", "min_rate", "id", "exchange", "stake_amount", "max_rate", "min_rate", "id", "exchange",
"stop_loss", "initial_stop_loss", "strategy", "timeframe"] "stop_loss", "initial_stop_loss", "strategy", "timeframe"]
filters = []
if strategy:
filters.append(Trade.strategy == strategy)
trades = pd.DataFrame([(t.pair, trades = pd.DataFrame([(t.pair,
t.open_date.replace(tzinfo=timezone.utc), t.open_date.replace(tzinfo=timezone.utc),
t.close_date.replace(tzinfo=timezone.utc) if t.close_date else None, t.close_date.replace(tzinfo=timezone.utc) if t.close_date else None,
@ -123,14 +199,14 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
t.stop_loss, t.initial_stop_loss, t.stop_loss, t.initial_stop_loss,
t.strategy, t.timeframe t.strategy, t.timeframe
) )
for t in Trade.get_trades().all()], for t in Trade.get_trades(filters).all()],
columns=columns) columns=columns)
return trades return trades
def load_trades(source: str, db_url: str, exportfilename: Path, def load_trades(source: str, db_url: str, exportfilename: Path,
no_trades: bool = False) -> pd.DataFrame: no_trades: bool = False, strategy: Optional[str] = None) -> pd.DataFrame:
""" """
Based on configuration option "trade_source": Based on configuration option "trade_source":
* loads data from DB (using `db_url`) * loads data from DB (using `db_url`)
@ -148,7 +224,7 @@ def load_trades(source: str, db_url: str, exportfilename: Path,
if source == "DB": if source == "DB":
return load_trades_from_db(db_url) return load_trades_from_db(db_url)
elif source == "file": elif source == "file":
return load_backtest_data(exportfilename) return load_backtest_data(exportfilename, strategy)
def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame, def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
@ -163,11 +239,31 @@ def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
else: else:
trades_start = dataframe.iloc[0]['date'] trades_start = dataframe.iloc[0]['date']
trades_stop = dataframe.iloc[-1]['date'] trades_stop = dataframe.iloc[-1]['date']
trades = trades.loc[(trades['open_time'] >= trades_start) & trades = trades.loc[(trades['open_date'] >= trades_start) &
(trades['close_time'] <= trades_stop)] (trades['close_date'] <= trades_stop)]
return trades return trades
def calculate_market_change(data: Dict[str, pd.DataFrame], column: str = "close") -> float:
"""
Calculate market change based on "column".
Calculation is done by taking the first non-null and the last non-null element of each column
and calculating the pctchange as "(last - first) / first".
Then the results per pair are combined as mean.
:param data: Dict of Dataframes, dict key should be pair.
:param column: Column in the original dataframes to use
:return:
"""
tmp_means = []
for pair, df in data.items():
start = df[column].dropna().iloc[0]
end = df[column].dropna().iloc[-1]
tmp_means.append((end - start) / start)
return np.mean(tmp_means)
def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame], def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
column: str = "close") -> pd.DataFrame: column: str = "close") -> pd.DataFrame:
""" """
@ -190,7 +286,7 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
""" """
Adds a column `col_name` with the cumulative profit for the given trades array. Adds a column `col_name` with the cumulative profit for the given trades array.
:param df: DataFrame with date index :param df: DataFrame with date index
:param trades: DataFrame containing trades (requires columns close_time and profit_percent) :param trades: DataFrame containing trades (requires columns close_date and profit_percent)
:param col_name: Column name that will be assigned the results :param col_name: Column name that will be assigned the results
:param timeframe: Timeframe used during the operations :param timeframe: Timeframe used during the operations
:return: Returns df with one additional column, col_name, containing the cumulative profit. :return: Returns df with one additional column, col_name, containing the cumulative profit.
@ -201,7 +297,7 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
from freqtrade.exchange import timeframe_to_minutes from freqtrade.exchange import timeframe_to_minutes
timeframe_minutes = timeframe_to_minutes(timeframe) timeframe_minutes = timeframe_to_minutes(timeframe)
# Resample to timeframe to make sure trades match candles # Resample to timeframe to make sure trades match candles
_trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_time' _trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_date'
)[['profit_percent']].sum() )[['profit_percent']].sum()
df.loc[:, col_name] = _trades_sum.cumsum() df.loc[:, col_name] = _trades_sum.cumsum()
# Set first value to 0 # Set first value to 0
@ -211,13 +307,13 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
return df return df
def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_time', def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
value_col: str = 'profit_percent' value_col: str = 'profit_percent'
) -> Tuple[float, pd.Timestamp, pd.Timestamp]: ) -> Tuple[float, pd.Timestamp, pd.Timestamp]:
""" """
Calculate max drawdown and the corresponding close dates Calculate max drawdown and the corresponding close dates
:param trades: DataFrame containing trades (requires columns close_time and profit_percent) :param trades: DataFrame containing trades (requires columns close_date and profit_percent)
:param date_col: Column in DataFrame to use for dates (defaults to 'close_time') :param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
:param value_col: Column in DataFrame to use for values (defaults to 'profit_percent') :param value_col: Column in DataFrame to use for values (defaults to 'profit_percent')
:return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time :return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time
:raise: ValueError if trade-dataframe was found empty. :raise: ValueError if trade-dataframe was found empty.

19
freqtrade/edge/edge_positioning.py
View File

@ -9,7 +9,7 @@ import utils_find_1st as utf1st
from pandas import DataFrame from pandas import DataFrame
from freqtrade.configuration import TimeRange from freqtrade.configuration import TimeRange
from freqtrade.constants import UNLIMITED_STAKE_AMOUNT from freqtrade.constants import UNLIMITED_STAKE_AMOUNT, DATETIME_PRINT_FORMAT
from freqtrade.exceptions import OperationalException from freqtrade.exceptions import OperationalException
from freqtrade.data.history import get_timerange, load_data, refresh_data from freqtrade.data.history import get_timerange, load_data, refresh_data
from freqtrade.strategy.interface import SellType from freqtrade.strategy.interface import SellType
@ -121,12 +121,9 @@ class Edge:
# Print timeframe # Print timeframe
min_date, max_date = get_timerange(preprocessed) min_date, max_date = get_timerange(preprocessed)
logger.info( logger.info(f'Measuring data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
'Measuring data from %s up to %s (%s days) ...', f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
min_date.isoformat(), f'({(max_date - min_date).days} days)..')
max_date.isoformat(),
(max_date - min_date).days
)
headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low'] headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
trades: list = [] trades: list = []
@ -240,7 +237,7 @@ class Edge:
# All returned values are relative, they are defined as ratios. # All returned values are relative, they are defined as ratios.
stake = 0.015 stake = 0.015
result['trade_duration'] = result['close_time'] - result['open_time'] result['trade_duration'] = result['close_date'] - result['open_date']
result['trade_duration'] = result['trade_duration'].map( result['trade_duration'] = result['trade_duration'].map(
lambda x: int(x.total_seconds() / 60)) lambda x: int(x.total_seconds() / 60))
@ -430,10 +427,8 @@ class Edge:
'stoploss': stoploss, 'stoploss': stoploss,
'profit_ratio': '', 'profit_ratio': '',
'profit_abs': '', 'profit_abs': '',
'open_time': date_column[open_trade_index], 'open_date': date_column[open_trade_index],
'close_time': date_column[exit_index], 'close_date': date_column[exit_index],
'open_index': start_point + open_trade_index,
'close_index': start_point + exit_index,
'trade_duration': '', 'trade_duration': '',
'open_rate': round(open_price, 15), 'open_rate': round(open_price, 15),
'close_rate': round(exit_price, 15), 'close_rate': round(exit_price, 15),

