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Merge branch 'develop' into progress-bar

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This commit is contained in:
Fredrik81
2020-04-08 01:56:43 +02:00
parent 132f5f73f5 9387585756
commit cdc774549e
63 changed files with 1025 additions and 740 deletions
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6
freqtrade/commands/arguments.py
View File

@@ -59,7 +59,7 @@ ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "download_trades", "exchang
ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
"db_url", "trade_source", "export", "exportfilename",
"timerange", "ticker_interval"]
"timerange", "ticker_interval", "no_trades"]
ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
"trade_source", "ticker_interval"]
@@ -297,7 +297,7 @@ class Arguments:
# Add convert-data subcommand
convert_data_cmd = subparsers.add_parser(
'convert-data',
help='Convert OHLCV data from one format to another.',
help='Convert candle (OHLCV) data from one format to another.',
parents=[_common_parser],
)
convert_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=True))
@@ -306,7 +306,7 @@ class Arguments:
# Add convert-trade-data subcommand
convert_trade_data_cmd = subparsers.add_parser(
'convert-trade-data',
help='Convert trade-data from one format to another.',
help='Convert trade data from one format to another.',
parents=[_common_parser],
)
convert_trade_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=False))

2
freqtrade/commands/build_config_commands.py
View File

@@ -76,7 +76,7 @@ def ask_user_config() -> Dict[str, Any]:
{
"type": "text",
"name": "ticker_interval",
"message": "Please insert your ticker interval:",
"message": "Please insert your timeframe (ticker interval):",
"default": "5m",
},
{

7
freqtrade/commands/cli_options.py
View File

@@ -355,7 +355,7 @@ AVAILABLE_CLI_OPTIONS = {
),
"dataformat_ohlcv": Arg(
'--data-format-ohlcv',
help='Storage format for downloaded ohlcv data. (default: `%(default)s`).',
help='Storage format for downloaded candle (OHLCV) data. (default: `%(default)s`).',
choices=constants.AVAILABLE_DATAHANDLERS,
default='json'
),
@@ -413,6 +413,11 @@ AVAILABLE_CLI_OPTIONS = {
metavar='INT',
default=750,
),
"no_trades": Arg(
'--no-trades',
help='Skip using trades from backtesting file and DB.',
action='store_true',
),
"trade_source": Arg(
'--trade-source',
help='Specify the source for trades (Can be DB or file (backtest file)) '

4
freqtrade/configuration/configuration.py
View File

@@ -196,6 +196,7 @@ class Configuration:
if self.args.get('exportfilename'):
self._args_to_config(config, argname='exportfilename',
logstring='Storing backtest results to {} ...')
config['exportfilename'] = Path(config['exportfilename'])
else:
config['exportfilename'] = (config['user_data_dir']
/ 'backtest_results/backtest-result.json')
@@ -358,6 +359,9 @@ class Configuration:
self._args_to_config(config, argname='erase',
logstring='Erase detected. Deleting existing data.')
self._args_to_config(config, argname='no_trades',
logstring='Parameter --no-trades detected.')
self._args_to_config(config, argname='timeframes',
logstring='timeframes --timeframes: {}')

33
freqtrade/configuration/load_config.py
View File

@@ -1,13 +1,15 @@
"""
This module contain functions to load the configuration file
"""
import rapidjson
import logging
import re
import sys
from pathlib import Path
from typing import Any, Dict
from freqtrade.exceptions import OperationalException
import rapidjson
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
@@ -15,6 +17,26 @@ logger = logging.getLogger(__name__)
CONFIG_PARSE_MODE = rapidjson.PM_COMMENTS | rapidjson.PM_TRAILING_COMMAS
def log_config_error_range(path: str, errmsg: str) -> str:
"""
Parses configuration file and prints range around error
"""
if path != '-':
offsetlist = re.findall(r'(?<=Parse\serror\sat\soffset\s)\d+', errmsg)
if offsetlist:
offset = int(offsetlist[0])
text = Path(path).read_text()
# Fetch an offset of 80 characters around the error line
subtext = text[offset-min(80, offset):offset+80]
segments = subtext.split('\n')
if len(segments) > 3:
# Remove first and last lines, to avoid odd truncations
return '\n'.join(segments[1:-1])
else:
return subtext
return ''
def load_config_file(path: str) -> Dict[str, Any]:
"""
Loads a config file from the given path
@@ -29,5 +51,12 @@ def load_config_file(path: str) -> Dict[str, Any]:
raise OperationalException(
f'Config file "{path}" not found!'
' Please create a config file or check whether it exists.')
except rapidjson.JSONDecodeError as e:
err_range = log_config_error_range(path, str(e))
raise OperationalException(
f'{e}\n'
f'Please verify the following segment of your configuration:\n{err_range}'
if err_range else 'Please verify your configuration file for syntax errors.'
)
return config