69
freqtrade/optimize/backtesting.py
View File

@ -13,6 +13,7 @@ from pandas import DataFrame
from freqtrade.configuration import (TimeRange, remove_credentials, from freqtrade.configuration import (TimeRange, remove_credentials,
validate_config_consistency) validate_config_consistency)
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.data import history from freqtrade.data import history
from freqtrade.data.converter import trim_dataframe from freqtrade.data.converter import trim_dataframe
from freqtrade.data.dataprovider import DataProvider from freqtrade.data.dataprovider import DataProvider
@ -20,7 +21,7 @@ from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.optimize.optimize_reports import (generate_backtest_stats, from freqtrade.optimize.optimize_reports import (generate_backtest_stats,
show_backtest_results, show_backtest_results,
store_backtest_result) store_backtest_stats)
from freqtrade.pairlist.pairlistmanager import PairListManager from freqtrade.pairlist.pairlistmanager import PairListManager
from freqtrade.persistence import Trade from freqtrade.persistence import Trade
from freqtrade.resolvers import ExchangeResolver, StrategyResolver from freqtrade.resolvers import ExchangeResolver, StrategyResolver
@ -36,14 +37,15 @@ class BacktestResult(NamedTuple):
pair: str pair: str
profit_percent: float profit_percent: float
profit_abs: float profit_abs: float
open_time: datetime open_date: datetime
close_time: datetime open_rate: float
open_index: int open_fee: float
close_index: int close_date: datetime
close_rate: float
close_fee: float
amount: float
trade_duration: float trade_duration: float
open_at_end: bool open_at_end: bool
open_rate: float
close_rate: float
sell_reason: SellType sell_reason: SellType
@ -135,10 +137,10 @@ class Backtesting:
min_date, max_date = history.get_timerange(data) min_date, max_date = history.get_timerange(data)
logger.info( logger.info(f'Loading data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
'Loading data from %s up to %s (%s days)..', f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days f'({(max_date - min_date).days} days)..')
)
# Adjust startts forward if not enough data is available # Adjust startts forward if not enough data is available
timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe), timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
self.required_startup, min_date) self.required_startup, min_date)
@ -223,7 +225,7 @@ class Backtesting:
open_rate=buy_row.open, open_rate=buy_row.open,
open_date=buy_row.date, open_date=buy_row.date,
stake_amount=stake_amount, stake_amount=stake_amount,
amount=stake_amount / buy_row.open, amount=round(stake_amount / buy_row.open, 8),
fee_open=self.fee, fee_open=self.fee,
fee_close=self.fee, fee_close=self.fee,
is_open=True, is_open=True,
@ -244,14 +246,15 @@ class Backtesting:
return BacktestResult(pair=pair, return BacktestResult(pair=pair,
profit_percent=trade.calc_profit_ratio(rate=closerate), profit_percent=trade.calc_profit_ratio(rate=closerate),
profit_abs=trade.calc_profit(rate=closerate), profit_abs=trade.calc_profit(rate=closerate),
open_time=buy_row.date, open_date=buy_row.date,
close_time=sell_row.date,
trade_duration=trade_dur,
open_index=buy_row.Index,
close_index=sell_row.Index,
open_at_end=False,
open_rate=buy_row.open, open_rate=buy_row.open,
open_fee=self.fee,
close_date=sell_row.date,
close_rate=closerate, close_rate=closerate,
close_fee=self.fee,
amount=trade.amount,
trade_duration=trade_dur,
open_at_end=False,
sell_reason=sell.sell_type sell_reason=sell.sell_type
) )
if partial_ohlcv: if partial_ohlcv:
@ -260,15 +263,16 @@ class Backtesting:
bt_res = BacktestResult(pair=pair, bt_res = BacktestResult(pair=pair,
profit_percent=trade.calc_profit_ratio(rate=sell_row.open), profit_percent=trade.calc_profit_ratio(rate=sell_row.open),
profit_abs=trade.calc_profit(rate=sell_row.open), profit_abs=trade.calc_profit(rate=sell_row.open),
open_time=buy_row.date, open_date=buy_row.date,
close_time=sell_row.date, open_rate=buy_row.open,
open_fee=self.fee,
close_date=sell_row.date,
close_rate=sell_row.open,
close_fee=self.fee,
amount=trade.amount,
trade_duration=int(( trade_duration=int((
sell_row.date - buy_row.date).total_seconds() // 60), sell_row.date - buy_row.date).total_seconds() // 60),
open_index=buy_row.Index,
close_index=sell_row.Index,
open_at_end=True, open_at_end=True,
open_rate=buy_row.open,
close_rate=sell_row.open,
sell_reason=SellType.FORCE_SELL sell_reason=SellType.FORCE_SELL
) )
logger.debug(f"{pair} - Force selling still open trade, " logger.debug(f"{pair} - Force selling still open trade, "
@ -354,8 +358,8 @@ class Backtesting:
if trade_entry: if trade_entry:
logger.debug(f"{pair} - Locking pair till " logger.debug(f"{pair} - Locking pair till "
f"close_time={trade_entry.close_time}") f"close_date={trade_entry.close_date}")
lock_pair_until[pair] = trade_entry.close_time lock_pair_until[pair] = trade_entry.close_date
trades.append(trade_entry) trades.append(trade_entry)
else: else:
# Set lock_pair_until to end of testing period if trade could not be closed # Set lock_pair_until to end of testing period if trade could not be closed
@ -398,10 +402,9 @@ class Backtesting:
preprocessed[pair] = trim_dataframe(df, timerange) preprocessed[pair] = trim_dataframe(df, timerange)
min_date, max_date = history.get_timerange(preprocessed) min_date, max_date = history.get_timerange(preprocessed)
logger.info( logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
'Backtesting with data from %s up to %s (%s days)..', f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days f'({(max_date - min_date).days} days)..')
)
# Execute backtest and print results # Execute backtest and print results
all_results[self.strategy.get_strategy_name()] = self.backtest( all_results[self.strategy.get_strategy_name()] = self.backtest(
processed=preprocessed, processed=preprocessed,
@ -412,8 +415,10 @@ class Backtesting:
position_stacking=position_stacking, position_stacking=position_stacking,
) )
stats = generate_backtest_stats(self.config, data, all_results,
min_date=min_date, max_date=max_date)
if self.config.get('export', False): if self.config.get('export', False):
store_backtest_result(self.config['exportfilename'], all_results) store_backtest_stats(self.config['exportfilename'], stats)
# Show backtest results # Show backtest results
stats = generate_backtest_stats(self.config, data, all_results)
show_backtest_results(self.config, stats) show_backtest_results(self.config, stats)

9
freqtrade/optimize/hyperopt.py
View File

@ -25,6 +25,7 @@ from joblib import (Parallel, cpu_count, delayed, dump, load,
wrap_non_picklable_objects) wrap_non_picklable_objects)
from pandas import DataFrame, isna, json_normalize from pandas import DataFrame, isna, json_normalize
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.data.converter import trim_dataframe from freqtrade.data.converter import trim_dataframe
from freqtrade.data.history import get_timerange from freqtrade.data.history import get_timerange
from freqtrade.exceptions import OperationalException from freqtrade.exceptions import OperationalException
@ -642,10 +643,10 @@ class Hyperopt:
preprocessed[pair] = trim_dataframe(df, timerange) preprocessed[pair] = trim_dataframe(df, timerange)
min_date, max_date = get_timerange(data) min_date, max_date = get_timerange(data)
logger.info( logger.info(f'Hyperopting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
'Hyperopting with data from %s up to %s (%s days)..', f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days f'({(max_date - min_date).days} days)..')
)
dump(preprocessed, self.data_pickle_file) dump(preprocessed, self.data_pickle_file)
# We don't need exchange instance anymore while running hyperopt # We don't need exchange instance anymore while running hyperopt

2
freqtrade/optimize/hyperopt_loss_sharpe_daily.py
View File

@ -43,7 +43,7 @@ class SharpeHyperOptLossDaily(IHyperOptLoss):
normalize=True) normalize=True)
sum_daily = ( sum_daily = (
results.resample(resample_freq, on='close_time').agg( results.resample(resample_freq, on='close_date').agg(
{"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0) {"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0)
) )

2
freqtrade/optimize/hyperopt_loss_sortino_daily.py
View File

@ -45,7 +45,7 @@ class SortinoHyperOptLossDaily(IHyperOptLoss):
normalize=True) normalize=True)
sum_daily = ( sum_daily = (
results.resample(resample_freq, on='close_time').agg( results.resample(resample_freq, on='close_date').agg(
{"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0) {"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0)
) )

179
freqtrade/optimize/optimize_reports.py
View File

@ -1,46 +1,40 @@
import logging import logging
from datetime import timedelta from datetime import datetime, timedelta, timezone
from pathlib import Path from pathlib import Path
from typing import Any, Dict, List from typing import Any, Dict, List
from arrow import Arrow
from pandas import DataFrame from pandas import DataFrame
from numpy import int64
from tabulate import tabulate from tabulate import tabulate
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
from freqtrade.data.btanalysis import calculate_max_drawdown, calculate_market_change
from freqtrade.misc import file_dump_json from freqtrade.misc import file_dump_json
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
def store_backtest_result(recordfilename: Path, all_results: Dict[str, DataFrame]) -> None: def store_backtest_stats(recordfilename: Path, stats: Dict[str, DataFrame]) -> None:
""" """
Stores backtest results to file (one file per strategy) Stores backtest results
:param recordfilename: Destination filename :param recordfilename: Path object, which can either be a filename or a directory.
:param all_results: Dict of Dataframes, one results dataframe per strategy Filenames will be appended with a timestamp right before the suffix
while for diectories, <directory>/backtest-result-<datetime>.json will be used as filename
:param stats: Dataframe containing the backtesting statistics
""" """
for strategy, results in all_results.items(): if recordfilename.is_dir():
records = backtest_result_to_list(results) filename = (recordfilename /
f'backtest-result-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}.json')
else:
filename = Path.joinpath(
recordfilename.parent,
f'{recordfilename.stem}-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}'
).with_suffix(recordfilename.suffix)
file_dump_json(filename, stats)
if records: latest_filename = Path.joinpath(filename.parent, LAST_BT_RESULT_FN)
filename = recordfilename file_dump_json(latest_filename, {'latest_backtest': str(filename.name)})
if len(all_results) > 1:
# Inject strategy to filename
filename = Path.joinpath(
recordfilename.parent,
f'{recordfilename.stem}-{strategy}').with_suffix(recordfilename.suffix)
logger.info(f'Dumping backtest results to {filename}')
file_dump_json(filename, records)
def backtest_result_to_list(results: DataFrame) -> List[List]:
"""
Converts a list of Backtest-results to list
:param results: Dataframe containing results for one strategy
:return: List of Lists containing the trades
"""
return [[t.pair, t.profit_percent, t.open_time.timestamp(),
t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
t.open_rate, t.close_rate, t.open_at_end, t.sell_reason.value]
for index, t in results.iterrows()]
def _get_line_floatfmt() -> List[str]: def _get_line_floatfmt() -> List[str]:
@ -66,11 +60,12 @@ def _generate_result_line(result: DataFrame, max_open_trades: int, first_column:
return { return {
'key': first_column, 'key': first_column,
'trades': len(result), 'trades': len(result),
'profit_mean': result['profit_percent'].mean(), 'profit_mean': result['profit_percent'].mean() if len(result) > 0 else 0.0,
'profit_mean_pct': result['profit_percent'].mean() * 100.0, 'profit_mean_pct': result['profit_percent'].mean() * 100.0 if len(result) > 0 else 0.0,
'profit_sum': result['profit_percent'].sum(), 'profit_sum': result['profit_percent'].sum(),
'profit_sum_pct': result['profit_percent'].sum() * 100.0, 'profit_sum_pct': result['profit_percent'].sum() * 100.0,
'profit_total_abs': result['profit_abs'].sum(), 'profit_total_abs': result['profit_abs'].sum(),
'profit_total': result['profit_percent'].sum() / max_open_trades,
'profit_total_pct': result['profit_percent'].sum() * 100.0 / max_open_trades, 'profit_total_pct': result['profit_percent'].sum() * 100.0 / max_open_trades,
'duration_avg': str(timedelta( 'duration_avg': str(timedelta(
minutes=round(result['trade_duration'].mean())) minutes=round(result['trade_duration'].mean()))
@ -141,7 +136,7 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
'profit_sum': profit_sum, 'profit_sum': profit_sum,
'profit_sum_pct': round(profit_sum * 100, 2), 'profit_sum_pct': round(profit_sum * 100, 2),
'profit_total_abs': result['profit_abs'].sum(), 'profit_total_abs': result['profit_abs'].sum(),
'profit_pct_total': profit_percent_tot, 'profit_total_pct': profit_percent_tot,
} }
) )
return tabular_data return tabular_data
@ -189,18 +184,48 @@ def generate_edge_table(results: dict) -> str:
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
daily_profit = results.resample('1d', on='close_date')['profit_percent'].sum()
worst = min(daily_profit)
best = max(daily_profit)
winning_days = sum(daily_profit > 0)
draw_days = sum(daily_profit == 0)
losing_days = sum(daily_profit < 0)
winning_trades = results.loc[results['profit_percent'] > 0]
losing_trades = results.loc[results['profit_percent'] < 0]
return {
'backtest_best_day': best,
'backtest_worst_day': worst,
'winning_days': winning_days,
'draw_days': draw_days,
'losing_days': losing_days,
'winner_holding_avg': (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
if not winning_trades.empty else timedelta()),
'loser_holding_avg': (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
if not losing_trades.empty else timedelta()),
}
def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame], def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
all_results: Dict[str, DataFrame]) -> Dict[str, Any]: all_results: Dict[str, DataFrame],
min_date: Arrow, max_date: Arrow
) -> Dict[str, Any]:
""" """
:param config: Configuration object used for backtest :param config: Configuration object used for backtest
:param btdata: Backtest data :param btdata: Backtest data
:param all_results: backtest result - dictionary with { Strategy: results}. :param all_results: backtest result - dictionary with { Strategy: results}.
:param min_date: Backtest start date
:param max_date: Backtest end date
:return: :return:
Dictionary containing results per strategy and a stratgy summary. Dictionary containing results per strategy and a stratgy summary.
""" """
stake_currency = config['stake_currency'] stake_currency = config['stake_currency']
max_open_trades = config['max_open_trades'] max_open_trades = config['max_open_trades']
result: Dict[str, Any] = {'strategy': {}} result: Dict[str, Any] = {'strategy': {}}
market_change = calculate_market_change(btdata, 'close')
for strategy, results in all_results.items(): for strategy, results in all_results.items():
pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency, pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
@ -212,14 +237,57 @@ def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
max_open_trades=max_open_trades, max_open_trades=max_open_trades,
results=results.loc[results['open_at_end']], results=results.loc[results['open_at_end']],
skip_nan=True) skip_nan=True)
daily_stats = generate_daily_stats(results)
results['open_timestamp'] = results['open_date'].astype(int64) // 1e6
results['close_timestamp'] = results['close_date'].astype(int64) // 1e6
backtest_days = (max_date - min_date).days
strat_stats = { strat_stats = {
'trades': backtest_result_to_list(results), 'trades': results.to_dict(orient='records'),
'results_per_pair': pair_results, 'results_per_pair': pair_results,
'sell_reason_summary': sell_reason_stats, 'sell_reason_summary': sell_reason_stats,
'left_open_trades': left_open_results, 'left_open_trades': left_open_results,
} 'total_trades': len(results),
'profit_mean': results['profit_percent'].mean(),
'profit_total': results['profit_percent'].sum(),
'profit_total_abs': results['profit_abs'].sum(),
'backtest_start': min_date.datetime,
'backtest_start_ts': min_date.timestamp * 1000,
'backtest_end': max_date.datetime,
'backtest_end_ts': max_date.timestamp * 1000,
'backtest_days': backtest_days,
'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else None,
'market_change': market_change,
'pairlist': list(btdata.keys()),
'stake_amount': config['stake_amount'],
'stake_currency': config['stake_currency'],
'max_open_trades': config['max_open_trades'],
'timeframe': config['timeframe'],
**daily_stats,
}
result['strategy'][strategy] = strat_stats result['strategy'][strategy] = strat_stats
try:
max_drawdown, drawdown_start, drawdown_end = calculate_max_drawdown(
results, value_col='profit_percent')
strat_stats.update({
'max_drawdown': max_drawdown,
'drawdown_start': drawdown_start,
'drawdown_start_ts': drawdown_start.timestamp() * 1000,
'drawdown_end': drawdown_end,
'drawdown_end_ts': drawdown_end.timestamp() * 1000,
})
except ValueError:
strat_stats.update({
'max_drawdown': 0.0,
'drawdown_start': datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_start_ts': 0,
'drawdown_end': datetime(1970, 1, 1, tzinfo=timezone.utc),
'drawdown_end_ts': 0,
})
strategy_results = generate_strategy_metrics(stake_currency=stake_currency, strategy_results = generate_strategy_metrics(stake_currency=stake_currency,
max_open_trades=max_open_trades, max_open_trades=max_open_trades,
all_results=all_results) all_results=all_results)
@ -273,7 +341,7 @@ def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_curren
output = [[ output = [[
t['sell_reason'], t['trades'], t['wins'], t['draws'], t['losses'], t['sell_reason'], t['trades'], t['wins'], t['draws'], t['losses'],
t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'], t['profit_pct_total'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'], t['profit_total_pct'],
] for t in sell_reason_stats] ] for t in sell_reason_stats]
return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right") return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
@ -298,6 +366,35 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
def text_table_add_metrics(strat_results: Dict) -> str:
if len(strat_results['trades']) > 0:
min_trade = min(strat_results['trades'], key=lambda x: x['open_date'])
metrics = [
('Backtesting from', strat_results['backtest_start'].strftime(DATETIME_PRINT_FORMAT)),
('Backtesting to', strat_results['backtest_end'].strftime(DATETIME_PRINT_FORMAT)),
('Total trades', strat_results['total_trades']),
('First trade', min_trade['open_date'].strftime(DATETIME_PRINT_FORMAT)),
('First trade Pair', min_trade['pair']),
('Total Profit %', f"{round(strat_results['profit_total'] * 100, 2)}%"),
('Trades per day', strat_results['trades_per_day']),
('Best day', f"{round(strat_results['backtest_best_day'] * 100, 2)}%"),
('Worst day', f"{round(strat_results['backtest_worst_day'] * 100, 2)}%"),
('Days win/draw/lose', f"{strat_results['winning_days']} / "
f"{strat_results['draw_days']} / {strat_results['losing_days']}"),
('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),
('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"),
('', ''), # Empty line to improve readability
('Max Drawdown', f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
('Drawdown Start', strat_results['drawdown_start'].strftime(DATETIME_PRINT_FORMAT)),
('Drawdown End', strat_results['drawdown_end'].strftime(DATETIME_PRINT_FORMAT)),
('Market change', f"{round(strat_results['market_change'] * 100, 2)}%"),
]
return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl")
else:
return ''
def show_backtest_results(config: Dict, backtest_stats: Dict): def show_backtest_results(config: Dict, backtest_stats: Dict):
stake_currency = config['stake_currency'] stake_currency = config['stake_currency']
@ -312,15 +409,21 @@ def show_backtest_results(config: Dict, backtest_stats: Dict):
table = text_table_sell_reason(sell_reason_stats=results['sell_reason_summary'], table = text_table_sell_reason(sell_reason_stats=results['sell_reason_summary'],
stake_currency=stake_currency) stake_currency=stake_currency)
if isinstance(table, str): if isinstance(table, str) and len(table) > 0:
print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '=')) print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
print(table) print(table)
table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency) table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency)
if isinstance(table, str): if isinstance(table, str) and len(table) > 0:
print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '=')) print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
print(table) print(table)
if isinstance(table, str):
table = text_table_add_metrics(results)
if isinstance(table, str) and len(table) > 0:
print(' SUMMARY METRICS '.center(len(table.splitlines()[0]), '='))
print(table)
if isinstance(table, str) and len(table) > 0:
print('=' * len(table.splitlines()[0])) print('=' * len(table.splitlines()[0]))
print() print()