2
freqtrade/configuration/timerange.py
View File

@@ -45,7 +45,7 @@ class TimeRange:
"""
Adjust startts by <startup_candles> candles.
Applies only if no startup-candles have been available.
:param timeframe_secs: Ticker timeframe in seconds e.g. `timeframe_to_seconds('5m')`
:param timeframe_secs: Timeframe in seconds e.g. `timeframe_to_seconds('5m')`
:param startup_candles: Number of candles to move start-date forward
:param min_date: Minimum data date loaded. Key kriterium to decide if start-time
has to be moved

39
freqtrade/data/btanalysis.py
View File

@@ -111,7 +111,7 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
t.calc_profit(), t.calc_profit_ratio(),
t.open_rate, t.close_rate, t.amount,
(round((t.close_date.timestamp() - t.open_date.timestamp()) / 60, 2)
if t.close_date else None),
if t.close_date else None),
t.sell_reason,
t.fee_open, t.fee_close,
t.open_rate_requested,
@@ -129,39 +129,56 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
return trades
def load_trades(source: str, db_url: str, exportfilename: str) -> pd.DataFrame:
def load_trades(source: str, db_url: str, exportfilename: Path,
no_trades: bool = False) -> pd.DataFrame:
"""
Based on configuration option "trade_source":
* loads data from DB (using `db_url`)
* loads data from backtestfile (using `exportfilename`)
:param source: "DB" or "file" - specify source to load from
:param db_url: sqlalchemy formatted url to a database
:param exportfilename: Json file generated by backtesting
:param no_trades: Skip using trades, only return backtesting data columns
:return: DataFrame containing trades
"""
if no_trades:
df = pd.DataFrame(columns=BT_DATA_COLUMNS)
return df
if source == "DB":
return load_trades_from_db(db_url)
elif source == "file":
return load_backtest_data(Path(exportfilename))
return load_backtest_data(exportfilename)
def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame) -> pd.DataFrame:
def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
date_index=False) -> pd.DataFrame:
"""
Compare trades and backtested pair DataFrames to get trades performed on backtested period
:return: the DataFrame of a trades of period
"""
trades = trades.loc[(trades['open_time'] >= dataframe.iloc[0]['date']) &
(trades['close_time'] <= dataframe.iloc[-1]['date'])]
if date_index:
trades_start = dataframe.index[0]
trades_stop = dataframe.index[-1]
else:
trades_start = dataframe.iloc[0]['date']
trades_stop = dataframe.iloc[-1]['date']
trades = trades.loc[(trades['open_time'] >= trades_start) &
(trades['close_time'] <= trades_stop)]
return trades
def combine_tickers_with_mean(tickers: Dict[str, pd.DataFrame],
column: str = "close") -> pd.DataFrame:
def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
column: str = "close") -> pd.DataFrame:
"""
Combine multiple dataframes "column"
:param tickers: Dict of Dataframes, dict key should be pair.
:param data: Dict of Dataframes, dict key should be pair.
:param column: Column in the original dataframes to use
:return: DataFrame with the column renamed to the dict key, and a column
named mean, containing the mean of all pairs.
"""
df_comb = pd.concat([tickers[pair].set_index('date').rename(
{column: pair}, axis=1)[pair] for pair in tickers], axis=1)
df_comb = pd.concat([data[pair].set_index('date').rename(
{column: pair}, axis=1)[pair] for pair in data], axis=1)
df_comb['mean'] = df_comb.mean(axis=1)