29
freqtrade/plot/plotting.py
View File

@ -8,7 +8,8 @@ from freqtrade.configuration import TimeRange
from freqtrade.data.btanalysis import (calculate_max_drawdown, from freqtrade.data.btanalysis import (calculate_max_drawdown,
combine_dataframes_with_mean, combine_dataframes_with_mean,
create_cum_profit, create_cum_profit,
extract_trades_of_period, load_trades) extract_trades_of_period,
load_trades)
from freqtrade.data.converter import trim_dataframe from freqtrade.data.converter import trim_dataframe
from freqtrade.data.dataprovider import DataProvider from freqtrade.data.dataprovider import DataProvider
from freqtrade.data.history import load_data from freqtrade.data.history import load_data
@ -53,19 +54,22 @@ def init_plotscript(config):
) )
no_trades = False no_trades = False
filename = config.get('exportfilename')
if config.get('no_trades', False): if config.get('no_trades', False):
no_trades = True no_trades = True
elif not config['exportfilename'].is_file() and config['trade_source'] == 'file': elif config['trade_source'] == 'file':
logger.warning("Backtest file is missing skipping trades.") if not filename.is_dir() and not filename.is_file():
no_trades = True logger.warning("Backtest file is missing skipping trades.")
no_trades = True
trades = load_trades( trades = load_trades(
config['trade_source'], config['trade_source'],
db_url=config.get('db_url'), db_url=config.get('db_url'),
exportfilename=config.get('exportfilename'), exportfilename=filename,
no_trades=no_trades no_trades=no_trades,
strategy=config.get("strategy"),
) )
trades = trim_dataframe(trades, timerange, 'open_time') trades = trim_dataframe(trades, timerange, 'open_date')
return {"ohlcv": data, return {"ohlcv": data,
"trades": trades, "trades": trades,
@ -165,10 +169,11 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
if trades is not None and len(trades) > 0: if trades is not None and len(trades) > 0:
# Create description for sell summarizing the trade # Create description for sell summarizing the trade
trades['desc'] = trades.apply(lambda row: f"{round(row['profit_percent'] * 100, 1)}%, " trades['desc'] = trades.apply(lambda row: f"{round(row['profit_percent'] * 100, 1)}%, "
f"{row['sell_reason']}, {row['duration']} min", f"{row['sell_reason']}, "
f"{row['trade_duration']} min",
axis=1) axis=1)
trade_buys = go.Scatter( trade_buys = go.Scatter(
x=trades["open_time"], x=trades["open_date"],
y=trades["open_rate"], y=trades["open_rate"],
mode='markers', mode='markers',
name='Trade buy', name='Trade buy',
@ -183,7 +188,7 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
) )
trade_sells = go.Scatter( trade_sells = go.Scatter(
x=trades.loc[trades['profit_percent'] > 0, "close_time"], x=trades.loc[trades['profit_percent'] > 0, "close_date"],
y=trades.loc[trades['profit_percent'] > 0, "close_rate"], y=trades.loc[trades['profit_percent'] > 0, "close_rate"],
text=trades.loc[trades['profit_percent'] > 0, "desc"], text=trades.loc[trades['profit_percent'] > 0, "desc"],
mode='markers', mode='markers',
@ -196,7 +201,7 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
) )
) )
trade_sells_loss = go.Scatter( trade_sells_loss = go.Scatter(
x=trades.loc[trades['profit_percent'] <= 0, "close_time"], x=trades.loc[trades['profit_percent'] <= 0, "close_date"],
y=trades.loc[trades['profit_percent'] <= 0, "close_rate"], y=trades.loc[trades['profit_percent'] <= 0, "close_rate"],
text=trades.loc[trades['profit_percent'] <= 0, "desc"], text=trades.loc[trades['profit_percent'] <= 0, "desc"],
mode='markers', mode='markers',
@ -510,7 +515,7 @@ def plot_profit(config: Dict[str, Any]) -> None:
# Remove open pairs - we don't know the profit yet so can't calculate profit for these. # Remove open pairs - we don't know the profit yet so can't calculate profit for these.
# Also, If only one open pair is left, then the profit-generation would fail. # Also, If only one open pair is left, then the profit-generation would fail.
trades = trades[(trades['pair'].isin(plot_elements["pairs"])) trades = trades[(trades['pair'].isin(plot_elements["pairs"]))
& (~trades['close_time'].isnull()) & (~trades['close_date'].isnull())
] ]
if len(trades) == 0: if len(trades) == 0:
raise OperationalException("No trades found, cannot generate Profit-plot without " raise OperationalException("No trades found, cannot generate Profit-plot without "

3
freqtrade/rpc/api_server.py
View File

@ -16,6 +16,7 @@ from werkzeug.security import safe_str_cmp
from werkzeug.serving import make_server from werkzeug.serving import make_server
from freqtrade.__init__ import __version__ from freqtrade.__init__ import __version__
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.rpc.rpc import RPC, RPCException from freqtrade.rpc.rpc import RPC, RPCException
from freqtrade.rpc.fiat_convert import CryptoToFiatConverter from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
@ -32,7 +33,7 @@ class ArrowJSONEncoder(JSONEncoder):
elif isinstance(obj, date): elif isinstance(obj, date):
return obj.strftime("%Y-%m-%d") return obj.strftime("%Y-%m-%d")
elif isinstance(obj, datetime): elif isinstance(obj, datetime):
return obj.strftime("%Y-%m-%d %H:%M:%S") return obj.strftime(DATETIME_PRINT_FORMAT)
iterable = iter(obj) iterable = iter(obj)
except TypeError: except TypeError:
pass pass

4
freqtrade/strategy/interface.py
View File

@ -44,6 +44,10 @@ class SellType(Enum):
EMERGENCY_SELL = "emergency_sell" EMERGENCY_SELL = "emergency_sell"
NONE = "" NONE = ""
def __str__(self):
# explicitly convert to String to help with exporting data.
return self.value
class SellCheckTuple(NamedTuple): class SellCheckTuple(NamedTuple):
""" """

51
freqtrade/templates/strategy_analysis_example.ipynb
View File

@ -34,7 +34,7 @@
"# config = Configuration.from_files([\"config.json\"])\n", "# config = Configuration.from_files([\"config.json\"])\n",
"\n", "\n",
"# Define some constants\n", "# Define some constants\n",
"config[\"ticker_interval\"] = \"5m\"\n", "config[\"timeframe\"] = \"5m\"\n",
"# Name of the strategy class\n", "# Name of the strategy class\n",
"config[\"strategy\"] = \"SampleStrategy\"\n", "config[\"strategy\"] = \"SampleStrategy\"\n",
"# Location of the data\n", "# Location of the data\n",
@ -53,7 +53,7 @@
"from freqtrade.data.history import load_pair_history\n", "from freqtrade.data.history import load_pair_history\n",
"\n", "\n",
"candles = load_pair_history(datadir=data_location,\n", "candles = load_pair_history(datadir=data_location,\n",
" timeframe=config[\"ticker_interval\"],\n", " timeframe=config[\"timeframe\"],\n",
" pair=pair)\n", " pair=pair)\n",
"\n", "\n",
"# Confirm success\n", "# Confirm success\n",
@ -136,10 +136,51 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"from freqtrade.data.btanalysis import load_backtest_data\n", "from freqtrade.data.btanalysis import load_backtest_data, load_backtest_stats\n",
"\n", "\n",
"# Load backtest results\n", "# if backtest_dir points to a directory, it'll automatically load the last backtest file.\n",
"trades = load_backtest_data(config[\"user_data_dir\"] / \"backtest_results/backtest-result.json\")\n", "backtest_dir = config[\"user_data_dir\"] / \"backtest_results\"\n",
"# backtest_dir can also point to a specific file \n",
"# backtest_dir = config[\"user_data_dir\"] / \"backtest_results/backtest-result-2020-07-01_20-04-22.json\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# You can get the full backtest statistics by using the following command.\n",
"# This contains all information used to generate the backtest result.\n",
"stats = load_backtest_stats(backtest_dir)\n",
"\n",
"strategy = 'SampleStrategy'\n",
"# All statistics are available per strategy, so if `--strategy-list` was used during backtest, this will be reflected here as well.\n",
"# Example usages:\n",
"print(stats['strategy'][strategy]['results_per_pair'])\n",
"# Get pairlist used for this backtest\n",
"print(stats['strategy'][strategy]['pairlist'])\n",
"# Get market change (average change of all pairs from start to end of the backtest period)\n",
"print(stats['strategy'][strategy]['market_change'])\n",
"# Maximum drawdown ()\n",
"print(stats['strategy'][strategy]['max_drawdown'])\n",
"# Maximum drawdown start and end\n",
"print(stats['strategy'][strategy]['drawdown_start'])\n",
"print(stats['strategy'][strategy]['drawdown_end'])\n",
"\n",
"\n",
"# Get strategy comparison (only relevant if multiple strategies were compared)\n",
"print(stats['strategy_comparison'])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load backtested trades as dataframe\n",
"trades = load_backtest_data(backtest_dir)\n",
"\n", "\n",
"# Show value-counts per pair\n", "# Show value-counts per pair\n",
"trades.groupby(\"pair\")[\"sell_reason\"].value_counts()" "trades.groupby(\"pair\")[\"sell_reason\"].value_counts()"

9
tests/conftest.py
View File

@ -181,7 +181,8 @@ def create_mock_trades(fee):
fee_close=fee.return_value, fee_close=fee.return_value,
open_rate=0.123, open_rate=0.123,
exchange='bittrex', exchange='bittrex',
open_order_id='dry_run_buy_12345' open_order_id='dry_run_buy_12345',
strategy='DefaultStrategy',
) )
Trade.session.add(trade) Trade.session.add(trade)
@ -197,7 +198,8 @@ def create_mock_trades(fee):
close_profit=0.005, close_profit=0.005,
exchange='bittrex', exchange='bittrex',
is_open=False, is_open=False,
open_order_id='dry_run_sell_12345' open_order_id='dry_run_sell_12345',
strategy='DefaultStrategy',
) )
Trade.session.add(trade) Trade.session.add(trade)
@ -225,7 +227,8 @@ def create_mock_trades(fee):
fee_close=fee.return_value, fee_close=fee.return_value,
open_rate=0.123, open_rate=0.123,
exchange='bittrex', exchange='bittrex',
open_order_id='prod_buy_12345' open_order_id='prod_buy_12345',
strategy='DefaultStrategy',
) )
Trade.session.add(trade) Trade.session.add(trade)