55
freqtrade/data/converter.py
View File

@@ -13,12 +13,12 @@ from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
logger = logging.getLogger(__name__)
def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
fill_missing: bool = True,
drop_incomplete: bool = True) -> DataFrame:
def ohlcv_to_dataframe(ohlcv: list, timeframe: str, pair: str, *,
fill_missing: bool = True, drop_incomplete: bool = True) -> DataFrame:
"""
Converts a ticker-list (format ccxt.fetch_ohlcv) to a Dataframe
:param ticker: ticker list, as returned by exchange.async_get_candle_history
Converts a list with candle (OHLCV) data (in format returned by ccxt.fetch_ohlcv)
to a Dataframe
:param ohlcv: list with candle (OHLCV) data, as returned by exchange.async_get_candle_history
:param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
:param pair: Pair this data is for (used to warn if fillup was necessary)
:param fill_missing: fill up missing candles with 0 candles
@@ -26,21 +26,18 @@ def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
:param drop_incomplete: Drop the last candle of the dataframe, assuming it's incomplete
:return: DataFrame
"""
logger.debug("Parsing tickerlist to dataframe")
logger.debug(f"Converting candle (OHLCV) data to dataframe for pair {pair}.")
cols = DEFAULT_DATAFRAME_COLUMNS
frame = DataFrame(ticker, columns=cols)
df = DataFrame(ohlcv, columns=cols)
frame['date'] = to_datetime(frame['date'],
unit='ms',
utc=True,
infer_datetime_format=True)
df['date'] = to_datetime(df['date'], unit='ms', utc=True, infer_datetime_format=True)
# Some exchanges return int values for volume and even for ohlc.
# Some exchanges return int values for Volume and even for OHLC.
# Convert them since TA-LIB indicators used in the strategy assume floats
# and fail with exception...
frame = frame.astype(dtype={'open': 'float', 'high': 'float', 'low': 'float', 'close': 'float',
'volume': 'float'})
return clean_ohlcv_dataframe(frame, timeframe, pair,
df = df.astype(dtype={'open': 'float', 'high': 'float', 'low': 'float', 'close': 'float',
'volume': 'float'})
return clean_ohlcv_dataframe(df, timeframe, pair,
fill_missing=fill_missing,
drop_incomplete=drop_incomplete)
@@ -49,11 +46,11 @@ def clean_ohlcv_dataframe(data: DataFrame, timeframe: str, pair: str, *,
fill_missing: bool = True,
drop_incomplete: bool = True) -> DataFrame:
"""
Clense a ohlcv dataframe by
Clense a OHLCV dataframe by
* Grouping it by date (removes duplicate tics)
* dropping last candles if requested
* Filling up missing data (if requested)
:param data: DataFrame containing ohlcv data.
:param data: DataFrame containing candle (OHLCV) data.
:param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
:param pair: Pair this data is for (used to warn if fillup was necessary)
:param fill_missing: fill up missing candles with 0 candles
@@ -88,16 +85,16 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
"""
from freqtrade.exchange import timeframe_to_minutes
ohlc_dict = {
ohlcv_dict = {
'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last',
'volume': 'sum'
}
ticker_minutes = timeframe_to_minutes(timeframe)
timeframe_minutes = timeframe_to_minutes(timeframe)
# Resample to create "NAN" values
df = dataframe.resample(f'{ticker_minutes}min', on='date').agg(ohlc_dict)
df = dataframe.resample(f'{timeframe_minutes}min', on='date').agg(ohlcv_dict)
# Forwardfill close for missing columns
df['close'] = df['close'].fillna(method='ffill')
@@ -159,20 +156,20 @@ def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
def trades_to_ohlcv(trades: list, timeframe: str) -> DataFrame:
"""
Converts trades list to ohlcv list
Converts trades list to OHLCV list
TODO: This should get a dedicated test
:param trades: List of trades, as returned by ccxt.fetch_trades.
:param timeframe: Ticker timeframe to resample data to
:return: ohlcv Dataframe.
:param timeframe: Timeframe to resample data to
:return: OHLCV Dataframe.
"""
from freqtrade.exchange import timeframe_to_minutes
ticker_minutes = timeframe_to_minutes(timeframe)
timeframe_minutes = timeframe_to_minutes(timeframe)
df = pd.DataFrame(trades)
df['datetime'] = pd.to_datetime(df['datetime'])
df = df.set_index('datetime')
df_new = df['price'].resample(f'{ticker_minutes}min').ohlc()
df_new['volume'] = df['amount'].resample(f'{ticker_minutes}min').sum()
df_new = df['price'].resample(f'{timeframe_minutes}min').ohlc()
df_new['volume'] = df['amount'].resample(f'{timeframe_minutes}min').sum()
df_new['date'] = df_new.index
# Drop 0 volume rows
df_new = df_new.dropna()
@@ -206,7 +203,7 @@ def convert_trades_format(config: Dict[str, Any], convert_from: str, convert_to:
def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
"""
Convert ohlcv from one format to another format.
Convert OHLCV from one format to another
:param config: Config dictionary
:param convert_from: Source format
:param convert_to: Target format
@@ -216,7 +213,7 @@ def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to:
src = get_datahandler(config['datadir'], convert_from)
trg = get_datahandler(config['datadir'], convert_to)
timeframes = config.get('timeframes', [config.get('ticker_interval')])
logger.info(f"Converting OHLCV for timeframe {timeframes}")
logger.info(f"Converting candle (OHLCV) for timeframe {timeframes}")
if 'pairs' not in config:
config['pairs'] = []
@@ -224,7 +221,7 @@ def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to:
for timeframe in timeframes:
config['pairs'].extend(src.ohlcv_get_pairs(config['datadir'],
timeframe))
logger.info(f"Converting OHLCV for {config['pairs']}")
logger.info(f"Converting candle (OHLCV) data for {config['pairs']}")
for timeframe in timeframes:
for pair in config['pairs']:

14
freqtrade/data/dataprovider.py
View File

@@ -1,7 +1,7 @@
"""
Dataprovider
Responsible to provide data to the bot
including Klines, tickers, historic data
including ticker and orderbook data, live and historical candle (OHLCV) data
Common Interface for bot and strategy to access data.
"""
import logging
@@ -43,10 +43,10 @@ class DataProvider:
def ohlcv(self, pair: str, timeframe: str = None, copy: bool = True) -> DataFrame:
"""
Get ohlcv data for the given pair as DataFrame
Get candle (OHLCV) data for the given pair as DataFrame
Please use the `available_pairs` method to verify which pairs are currently cached.
:param pair: pair to get the data for
:param timeframe: Ticker timeframe to get data for
:param timeframe: Timeframe to get data for
:param copy: copy dataframe before returning if True.
Use False only for read-only operations (where the dataframe is not modified)
"""
@@ -58,7 +58,7 @@ class DataProvider:
def historic_ohlcv(self, pair: str, timeframe: str = None) -> DataFrame:
"""
Get stored historic ohlcv data
Get stored historical candle (OHLCV) data
:param pair: pair to get the data for
:param timeframe: timeframe to get data for
"""
@@ -69,17 +69,17 @@ class DataProvider:
def get_pair_dataframe(self, pair: str, timeframe: str = None) -> DataFrame:
"""
Return pair ohlcv data, either live or cached historical -- depending
Return pair candle (OHLCV) data, either live or cached historical -- depending
on the runmode.
:param pair: pair to get the data for
:param timeframe: timeframe to get data for
:return: Dataframe for this pair
"""
if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
# Get live ohlcv data.
# Get live OHLCV data.
data = self.ohlcv(pair=pair, timeframe=timeframe)
else:
# Get historic ohlcv data (cached on disk).
# Get historical OHLCV data (cached on disk).
data = self.historic_ohlcv(pair=pair, timeframe=timeframe)
if len(data) == 0:
logger.warning(f"No data found for ({pair}, {timeframe}).")

22
freqtrade/data/history/history_utils.py
View File

@@ -9,7 +9,7 @@ from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
from freqtrade.data.converter import parse_ticker_dataframe, trades_to_ohlcv
from freqtrade.data.converter import ohlcv_to_dataframe, trades_to_ohlcv
from freqtrade.data.history.idatahandler import IDataHandler, get_datahandler
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import Exchange
@@ -28,10 +28,10 @@ def load_pair_history(pair: str,
data_handler: IDataHandler = None,
) -> DataFrame:
"""
Load cached ticker history for the given pair.
Load cached ohlcv history for the given pair.
:param pair: Pair to load data for
:param timeframe: Ticker timeframe (e.g. "5m")
:param timeframe: Timeframe (e.g. "5m")
:param datadir: Path to the data storage location.
:param data_format: Format of the data. Ignored if data_handler is set.
:param timerange: Limit data to be loaded to this timerange
@@ -63,10 +63,10 @@ def load_data(datadir: Path,
data_format: str = 'json',
) -> Dict[str, DataFrame]:
"""
Load ticker history data for a list of pairs.
Load ohlcv history data for a list of pairs.
:param datadir: Path to the data storage location.
:param timeframe: Ticker Timeframe (e.g. "5m")
:param timeframe: Timeframe (e.g. "5m")
:param pairs: List of pairs to load
:param timerange: Limit data to be loaded to this timerange
:param fill_up_missing: Fill missing values with "No action"-candles
@@ -104,10 +104,10 @@ def refresh_data(datadir: Path,
timerange: Optional[TimeRange] = None,
) -> None:
"""
Refresh ticker history data for a list of pairs.
Refresh ohlcv history data for a list of pairs.
:param datadir: Path to the data storage location.
:param timeframe: Ticker Timeframe (e.g. "5m")
:param timeframe: Timeframe (e.g. "5m")
:param pairs: List of pairs to load
:param exchange: Exchange object
:param timerange: Limit data to be loaded to this timerange
@@ -165,7 +165,7 @@ def _download_pair_history(datadir: Path,
Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
:param pair: pair to download
:param timeframe: Ticker Timeframe (e.g 5m)
:param timeframe: Timeframe (e.g "5m")
:param timerange: range of time to download
:return: bool with success state
"""
@@ -194,8 +194,8 @@ def _download_pair_history(datadir: Path,
days=-30).float_timestamp) * 1000
)
# TODO: Maybe move parsing to exchange class (?)
new_dataframe = parse_ticker_dataframe(new_data, timeframe, pair,
fill_missing=False, drop_incomplete=True)
new_dataframe = ohlcv_to_dataframe(new_data, timeframe, pair,
fill_missing=False, drop_incomplete=True)
if data.empty:
data = new_dataframe
else:
@@ -362,7 +362,7 @@ def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,
:param pair: pair used for log output.
:param min_date: start-date of the data
:param max_date: end-date of the data
:param timeframe_min: ticker Timeframe in minutes
:param timeframe_min: Timeframe in minutes
"""
# total difference in minutes / timeframe-minutes
expected_frames = int((max_date - min_date).total_seconds() // 60 // timeframe_min)