110
tests/data/test_btanalysis.py
View File

@ -6,24 +6,48 @@ from arrow import Arrow
from pandas import DataFrame, DateOffset, Timestamp, to_datetime from pandas import DataFrame, DateOffset, Timestamp, to_datetime
from freqtrade.configuration import TimeRange from freqtrade.configuration import TimeRange
from freqtrade.constants import LAST_BT_RESULT_FN
from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, from freqtrade.data.btanalysis import (BT_DATA_COLUMNS,
analyze_trade_parallelism, analyze_trade_parallelism,
calculate_market_change,
calculate_max_drawdown, calculate_max_drawdown,
combine_dataframes_with_mean, combine_dataframes_with_mean,
create_cum_profit, create_cum_profit,
extract_trades_of_period, extract_trades_of_period,
get_latest_backtest_filename,
load_backtest_data, load_trades, load_backtest_data, load_trades,
load_trades_from_db) load_trades_from_db)
from freqtrade.data.history import load_data, load_pair_history from freqtrade.data.history import load_data, load_pair_history
from freqtrade.optimize.backtesting import BacktestResult
from tests.conftest import create_mock_trades from tests.conftest import create_mock_trades
def test_load_backtest_data(testdatadir): def test_get_latest_backtest_filename(testdatadir, mocker):
with pytest.raises(ValueError, match=r"Directory .* does not exist\."):
get_latest_backtest_filename(testdatadir / 'does_not_exist')
with pytest.raises(ValueError,
match=r"Directory .* does not seem to contain .*"):
get_latest_backtest_filename(testdatadir.parent)
res = get_latest_backtest_filename(testdatadir)
assert res == 'backtest-result_new.json'
res = get_latest_backtest_filename(str(testdatadir))
assert res == 'backtest-result_new.json'
mocker.patch("freqtrade.data.btanalysis.json_load", return_value={})
with pytest.raises(ValueError, match=r"Invalid '.last_result.json' format."):
get_latest_backtest_filename(testdatadir)
def test_load_backtest_data_old_format(testdatadir):
filename = testdatadir / "backtest-result_test.json" filename = testdatadir / "backtest-result_test.json"
bt_data = load_backtest_data(filename) bt_data = load_backtest_data(filename)
assert isinstance(bt_data, DataFrame) assert isinstance(bt_data, DataFrame)
assert list(bt_data.columns) == BT_DATA_COLUMNS + ["profit"] assert list(bt_data.columns) == BT_DATA_COLUMNS + ["profit_abs"]
assert len(bt_data) == 179 assert len(bt_data) == 179
# Test loading from string (must yield same result) # Test loading from string (must yield same result)
@ -34,6 +58,49 @@ def test_load_backtest_data(testdatadir):
load_backtest_data(str("filename") + "nofile") load_backtest_data(str("filename") + "nofile")
def test_load_backtest_data_new_format(testdatadir):
filename = testdatadir / "backtest-result_new.json"
bt_data = load_backtest_data(filename)
assert isinstance(bt_data, DataFrame)
assert set(bt_data.columns) == set(list(BacktestResult._fields) + ["profit_abs"])
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)
# Test loading from folder (must yield same result)
bt_data3 = load_backtest_data(testdatadir)
assert bt_data.equals(bt_data3)
with pytest.raises(ValueError, match=r"File .* does not exist\."):
load_backtest_data(str("filename") + "nofile")
with pytest.raises(ValueError, match=r"Unknown dataformat."):
load_backtest_data(testdatadir / LAST_BT_RESULT_FN)
def test_load_backtest_data_multi(testdatadir):
filename = testdatadir / "backtest-result_multistrat.json"
for strategy in ('DefaultStrategy', 'TestStrategy'):
bt_data = load_backtest_data(filename, strategy=strategy)
assert isinstance(bt_data, DataFrame)
assert set(bt_data.columns) == set(list(BacktestResult._fields) + ["profit_abs"])
assert len(bt_data) == 179
# Test loading from string (must yield same result)
bt_data2 = load_backtest_data(str(filename), strategy=strategy)
assert bt_data.equals(bt_data2)
with pytest.raises(ValueError, match=r"Strategy XYZ not available in the backtest result\."):
load_backtest_data(filename, strategy='XYZ')
with pytest.raises(ValueError, match=r"Detected backtest result with more than one strategy.*"):
load_backtest_data(filename)
@pytest.mark.usefixtures("init_persistence") @pytest.mark.usefixtures("init_persistence")
def test_load_trades_from_db(default_conf, fee, mocker): def test_load_trades_from_db(default_conf, fee, mocker):
@ -46,12 +113,16 @@ def test_load_trades_from_db(default_conf, fee, mocker):
assert len(trades) == 4 assert len(trades) == 4
assert isinstance(trades, DataFrame) assert isinstance(trades, DataFrame)
assert "pair" in trades.columns assert "pair" in trades.columns
assert "open_time" in trades.columns assert "open_date" in trades.columns
assert "profit_percent" in trades.columns assert "profit_percent" in trades.columns
for col in BT_DATA_COLUMNS: for col in BT_DATA_COLUMNS:
if col not in ['index', 'open_at_end']: if col not in ['index', 'open_at_end']:
assert col in trades.columns assert col in trades.columns
trades = load_trades_from_db(db_url=default_conf['db_url'], strategy='DefaultStrategy')
assert len(trades) == 3
trades = load_trades_from_db(db_url=default_conf['db_url'], strategy='NoneStrategy')
assert len(trades) == 0
def test_extract_trades_of_period(testdatadir): def test_extract_trades_of_period(testdatadir):
@ -66,13 +137,13 @@ def test_extract_trades_of_period(testdatadir):
{'pair': [pair, pair, pair, pair], {'pair': [pair, pair, pair, pair],
'profit_percent': [0.0, 0.1, -0.2, -0.5], 'profit_percent': [0.0, 0.1, -0.2, -0.5],
'profit_abs': [0.0, 1, -2, -5], 'profit_abs': [0.0, 1, -2, -5],
'open_time': to_datetime([Arrow(2017, 11, 13, 15, 40, 0).datetime, 'open_date': to_datetime([Arrow(2017, 11, 13, 15, 40, 0).datetime,
Arrow(2017, 11, 14, 9, 41, 0).datetime, Arrow(2017, 11, 14, 9, 41, 0).datetime,
Arrow(2017, 11, 14, 14, 20, 0).datetime, Arrow(2017, 11, 14, 14, 20, 0).datetime,
Arrow(2017, 11, 15, 3, 40, 0).datetime, Arrow(2017, 11, 15, 3, 40, 0).datetime,
], utc=True ], utc=True
), ),
'close_time': to_datetime([Arrow(2017, 11, 13, 16, 40, 0).datetime, 'close_date': to_datetime([Arrow(2017, 11, 13, 16, 40, 0).datetime,
Arrow(2017, 11, 14, 10, 41, 0).datetime, Arrow(2017, 11, 14, 10, 41, 0).datetime,
Arrow(2017, 11, 14, 15, 25, 0).datetime, Arrow(2017, 11, 14, 15, 25, 0).datetime,
Arrow(2017, 11, 15, 3, 55, 0).datetime, Arrow(2017, 11, 15, 3, 55, 0).datetime,
@ -81,10 +152,10 @@ def test_extract_trades_of_period(testdatadir):
trades1 = extract_trades_of_period(data, trades) trades1 = extract_trades_of_period(data, trades)
# First and last trade are dropped as they are out of range # First and last trade are dropped as they are out of range
assert len(trades1) == 2 assert len(trades1) == 2
assert trades1.iloc[0].open_time == Arrow(2017, 11, 14, 9, 41, 0).datetime assert trades1.iloc[0].open_date == Arrow(2017, 11, 14, 9, 41, 0).datetime
assert trades1.iloc[0].close_time == Arrow(2017, 11, 14, 10, 41, 0).datetime assert trades1.iloc[0].close_date == Arrow(2017, 11, 14, 10, 41, 0).datetime
assert trades1.iloc[-1].open_time == Arrow(2017, 11, 14, 14, 20, 0).datetime assert trades1.iloc[-1].open_date == Arrow(2017, 11, 14, 14, 20, 0).datetime
assert trades1.iloc[-1].close_time == Arrow(2017, 11, 14, 15, 25, 0).datetime assert trades1.iloc[-1].close_date == Arrow(2017, 11, 14, 15, 25, 0).datetime
def test_analyze_trade_parallelism(default_conf, mocker, testdatadir): def test_analyze_trade_parallelism(default_conf, mocker, testdatadir):
@ -105,7 +176,8 @@ def test_load_trades(default_conf, mocker):
load_trades("DB", load_trades("DB",
db_url=default_conf.get('db_url'), db_url=default_conf.get('db_url'),
exportfilename=default_conf.get('exportfilename'), exportfilename=default_conf.get('exportfilename'),
no_trades=False no_trades=False,
strategy="DefaultStrategy",
) )
assert db_mock.call_count == 1 assert db_mock.call_count == 1
@ -135,6 +207,14 @@ def test_load_trades(default_conf, mocker):
assert bt_mock.call_count == 0 assert bt_mock.call_count == 0
def test_calculate_market_change(testdatadir):
pairs = ["ETH/BTC", "ADA/BTC"]
data = load_data(datadir=testdatadir, pairs=pairs, timeframe='5m')
result = calculate_market_change(data)
assert isinstance(result, float)
assert pytest.approx(result) == 0.00955514
def test_combine_dataframes_with_mean(testdatadir): def test_combine_dataframes_with_mean(testdatadir):
pairs = ["ETH/BTC", "ADA/BTC"] pairs = ["ETH/BTC", "ADA/BTC"]
data = load_data(datadir=testdatadir, pairs=pairs, timeframe='5m') data = load_data(datadir=testdatadir, pairs=pairs, timeframe='5m')
@ -165,7 +245,7 @@ def test_create_cum_profit1(testdatadir):
filename = testdatadir / "backtest-result_test.json" filename = testdatadir / "backtest-result_test.json"
bt_data = load_backtest_data(filename) bt_data = load_backtest_data(filename)
# Move close-time to "off" the candle, to make sure the logic still works # Move close-time to "off" the candle, to make sure the logic still works
bt_data.loc[:, 'close_time'] = bt_data.loc[:, 'close_time'] + DateOffset(seconds=20) bt_data.loc[:, 'close_date'] = bt_data.loc[:, 'close_date'] + DateOffset(seconds=20)
timerange = TimeRange.parse_timerange("20180110-20180112") timerange = TimeRange.parse_timerange("20180110-20180112")
df = load_pair_history(pair="TRX/BTC", timeframe='5m', df = load_pair_history(pair="TRX/BTC", timeframe='5m',
@ -204,11 +284,11 @@ def test_calculate_max_drawdown2():
-0.033961, 0.010680, 0.010886, -0.029274, 0.011178, 0.010693, 0.010711] -0.033961, 0.010680, 0.010886, -0.029274, 0.011178, 0.010693, 0.010711]
dates = [Arrow(2020, 1, 1).shift(days=i) for i in range(len(values))] dates = [Arrow(2020, 1, 1).shift(days=i) for i in range(len(values))]
df = DataFrame(zip(values, dates), columns=['profit', 'open_time']) df = DataFrame(zip(values, dates), columns=['profit', 'open_date'])
# sort by profit and reset index # sort by profit and reset index
df = df.sort_values('profit').reset_index(drop=True) df = df.sort_values('profit').reset_index(drop=True)
df1 = df.copy() df1 = df.copy()
drawdown, h, low = calculate_max_drawdown(df, date_col='open_time', value_col='profit') drawdown, h, low = calculate_max_drawdown(df, date_col='open_date', value_col='profit')
# Ensure df has not been altered. # Ensure df has not been altered.
assert df.equals(df1) assert df.equals(df1)
@ -217,6 +297,6 @@ def test_calculate_max_drawdown2():
assert h < low assert h < low
assert drawdown == 0.091755 assert drawdown == 0.091755
df = DataFrame(zip(values[:5], dates[:5]), columns=['profit', 'open_time']) df = DataFrame(zip(values[:5], dates[:5]), columns=['profit', 'open_date'])
with pytest.raises(ValueError, match='No losing trade, therefore no drawdown.'): with pytest.raises(ValueError, match='No losing trade, therefore no drawdown.'):
calculate_max_drawdown(df, date_col='open_time', value_col='profit') calculate_max_drawdown(df, date_col='open_date', value_col='profit')