42
freqtrade/data/history/idatahandler.py
View File

@@ -55,7 +55,7 @@ class IDataHandler(ABC):
Implements the loading and conversion to a Pandas dataframe.
Timerange trimming and dataframe validation happens outside of this method.
:param pair: Pair to load data
:param timeframe: Ticker timeframe (e.g. "5m")
:param timeframe: Timeframe (e.g. "5m")
:param timerange: Limit data to be loaded to this timerange.
Optionally implemented by subclasses to avoid loading
all data where possible.
@@ -67,7 +67,7 @@ class IDataHandler(ABC):
"""
Remove data for this pair
:param pair: Delete data for this pair.
:param timeframe: Ticker timeframe (e.g. "5m")
:param timeframe: Timeframe (e.g. "5m")
:return: True when deleted, false if file did not exist.
"""
@@ -129,10 +129,10 @@ class IDataHandler(ABC):
warn_no_data: bool = True
) -> DataFrame:
"""
Load cached ticker history for the given pair.
Load cached candle (OHLCV) data for the given pair.
:param pair: Pair to load data for
:param timeframe: Ticker timeframe (e.g. "5m")
:param timeframe: Timeframe (e.g. "5m")
:param timerange: Limit data to be loaded to this timerange
:param fill_missing: Fill missing values with "No action"-candles
:param drop_incomplete: Drop last candle assuming it may be incomplete.
@@ -147,12 +147,7 @@ class IDataHandler(ABC):
pairdf = self._ohlcv_load(pair, timeframe,
timerange=timerange_startup)
if pairdf.empty:
if warn_no_data:
logger.warning(
f'No history data for pair: "{pair}", timeframe: {timeframe}. '
'Use `freqtrade download-data` to download the data'
)
if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
return pairdf
else:
enddate = pairdf.iloc[-1]['date']
@@ -160,13 +155,30 @@ class IDataHandler(ABC):
if timerange_startup:
self._validate_pairdata(pair, pairdf, timerange_startup)
pairdf = trim_dataframe(pairdf, timerange_startup)
if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
return pairdf
# incomplete candles should only be dropped if we didn't trim the end beforehand.
return clean_ohlcv_dataframe(pairdf, timeframe,
pair=pair,
fill_missing=fill_missing,
drop_incomplete=(drop_incomplete and
enddate == pairdf.iloc[-1]['date']))
pairdf = clean_ohlcv_dataframe(pairdf, timeframe,
pair=pair,
fill_missing=fill_missing,
drop_incomplete=(drop_incomplete and
enddate == pairdf.iloc[-1]['date']))
self._check_empty_df(pairdf, pair, timeframe, warn_no_data)
return pairdf
def _check_empty_df(self, pairdf: DataFrame, pair: str, timeframe: str, warn_no_data: bool):
"""
Warn on empty dataframe
"""
if pairdf.empty:
if warn_no_data:
logger.warning(
f'No history data for pair: "{pair}", timeframe: {timeframe}. '
'Use `freqtrade download-data` to download the data'
)
return True
return False
def _validate_pairdata(self, pair, pairdata: DataFrame, timerange: TimeRange):
"""

4
freqtrade/data/history/jsondatahandler.py
View File

@@ -60,7 +60,7 @@ class JsonDataHandler(IDataHandler):
Implements the loading and conversion to a Pandas dataframe.
Timerange trimming and dataframe validation happens outside of this method.
:param pair: Pair to load data
:param timeframe: Ticker timeframe (e.g. "5m")
:param timeframe: Timeframe (e.g. "5m")
:param timerange: Limit data to be loaded to this timerange.
Optionally implemented by subclasses to avoid loading
all data where possible.
@@ -83,7 +83,7 @@ class JsonDataHandler(IDataHandler):
"""
Remove data for this pair
:param pair: Delete data for this pair.
:param timeframe: Ticker timeframe (e.g. "5m")
:param timeframe: Timeframe (e.g. "5m")
:return: True when deleted, false if file did not exist.
"""
filename = self._pair_data_filename(self._datadir, pair, timeframe)

30
freqtrade/edge/edge_positioning.py
View File

@@ -8,10 +8,10 @@ import numpy as np
import utils_find_1st as utf1st
from pandas import DataFrame
from freqtrade import constants
from freqtrade.configuration import TimeRange
from freqtrade.data import history
from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
from freqtrade.exceptions import OperationalException
from freqtrade.data.history import get_timerange, load_data, refresh_data
from freqtrade.strategy.interface import SellType
logger = logging.getLogger(__name__)
@@ -54,7 +54,7 @@ class Edge:
if self.config['max_open_trades'] != float('inf'):
logger.critical('max_open_trades should be -1 in config !')
if self.config['stake_amount'] != constants.UNLIMITED_STAKE_AMOUNT:
if self.config['stake_amount'] != UNLIMITED_STAKE_AMOUNT:
raise OperationalException('Edge works only with unlimited stake amount')
# Deprecated capital_available_percentage. Will use tradable_balance_ratio in the future.
@@ -96,7 +96,7 @@ class Edge:
logger.info('Using local backtesting data (using whitelist in given config) ...')
if self._refresh_pairs:
history.refresh_data(
refresh_data(
datadir=self.config['datadir'],
pairs=pairs,
exchange=self.exchange,
@@ -104,7 +104,7 @@ class Edge:
timerange=self._timerange,
)
data = history.load_data(
data = load_data(
datadir=self.config['datadir'],
pairs=pairs,
timeframe=self.strategy.ticker_interval,
@@ -119,10 +119,10 @@ class Edge:
logger.critical("No data found. Edge is stopped ...")
return False
preprocessed = self.strategy.tickerdata_to_dataframe(data)
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
# Print timeframe
min_date, max_date = history.get_timerange(preprocessed)
min_date, max_date = get_timerange(preprocessed)
logger.info(
'Measuring data from %s up to %s (%s days) ...',
min_date.isoformat(),
@@ -137,10 +137,10 @@ class Edge:
pair_data = pair_data.sort_values(by=['date'])
pair_data = pair_data.reset_index(drop=True)
ticker_data = self.strategy.advise_sell(
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
trades += self._find_trades_for_stoploss_range(ticker_data, pair, self._stoploss_range)
trades += self._find_trades_for_stoploss_range(df_analyzed, pair, self._stoploss_range)
# If no trade found then exit
if len(trades) == 0:
@@ -317,7 +317,7 @@ class Edge:
}
# Group by (pair and stoploss) by applying above aggregator
df = results.groupby(['pair', 'stoploss'])['profit_abs', 'trade_duration'].agg(
df = results.groupby(['pair', 'stoploss'])[['profit_abs', 'trade_duration']].agg(
groupby_aggregator).reset_index(col_level=1)
# Dropping level 0 as we don't need it
@@ -359,11 +359,11 @@ class Edge:
# Returning a list of pairs in order of "expectancy"
return final
def _find_trades_for_stoploss_range(self, ticker_data, pair, stoploss_range):
buy_column = ticker_data['buy'].values
sell_column = ticker_data['sell'].values
date_column = ticker_data['date'].values
ohlc_columns = ticker_data[['open', 'high', 'low', 'close']].values
def _find_trades_for_stoploss_range(self, df, pair, stoploss_range):
buy_column = df['buy'].values
sell_column = df['sell'].values
date_column = df['date'].values
ohlc_columns = df[['open', 'high', 'low', 'close']].values
result: list = []
for stoploss in stoploss_range:

79
freqtrade/exchange/exchange.py
View File

@@ -18,7 +18,7 @@ from ccxt.base.decimal_to_precision import (ROUND_DOWN, ROUND_UP, TICK_SIZE,
TRUNCATE, decimal_to_precision)
from pandas import DataFrame
from freqtrade.data.converter import parse_ticker_dataframe
from freqtrade.data.converter import ohlcv_to_dataframe
from freqtrade.exceptions import (DependencyException, InvalidOrderException,
OperationalException, TemporaryError)
from freqtrade.exchange.common import BAD_EXCHANGES, retrier, retrier_async
@@ -351,7 +351,7 @@ class Exchange:
def validate_timeframes(self, timeframe: Optional[str]) -> None:
"""
Checks if ticker interval from config is a supported timeframe on the exchange
Check if timeframe from config is a supported timeframe on the exchange
"""
if not hasattr(self._api, "timeframes") or self._api.timeframes is None:
# If timeframes attribute is missing (or is None), the exchange probably
@@ -364,7 +364,7 @@ class Exchange:
if timeframe and (timeframe not in self.timeframes):
raise OperationalException(
f"Invalid ticker interval '{timeframe}'. This exchange supports: {self.timeframes}")
f"Invalid timeframe '{timeframe}'. This exchange supports: {self.timeframes}")
if timeframe and timeframe_to_minutes(timeframe) < 1:
raise OperationalException(
@@ -599,7 +599,7 @@ class Exchange:
return self._api.fetch_tickers()
except ccxt.NotSupported as e:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching tickers in batch.'
f'Exchange {self._api.name} does not support fetching tickers in batch. '
f'Message: {e}') from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
@@ -623,13 +623,13 @@ class Exchange:
def get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int) -> List:
"""
Gets candle history using asyncio and returns the list of candles.
Handles all async doing.
Async over one pair, assuming we get `_ohlcv_candle_limit` candles per call.
Get candle history using asyncio and returns the list of candles.
Handles all async work for this.
Async over one pair, assuming we get `self._ohlcv_candle_limit` candles per call.
:param pair: Pair to download
:param timeframe: Ticker Timeframe to get
:param timeframe: Timeframe to get data for
:param since_ms: Timestamp in milliseconds to get history from
:returns List of tickers
:returns List with candle (OHLCV) data
"""
return asyncio.get_event_loop().run_until_complete(
self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
@@ -649,26 +649,27 @@ class Exchange:
pair, timeframe, since) for since in
range(since_ms, arrow.utcnow().timestamp * 1000, one_call)]
tickers = await asyncio.gather(*input_coroutines, return_exceptions=True)
results = await asyncio.gather(*input_coroutines, return_exceptions=True)
# Combine tickers
# Combine gathered results
data: List = []
for p, timeframe, ticker in tickers:
for p, timeframe, res in results:
if p == pair:
data.extend(ticker)
data.extend(res)
# Sort data again after extending the result - above calls return in "async order"
data = sorted(data, key=lambda x: x[0])
logger.info("downloaded %s with length %s.", pair, len(data))
logger.info("Downloaded data for %s with length %s.", pair, len(data))
return data
def refresh_latest_ohlcv(self, pair_list: List[Tuple[str, str]]) -> List[Tuple[str, List]]:
"""
Refresh in-memory ohlcv asynchronously and set `_klines` with the result
Refresh in-memory OHLCV asynchronously and set `_klines` with the result
Loops asynchronously over pair_list and downloads all pairs async (semi-parallel).
Only used in the dataprovider.refresh() method.
:param pair_list: List of 2 element tuples containing pair, interval to refresh
:return: Returns a List of ticker-dataframes.
:return: TODO: return value is only used in the tests, get rid of it
"""
logger.debug("Refreshing ohlcv data for %d pairs", len(pair_list))
logger.debug("Refreshing candle (OHLCV) data for %d pairs", len(pair_list))
input_coroutines = []
@@ -679,15 +680,15 @@ class Exchange:
input_coroutines.append(self._async_get_candle_history(pair, timeframe))
else:
logger.debug(