2
tests/data/test_history.py
View File

@ -36,7 +36,7 @@ def _backup_file(file: Path, copy_file: bool = False) -> None:
""" """
Backup existing file to avoid deleting the user file Backup existing file to avoid deleting the user file
:param file: complete path to the file :param file: complete path to the file
:param touch_file: create an empty file in replacement :param copy_file: keep file in place too.
:return: None :return: None
""" """
file_swp = str(file) + '.swp' file_swp = str(file) + '.swp'

46
tests/edge/test_edge.py
View File

@ -163,8 +163,8 @@ def test_edge_results(edge_conf, mocker, caplog, data) -> None:
for c, trade in enumerate(data.trades): for c, trade in enumerate(data.trades):
res = results.iloc[c] res = results.iloc[c]
assert res.exit_type == trade.sell_reason assert res.exit_type == trade.sell_reason
assert res.open_time == _get_frame_time_from_offset(trade.open_tick).replace(tzinfo=None) assert res.open_date == _get_frame_time_from_offset(trade.open_tick).replace(tzinfo=None)
assert res.close_time == _get_frame_time_from_offset(trade.close_tick).replace(tzinfo=None) assert res.close_date == _get_frame_time_from_offset(trade.close_tick).replace(tzinfo=None)
def test_adjust(mocker, edge_conf): def test_adjust(mocker, edge_conf):
@ -354,10 +354,8 @@ def test_process_expectancy(mocker, edge_conf, fee, risk_reward_ratio, expectanc
'stoploss': -0.9, 'stoploss': -0.9,
'profit_percent': '', 'profit_percent': '',
'profit_abs': '', 'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:05:00.000000000'), 'open_date': np.datetime64('2018-10-03T00:05:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:10:00.000000000'), 'close_date': np.datetime64('2018-10-03T00:10:00.000000000'),
'open_index': 1,
'close_index': 1,
'trade_duration': '', 'trade_duration': '',
'open_rate': 17, 'open_rate': 17,
'close_rate': 17, 'close_rate': 17,
@ -367,10 +365,8 @@ def test_process_expectancy(mocker, edge_conf, fee, risk_reward_ratio, expectanc
'stoploss': -0.9, 'stoploss': -0.9,
'profit_percent': '', 'profit_percent': '',
'profit_abs': '', 'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:20:00.000000000'), 'open_date': np.datetime64('2018-10-03T00:20:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:25:00.000000000'), 'close_date': np.datetime64('2018-10-03T00:25:00.000000000'),
'open_index': 4,
'close_index': 4,
'trade_duration': '', 'trade_duration': '',
'open_rate': 20, 'open_rate': 20,
'close_rate': 20, 'close_rate': 20,
@ -380,10 +376,8 @@ def test_process_expectancy(mocker, edge_conf, fee, risk_reward_ratio, expectanc
'stoploss': -0.9, 'stoploss': -0.9,
'profit_percent': '', 'profit_percent': '',
'profit_abs': '', 'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:30:00.000000000'), 'open_date': np.datetime64('2018-10-03T00:30:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:40:00.000000000'), 'close_date': np.datetime64('2018-10-03T00:40:00.000000000'),
'open_index': 6,
'close_index': 7,
'trade_duration': '', 'trade_duration': '',
'open_rate': 26, 'open_rate': 26,
'close_rate': 34, 'close_rate': 34,
@ -424,8 +418,8 @@ def test_process_expectancy_remove_pumps(mocker, edge_conf, fee,):
'stoploss': -0.9, 'stoploss': -0.9,
'profit_percent': '', 'profit_percent': '',
'profit_abs': '', 'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:05:00.000000000'), 'open_date': np.datetime64('2018-10-03T00:05:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:10:00.000000000'), 'close_date': np.datetime64('2018-10-03T00:10:00.000000000'),
'open_index': 1, 'open_index': 1,
'close_index': 1, 'close_index': 1,
'trade_duration': '', 'trade_duration': '',
@ -437,8 +431,8 @@ def test_process_expectancy_remove_pumps(mocker, edge_conf, fee,):
'stoploss': -0.9, 'stoploss': -0.9,
'profit_percent': '', 'profit_percent': '',
'profit_abs': '', 'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:20:00.000000000'), 'open_date': np.datetime64('2018-10-03T00:20:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:25:00.000000000'), 'close_date': np.datetime64('2018-10-03T00:25:00.000000000'),
'open_index': 4, 'open_index': 4,
'close_index': 4, 'close_index': 4,
'trade_duration': '', 'trade_duration': '',
@ -449,8 +443,8 @@ def test_process_expectancy_remove_pumps(mocker, edge_conf, fee,):
'stoploss': -0.9, 'stoploss': -0.9,
'profit_percent': '', 'profit_percent': '',
'profit_abs': '', 'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:20:00.000000000'), 'open_date': np.datetime64('2018-10-03T00:20:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:25:00.000000000'), 'close_date': np.datetime64('2018-10-03T00:25:00.000000000'),
'open_index': 4, 'open_index': 4,
'close_index': 4, 'close_index': 4,
'trade_duration': '', 'trade_duration': '',
@ -461,8 +455,8 @@ def test_process_expectancy_remove_pumps(mocker, edge_conf, fee,):
'stoploss': -0.9, 'stoploss': -0.9,
'profit_percent': '', 'profit_percent': '',
'profit_abs': '', 'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:20:00.000000000'), 'open_date': np.datetime64('2018-10-03T00:20:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:25:00.000000000'), 'close_date': np.datetime64('2018-10-03T00:25:00.000000000'),
'open_index': 4, 'open_index': 4,
'close_index': 4, 'close_index': 4,
'trade_duration': '', 'trade_duration': '',
@ -473,8 +467,8 @@ def test_process_expectancy_remove_pumps(mocker, edge_conf, fee,):
'stoploss': -0.9, 'stoploss': -0.9,
'profit_percent': '', 'profit_percent': '',
'profit_abs': '', 'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:20:00.000000000'), 'open_date': np.datetime64('2018-10-03T00:20:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:25:00.000000000'), 'close_date': np.datetime64('2018-10-03T00:25:00.000000000'),
'open_index': 4, 'open_index': 4,
'close_index': 4, 'close_index': 4,
'trade_duration': '', 'trade_duration': '',
@ -486,8 +480,8 @@ def test_process_expectancy_remove_pumps(mocker, edge_conf, fee,):
'stoploss': -0.9, 'stoploss': -0.9,
'profit_percent': '', 'profit_percent': '',
'profit_abs': '', 'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:30:00.000000000'), 'open_date': np.datetime64('2018-10-03T00:30:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:40:00.000000000'), 'close_date': np.datetime64('2018-10-03T00:40:00.000000000'),
'open_index': 6, 'open_index': 6,
'close_index': 7, 'close_index': 7,
'trade_duration': '', 'trade_duration': '',

4
tests/optimize/test_backtest_detail.py
View File

@ -395,5 +395,5 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
for c, trade in enumerate(data.trades): for c, trade in enumerate(data.trades):
res = results.iloc[c] res = results.iloc[c]
assert res.sell_reason == trade.sell_reason assert res.sell_reason == trade.sell_reason
assert res.open_time == _get_frame_time_from_offset(trade.open_tick) assert res.open_date == _get_frame_time_from_offset(trade.open_tick)
assert res.close_time == _get_frame_time_from_offset(trade.close_tick) assert res.close_date == _get_frame_time_from_offset(trade.close_tick)