"Using cached ohlcv data for pair %s, timeframe %s ...",
"Using cached candle (OHLCV) data for pair %s, timeframe %s ...",
pair, timeframe
)
tickers = asyncio.get_event_loop().run_until_complete(
results = asyncio.get_event_loop().run_until_complete(
asyncio.gather(*input_coroutines, return_exceptions=True))
# handle caching
for res in tickers:
for res in results:
if isinstance(res, Exception):
logger.warning("Async code raised an exception: %s", res.__class__.__name__)
continue
@@ -698,13 +699,14 @@ class Exchange:
if ticks:
self._pairs_last_refresh_time[(pair, timeframe)] = ticks[-1][0] // 1000
# keeping parsed dataframe in cache
self._klines[(pair, timeframe)] = parse_ticker_dataframe(
self._klines[(pair, timeframe)] = ohlcv_to_dataframe(
ticks, timeframe, pair=pair, fill_missing=True,
drop_incomplete=self._ohlcv_partial_candle)
return tickers
return results
def _now_is_time_to_refresh(self, pair: str, timeframe: str) -> bool:
# Calculating ticker interval in seconds
# Timeframe in seconds
interval_in_sec = timeframe_to_seconds(timeframe)
return not ((self._pairs_last_refresh_time.get((pair, timeframe), 0)
@@ -714,11 +716,11 @@ class Exchange:
async def _async_get_candle_history(self, pair: str, timeframe: str,
since_ms: Optional[int] = None) -> Tuple[str, str, List]:
"""
Asynchronously gets candle histories using fetch_ohlcv
Asynchronously get candle history data using fetch_ohlcv
returns tuple: (pair, timeframe, ohlcv_list)
"""
try:
# fetch ohlcv asynchronously
# Fetch OHLCV asynchronously
s = '(' + arrow.get(since_ms // 1000).isoformat() + ') ' if since_ms is not None else ''
logger.debug(
"Fetching pair %s, interval %s, since %s %s...",
@@ -728,9 +730,9 @@ class Exchange:
data = await self._api_async.fetch_ohlcv(pair, timeframe=timeframe,
since=since_ms)
# Because some exchange sort Tickers ASC and other DESC.
# Ex: Bittrex returns a list of tickers ASC (oldest first, newest last)
# when GDAX returns a list of tickers DESC (newest first, oldest last)
# Some exchanges sort OHLCV in ASC order and others in DESC.
# Ex: Bittrex returns the list of OHLCV in ASC order (oldest first, newest last)
# while GDAX returns the list of OHLCV in DESC order (newest first, oldest last)
# Only sort if necessary to save computing time
try:
if data and data[0][0] > data[-1][0]:
@@ -743,14 +745,15 @@ class Exchange:
except ccxt.NotSupported as e:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching historical candlestick data.'
f'Message: {e}') from e
f'Exchange {self._api.name} does not support fetching historical '
f'candle (OHLCV) data. Message: {e}') from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(f'Could not load ticker history for pair {pair} due to '
f'{e.__class__.__name__}. Message: {e}') from e
raise TemporaryError(f'Could not fetch historical candle (OHLCV) data '
f'for pair {pair} due to {e.__class__.__name__}. '
f'Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(f'Could not fetch ticker data for pair {pair}. '
f'Msg: {e}') from e
raise OperationalException(f'Could not fetch historical candle (OHLCV) data '
f'for pair {pair}. Message: {e}') from e
@retrier_async
async def _async_fetch_trades(self, pair: str,
@@ -883,14 +886,14 @@ class Exchange:
until: Optional[int] = None,
from_id: Optional[str] = None) -> Tuple[str, List]:
"""
Gets candle history using asyncio and returns the list of candles.
Handles all async doing.
Async over one pair, assuming we get `_ohlcv_candle_limit` candles per call.
Get trade history data using asyncio.
Handles all async work and returns the list of candles.
Async over one pair, assuming we get `self._ohlcv_candle_limit` candles per call.
:param pair: Pair to download
:param since: Timestamp in milliseconds to get history from
:param until: Timestamp in milliseconds. Defaults to current timestamp if not defined.
:param from_id: Download data starting with ID (if id is known)
:returns List of tickers
:returns List of trade data
"""
if not self.exchange_has("fetchTrades"):
raise OperationalException("This exchange does not suport downloading Trades.")