58
tests/optimize/test_backtesting.py
View File

@ -354,8 +354,8 @@ def test_backtesting_start(default_conf, mocker, testdatadir, caplog) -> None:
exists = [ exists = [
'Using stake_currency: BTC ...', 'Using stake_currency: BTC ...',
'Using stake_amount: 0.001 ...', 'Using stake_amount: 0.001 ...',
'Backtesting with data from 2017-11-14T21:17:00+00:00 ' 'Backtesting with data from 2017-11-14 21:17:00 '
'up to 2017-11-14T22:59:00+00:00 (0 days)..' 'up to 2017-11-14 22:59:00 (0 days)..'
] ]
for line in exists: for line in exists:
assert log_has(line, caplog) assert log_has(line, caplog)
@ -464,28 +464,29 @@ def test_backtest(default_conf, fee, mocker, testdatadir) -> None:
{'pair': [pair, pair], {'pair': [pair, pair],
'profit_percent': [0.0, 0.0], 'profit_percent': [0.0, 0.0],
'profit_abs': [0.0, 0.0], 'profit_abs': [0.0, 0.0],
'open_time': pd.to_datetime([Arrow(2018, 1, 29, 18, 40, 0).datetime, 'open_date': pd.to_datetime([Arrow(2018, 1, 29, 18, 40, 0).datetime,
Arrow(2018, 1, 30, 3, 30, 0).datetime], utc=True Arrow(2018, 1, 30, 3, 30, 0).datetime], utc=True
), ),
'close_time': pd.to_datetime([Arrow(2018, 1, 29, 22, 35, 0).datetime, 'open_rate': [0.104445, 0.10302485],
'open_fee': [0.0025, 0.0025],
'close_date': pd.to_datetime([Arrow(2018, 1, 29, 22, 35, 0).datetime,
Arrow(2018, 1, 30, 4, 10, 0).datetime], utc=True), Arrow(2018, 1, 30, 4, 10, 0).datetime], utc=True),
'open_index': [78, 184], 'close_rate': [0.104969, 0.103541],
'close_index': [125, 192], 'close_fee': [0.0025, 0.0025],
'amount': [0.00957442, 0.0097064],
'trade_duration': [235, 40], 'trade_duration': [235, 40],
'open_at_end': [False, False], 'open_at_end': [False, False],
'open_rate': [0.104445, 0.10302485],
'close_rate': [0.104969, 0.103541],
'sell_reason': [SellType.ROI, SellType.ROI] 'sell_reason': [SellType.ROI, SellType.ROI]
}) })
pd.testing.assert_frame_equal(results, expected) pd.testing.assert_frame_equal(results, expected)
data_pair = processed[pair] data_pair = processed[pair]
for _, t in results.iterrows(): for _, t in results.iterrows():
ln = data_pair.loc[data_pair["date"] == t["open_time"]] ln = data_pair.loc[data_pair["date"] == t["open_date"]]
# Check open trade rate alignes to open rate # Check open trade rate alignes to open rate
assert ln is not None assert ln is not None
assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6) assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6)
# check close trade rate alignes to close rate or is between high and low # check close trade rate alignes to close rate or is between high and low
ln = data_pair.loc[data_pair["date"] == t["close_time"]] ln = data_pair.loc[data_pair["date"] == t["close_date"]]
assert (round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6) or assert (round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6) or
round(ln.iloc[0]["low"], 6) < round( round(ln.iloc[0]["low"], 6) < round(
t["close_rate"], 6) < round(ln.iloc[0]["high"], 6)) t["close_rate"], 6) < round(ln.iloc[0]["high"], 6))
@ -677,10 +678,10 @@ def test_backtest_start_timerange(default_conf, mocker, caplog, testdatadir):
f'Using data directory: {testdatadir} ...', f'Using data directory: {testdatadir} ...',
'Using stake_currency: BTC ...', 'Using stake_currency: BTC ...',
'Using stake_amount: 0.001 ...', 'Using stake_amount: 0.001 ...',
'Loading data from 2017-11-14T20:57:00+00:00 ' 'Loading data from 2017-11-14 20:57:00 '
'up to 2017-11-14T22:58:00+00:00 (0 days)..', 'up to 2017-11-14 22:58:00 (0 days)..',
'Backtesting with data from 2017-11-14T21:17:00+00:00 ' 'Backtesting with data from 2017-11-14 21:17:00 '
'up to 2017-11-14T22:58:00+00:00 (0 days)..', 'up to 2017-11-14 22:58:00 (0 days)..',
'Parameter --enable-position-stacking detected ...' 'Parameter --enable-position-stacking detected ...'
] ]
@ -707,6 +708,7 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
generate_pair_metrics=MagicMock(), generate_pair_metrics=MagicMock(),
generate_sell_reason_stats=sell_reason_mock, generate_sell_reason_stats=sell_reason_mock,
generate_strategy_metrics=strat_summary, generate_strategy_metrics=strat_summary,
generate_daily_stats=MagicMock(),
) )
patched_configuration_load_config_file(mocker, default_conf) patched_configuration_load_config_file(mocker, default_conf)
@ -740,10 +742,10 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
f'Using data directory: {testdatadir} ...', f'Using data directory: {testdatadir} ...',
'Using stake_currency: BTC ...', 'Using stake_currency: BTC ...',
'Using stake_amount: 0.001 ...', 'Using stake_amount: 0.001 ...',
'Loading data from 2017-11-14T20:57:00+00:00 ' 'Loading data from 2017-11-14 20:57:00 '
'up to 2017-11-14T22:58:00+00:00 (0 days)..', 'up to 2017-11-14 22:58:00 (0 days)..',
'Backtesting with data from 2017-11-14T21:17:00+00:00 ' 'Backtesting with data from 2017-11-14 21:17:00 '
'up to 2017-11-14T22:58:00+00:00 (0 days)..', 'up to 2017-11-14 22:58:00 (0 days)..',
'Parameter --enable-position-stacking detected ...', 'Parameter --enable-position-stacking detected ...',
'Running backtesting for Strategy DefaultStrategy', 'Running backtesting for Strategy DefaultStrategy',
'Running backtesting for Strategy TestStrategyLegacy', 'Running backtesting for Strategy TestStrategyLegacy',
@ -761,13 +763,11 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC'], pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC'],
'profit_percent': [0.0, 0.0], 'profit_percent': [0.0, 0.0],
'profit_abs': [0.0, 0.0], 'profit_abs': [0.0, 0.0],
'open_time': pd.to_datetime(['2018-01-29 18:40:00', 'open_date': pd.to_datetime(['2018-01-29 18:40:00',
'2018-01-30 03:30:00', ], utc=True '2018-01-30 03:30:00', ], utc=True
), ),
'close_time': pd.to_datetime(['2018-01-29 20:45:00', 'close_date': pd.to_datetime(['2018-01-29 20:45:00',
'2018-01-30 05:35:00', ], utc=True), '2018-01-30 05:35:00', ], utc=True),
'open_index': [78, 184],
'close_index': [125, 192],
'trade_duration': [235, 40], 'trade_duration': [235, 40],
'open_at_end': [False, False], 'open_at_end': [False, False],
'open_rate': [0.104445, 0.10302485], 'open_rate': [0.104445, 0.10302485],
@ -777,15 +777,13 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC', 'ETH/BTC'], pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC', 'ETH/BTC'],
'profit_percent': [0.03, 0.01, 0.1], 'profit_percent': [0.03, 0.01, 0.1],
'profit_abs': [0.01, 0.02, 0.2], 'profit_abs': [0.01, 0.02, 0.2],
'open_time': pd.to_datetime(['2018-01-29 18:40:00', 'open_date': pd.to_datetime(['2018-01-29 18:40:00',
'2018-01-30 03:30:00', '2018-01-30 03:30:00',
'2018-01-30 05:30:00'], utc=True '2018-01-30 05:30:00'], utc=True
), ),
'close_time': pd.to_datetime(['2018-01-29 20:45:00', 'close_date': pd.to_datetime(['2018-01-29 20:45:00',
'2018-01-30 05:35:00', '2018-01-30 05:35:00',
'2018-01-30 08:30:00'], utc=True), '2018-01-30 08:30:00'], utc=True),
'open_index': [78, 184, 185],
'close_index': [125, 224, 205],
'trade_duration': [47, 40, 20], 'trade_duration': [47, 40, 20],
'open_at_end': [False, False, False], 'open_at_end': [False, False, False],
'open_rate': [0.104445, 0.10302485, 0.122541], 'open_rate': [0.104445, 0.10302485, 0.122541],
@ -823,10 +821,10 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
f'Using data directory: {testdatadir} ...', f'Using data directory: {testdatadir} ...',
'Using stake_currency: BTC ...', 'Using stake_currency: BTC ...',
'Using stake_amount: 0.001 ...', 'Using stake_amount: 0.001 ...',
'Loading data from 2017-11-14T20:57:00+00:00 ' 'Loading data from 2017-11-14 20:57:00 '
'up to 2017-11-14T22:58:00+00:00 (0 days)..', 'up to 2017-11-14 22:58:00 (0 days)..',
'Backtesting with data from 2017-11-14T21:17:00+00:00 ' 'Backtesting with data from 2017-11-14 21:17:00 '
'up to 2017-11-14T22:58:00+00:00 (0 days)..', 'up to 2017-11-14 22:58:00 (0 days)..',
'Parameter --enable-position-stacking detected ...', 'Parameter --enable-position-stacking detected ...',
'Running backtesting for Strategy DefaultStrategy', 'Running backtesting for Strategy DefaultStrategy',
'Running backtesting for Strategy TestStrategyLegacy', 'Running backtesting for Strategy TestStrategyLegacy',

2
tests/optimize/test_hyperopt.py
View File

@ -59,7 +59,7 @@ def hyperopt_results():
'profit_abs': [-0.2, 0.4, 0.6], 'profit_abs': [-0.2, 0.4, 0.6],
'trade_duration': [10, 30, 10], 'trade_duration': [10, 30, 10],
'sell_reason': [SellType.STOP_LOSS, SellType.ROI, SellType.ROI], 'sell_reason': [SellType.STOP_LOSS, SellType.ROI, SellType.ROI],
'close_time': 'close_date':
[ [
datetime(2019, 1, 1, 9, 26, 3, 478039), datetime(2019, 1, 1, 9, 26, 3, 478039),
datetime(2019, 2, 1, 9, 26, 3, 478039), datetime(2019, 2, 1, 9, 26, 3, 478039),

221
tests/optimize/test_optimize_reports.py
View File

@ -1,16 +1,29 @@
import re
from datetime import timedelta
from pathlib import Path from pathlib import Path
import pandas as pd import pandas as pd
import pytest import pytest
from arrow import Arrow from arrow import Arrow
from freqtrade.configuration import TimeRange
from freqtrade.constants import LAST_BT_RESULT_FN
from freqtrade.data import history
from freqtrade.data.btanalysis import (get_latest_backtest_filename,
load_backtest_data)
from freqtrade.edge import PairInfo from freqtrade.edge import PairInfo
from freqtrade.optimize.optimize_reports import ( from freqtrade.optimize.optimize_reports import (generate_backtest_stats,
generate_pair_metrics, generate_edge_table, generate_sell_reason_stats, generate_daily_stats,
text_table_bt_results, text_table_sell_reason, generate_strategy_metrics, generate_edge_table,
text_table_strategy, store_backtest_result) generate_pair_metrics,
generate_sell_reason_stats,
generate_strategy_metrics,
store_backtest_stats,
text_table_bt_results,
text_table_sell_reason,
text_table_strategy)
from freqtrade.strategy.interface import SellType from freqtrade.strategy.interface import SellType
from tests.conftest import patch_exchange from tests.data.test_history import _backup_file, _clean_test_file
def test_text_table_bt_results(default_conf, mocker): def test_text_table_bt_results(default_conf, mocker):
@ -43,6 +56,115 @@ def test_text_table_bt_results(default_conf, mocker):
assert text_table_bt_results(pair_results, stake_currency='BTC') == result_str assert text_table_bt_results(pair_results, stake_currency='BTC') == result_str
def test_generate_backtest_stats(default_conf, testdatadir):
results = {'DefStrat': pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
"UNITTEST/BTC", "UNITTEST/BTC"],
"profit_percent": [0.003312, 0.010801, 0.013803, 0.002780],
"profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
"open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
Arrow(2017, 11, 14, 21, 36, 00).datetime,
Arrow(2017, 11, 14, 22, 12, 00).datetime,
Arrow(2017, 11, 14, 22, 44, 00).datetime],
"close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
Arrow(2017, 11, 14, 22, 10, 00).datetime,
Arrow(2017, 11, 14, 22, 43, 00).datetime,
Arrow(2017, 11, 14, 22, 58, 00).datetime],
"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
"close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
"trade_duration": [123, 34, 31, 14],
"open_at_end": [False, False, False, True],
"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
SellType.ROI, SellType.FORCE_SELL]
})}
timerange = TimeRange.parse_timerange('1510688220-1510700340')
min_date = Arrow.fromtimestamp(1510688220)
max_date = Arrow.fromtimestamp(1510700340)
btdata = history.load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange,
fill_up_missing=True)
stats = generate_backtest_stats(default_conf, btdata, results, min_date, max_date)
assert isinstance(stats, dict)
assert 'strategy' in stats
assert 'DefStrat' in stats['strategy']
assert 'strategy_comparison' in stats
strat_stats = stats['strategy']['DefStrat']
assert strat_stats['backtest_start'] == min_date.datetime
assert strat_stats['backtest_end'] == max_date.datetime
assert strat_stats['total_trades'] == len(results['DefStrat'])
# Above sample had no loosing trade
assert strat_stats['max_drawdown'] == 0.0
results = {'DefStrat': pd.DataFrame(
{"pair": ["UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC"],
"profit_percent": [0.003312, 0.010801, -0.013803, 0.002780],
"profit_abs": [0.000003, 0.000011, -0.000014, 0.000003],
"open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
Arrow(2017, 11, 14, 21, 36, 00).datetime,
Arrow(2017, 11, 14, 22, 12, 00).datetime,
Arrow(2017, 11, 14, 22, 44, 00).datetime],
"close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
Arrow(2017, 11, 14, 22, 10, 00).datetime,
Arrow(2017, 11, 14, 22, 43, 00).datetime,
Arrow(2017, 11, 14, 22, 58, 00).datetime],
"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
"close_rate": [0.002546, 0.003014, 0.0032903, 0.003217],
"trade_duration": [123, 34, 31, 14],
"open_at_end": [False, False, False, True],
"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
SellType.ROI, SellType.FORCE_SELL]
})}
assert strat_stats['max_drawdown'] == 0.0
assert strat_stats['drawdown_start'] == Arrow.fromtimestamp(0).datetime
assert strat_stats['drawdown_end'] == Arrow.fromtimestamp(0).datetime
assert strat_stats['drawdown_end_ts'] == 0
assert strat_stats['drawdown_start_ts'] == 0
assert strat_stats['pairlist'] == ['UNITTEST/BTC']
# Test storing stats
filename = Path(testdatadir / 'btresult.json')
filename_last = Path(testdatadir / LAST_BT_RESULT_FN)
_backup_file(filename_last, copy_file=True)
assert not filename.is_file()
store_backtest_stats(filename, stats)
# get real Filename (it's btresult-<date>.json)
last_fn = get_latest_backtest_filename(filename_last.parent)
assert re.match(r"btresult-.*\.json", last_fn)
filename1 = (testdatadir / last_fn)
assert filename1.is_file()
content = filename1.read_text()
assert 'max_drawdown' in content
assert 'strategy' in content
assert 'pairlist' in content
assert filename_last.is_file()
_clean_test_file(filename_last)
filename1.unlink()
def test_store_backtest_stats(testdatadir, mocker):
dump_mock = mocker.patch('freqtrade.optimize.optimize_reports.file_dump_json')
store_backtest_stats(testdatadir, {})
assert dump_mock.call_count == 2
assert isinstance(dump_mock.call_args_list[0][0][0], Path)
assert str(dump_mock.call_args_list[0][0][0]).startswith(str(testdatadir/'backtest-result'))
dump_mock.reset_mock()
filename = testdatadir / 'testresult.json'
store_backtest_stats(filename, {})
assert dump_mock.call_count == 2
assert isinstance(dump_mock.call_args_list[0][0][0], Path)
# result will be testdatadir / testresult-<timestamp>.json
assert str(dump_mock.call_args_list[0][0][0]).startswith(str(testdatadir / 'testresult'))
def test_generate_pair_metrics(default_conf, mocker): def test_generate_pair_metrics(default_conf, mocker):
results = pd.DataFrame( results = pd.DataFrame(
@ -68,6 +190,21 @@ def test_generate_pair_metrics(default_conf, mocker):
pytest.approx(pair_results[-1]['profit_sum_pct']) == pair_results[-1]['profit_sum'] * 100) pytest.approx(pair_results[-1]['profit_sum_pct']) == pair_results[-1]['profit_sum'] * 100)
def test_generate_daily_stats(testdatadir):
filename = testdatadir / "backtest-result_new.json"
bt_data = load_backtest_data(filename)
res = generate_daily_stats(bt_data)
assert isinstance(res, dict)
assert round(res['backtest_best_day'], 4) == 0.1796
assert round(res['backtest_worst_day'], 4) == -0.1468
assert res['winning_days'] == 14
assert res['draw_days'] == 4
assert res['losing_days'] == 3
assert res['winner_holding_avg'] == timedelta(seconds=1440)
assert res['loser_holding_avg'] == timedelta(days=1, seconds=21420)
def test_text_table_sell_reason(default_conf): def test_text_table_sell_reason(default_conf):
results = pd.DataFrame( results = pd.DataFrame(
@ -188,77 +325,3 @@ def test_generate_edge_table(edge_conf, mocker):
assert generate_edge_table(results).count('| ETH/BTC |') == 1 assert generate_edge_table(results).count('| ETH/BTC |') == 1
assert generate_edge_table(results).count( assert generate_edge_table(results).count(
'| Risk Reward Ratio | Required Risk Reward | Expectancy |') == 1 '| Risk Reward Ratio | Required Risk Reward | Expectancy |') == 1
def test_backtest_record(default_conf, fee, mocker):
names = []
records = []
patch_exchange(mocker)
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
mocker.patch(
'freqtrade.optimize.optimize_reports.file_dump_json',
new=lambda n, r: (names.append(n), records.append(r))
)
results = {'DefStrat': pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
"UNITTEST/BTC", "UNITTEST/BTC"],
"profit_percent": [0.003312, 0.010801, 0.013803, 0.002780],
"profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
"open_time": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
Arrow(2017, 11, 14, 21, 36, 00).datetime,
Arrow(2017, 11, 14, 22, 12, 00).datetime,
Arrow(2017, 11, 14, 22, 44, 00).datetime],
"close_time": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
Arrow(2017, 11, 14, 22, 10, 00).datetime,
Arrow(2017, 11, 14, 22, 43, 00).datetime,
Arrow(2017, 11, 14, 22, 58, 00).datetime],
"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
"close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
"open_index": [1, 119, 153, 185],
"close_index": [118, 151, 184, 199],
"trade_duration": [123, 34, 31, 14],
"open_at_end": [False, False, False, True],
"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
SellType.ROI, SellType.FORCE_SELL]
})}
store_backtest_result(Path("backtest-result.json"), results)
# Assert file_dump_json was only called once
assert names == [Path('backtest-result.json')]
records = records[0]
# Ensure records are of correct type
assert len(records) == 4
# reset test to test with strategy name
names = []
records = []
results['Strat'] = results['DefStrat']
results['Strat2'] = results['DefStrat']
store_backtest_result(Path("backtest-result.json"), results)
assert names == [
Path('backtest-result-DefStrat.json'),
Path('backtest-result-Strat.json'),
Path('backtest-result-Strat2.json'),
]
records = records[0]
# Ensure records are of correct type
assert len(records) == 4
# ('UNITTEST/BTC', 0.00331158, '1510684320', '1510691700', 0, 117)
# Below follows just a typecheck of the schema/type of trade-records
oix = None
for (pair, profit, date_buy, date_sell, buy_index, dur,
openr, closer, open_at_end, sell_reason) in records:
assert pair == 'UNITTEST/BTC'
assert isinstance(profit, float)
# FIX: buy/sell should be converted to ints
assert isinstance(date_buy, float)
assert isinstance(date_sell, float)
assert isinstance(openr, float)
assert isinstance(closer, float)
assert isinstance(open_at_end, bool)
assert isinstance(sell_reason, str)
isinstance(buy_index, pd._libs.tslib.Timestamp)
if oix:
assert buy_index > oix
oix = buy_index
assert dur > 0

2
tests/test_freqtradebot.py
View File

@ -320,7 +320,7 @@ def test_edge_overrides_stoploss(limit_buy_order, fee, caplog, mocker, edge_conf
# stoploss shoud be hit # stoploss shoud be hit
assert freqtrade.handle_trade(trade) is True assert freqtrade.handle_trade(trade) is True
assert log_has('Executing Sell for NEO/BTC. Reason: SellType.STOP_LOSS', caplog) assert log_has('Executing Sell for NEO/BTC. Reason: stop_loss', caplog)
assert trade.sell_reason == SellType.STOP_LOSS.value assert trade.sell_reason == SellType.STOP_LOSS.value

2
tests/test_plotting.py
View File

@ -267,7 +267,7 @@ def test_generate_profit_graph(testdatadir):
trades = load_backtest_data(filename) trades = load_backtest_data(filename)
timerange = TimeRange.parse_timerange("20180110-20180112") timerange = TimeRange.parse_timerange("20180110-20180112")
pairs = ["TRX/BTC", "XLM/BTC"] pairs = ["TRX/BTC", "XLM/BTC"]
trades = trades[trades['close_time'] < pd.Timestamp('2018-01-12', tz='UTC')] trades = trades[trades['close_date'] < pd.Timestamp('2018-01-12', tz='UTC')]
data = history.load_data(datadir=testdatadir, data = history.load_data(datadir=testdatadir,
pairs=pairs, pairs=pairs,

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tests/testdata/.last_result.json vendored Normal file
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@ -0,0 +1 @@
{"latest_backtest":"backtest-result_new.json"}

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