22
freqtrade/freqtradebot.py
View File

@@ -172,8 +172,8 @@ class FreqtradeBot:
_whitelist = self.edge.adjust(_whitelist)
if trades:
# Extend active-pair whitelist with pairs from open trades
# It ensures that tickers are downloaded for open trades
# Extend active-pair whitelist with pairs of open trades
# It ensures that candle (OHLCV) data are downloaded for open trades as well
_whitelist.extend([trade.pair for trade in trades if trade.pair not in _whitelist])
return _whitelist
@@ -394,16 +394,18 @@ class FreqtradeBot:
logger.info(f"Pair {pair} is currently locked.")
return False
# get_free_open_trades is checked before create_trade is called
# but it is still used here to prevent opening too many trades within one iteration
if not self.get_free_open_trades():
logger.debug(f"Can't open a new trade for {pair}: max number of trades is reached.")
return False
# running get_signal on historical data fetched
(buy, sell) = self.strategy.get_signal(
pair, self.strategy.ticker_interval,
self.dataprovider.ohlcv(pair, self.strategy.ticker_interval))
if buy and not sell:
if not self.get_free_open_trades():
logger.debug("Can't open a new trade: max number of trades is reached.")
return False
stake_amount = self.get_trade_stake_amount(pair)
if not stake_amount:
logger.debug(f"Stake amount is 0, ignoring possible trade for {pair}.")
@@ -628,7 +630,7 @@ class FreqtradeBot:
def get_sell_rate(self, pair: str, refresh: bool) -> float:
"""
Get sell rate - either using get-ticker bid or first bid based on orderbook
Get sell rate - either using ticker bid or first bid based on orderbook
The orderbook portion is only used for rpc messaging, which would otherwise fail
for BitMex (has no bid/ask in fetch_ticker)
or remain static in any other case since it's not updating.
@@ -891,6 +893,9 @@ class FreqtradeBot:
if order['status'] != 'canceled':
reason = "cancelled due to timeout"
corder = self.exchange.cancel_order(trade.open_order_id, trade.pair)
# Some exchanges don't return a dict here.
if not isinstance(corder, dict):
corder = {}
logger.info('Buy order %s for %s.', reason, trade)
else:
# Order was cancelled already, so we can reuse the existing dict
@@ -949,6 +954,7 @@ class FreqtradeBot:
trade.close_rate = None
trade.close_profit = None
trade.close_profit_abs = None
trade.close_date = None
trade.is_open = True
trade.open_order_id = None
@@ -1043,7 +1049,7 @@ class FreqtradeBot:
"""
profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit_trade = trade.calc_profit(rate=profit_rate)
# Use cached ticker here - it was updated seconds ago.
# Use cached rates here - it was updated seconds ago.
current_rate = self.get_sell_rate(trade.pair, False)
profit_ratio = trade.calc_profit_ratio(profit_rate)
gain = "profit" if profit_ratio > 0 else "loss"

12
freqtrade/misc.py
View File

@@ -81,13 +81,13 @@ def file_load_json(file):
gzipfile = file
# Try gzip file first, otherwise regular json file.
if gzipfile.is_file():
logger.debug('Loading ticker data from file %s', gzipfile)
with gzip.open(gzipfile) as tickerdata:
pairdata = json_load(tickerdata)
logger.debug(f"Loading historical data from file {gzipfile}")
with gzip.open(gzipfile) as datafile:
pairdata = json_load(datafile)
elif file.is_file():
logger.debug('Loading ticker data from file %s', file)
with open(file) as tickerdata:
pairdata = json_load(tickerdata)
logger.debug(f"Loading historical data from file {file}")
with open(file) as datafile:
pairdata = json_load(datafile)
else:
return None
return pairdata

121
freqtrade/optimize/backtesting.py
View File

@@ -6,8 +6,7 @@ This module contains the backtesting logic
import logging
from copy import deepcopy
from datetime import datetime, timedelta
from pathlib import Path
from typing import Any, Dict, List, NamedTuple, Optional
from typing import Any, Dict, List, NamedTuple, Optional, Tuple
import arrow
from pandas import DataFrame
@@ -19,10 +18,8 @@ from freqtrade.data.converter import trim_dataframe
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.misc import file_dump_json
from freqtrade.optimize.optimize_reports import (
generate_text_table, generate_text_table_sell_reason,
generate_text_table_strategy)
from freqtrade.optimize.optimize_reports import (show_backtest_results,
store_backtest_result)
from freqtrade.persistence import Trade
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.state import RunMode
@@ -88,8 +85,8 @@ class Backtesting:
validate_config_consistency(self.config)
if "ticker_interval" not in self.config:
raise OperationalException("Ticker-interval needs to be set in either configuration "
"or as cli argument `--ticker-interval 5m`")
raise OperationalException("Timeframe (ticker interval) needs to be set in either "
"configuration or as cli argument `--ticker-interval 5m`")
self.timeframe = str(self.config.get('ticker_interval'))
self.timeframe_min = timeframe_to_minutes(self.timeframe)
@@ -108,7 +105,7 @@ class Backtesting:
# And the regular "stoploss" function would not apply to that case
self.strategy.order_types['stoploss_on_exchange'] = False
def load_bt_data(self):
def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
timerange = TimeRange.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
@@ -134,49 +131,33 @@ class Backtesting:
return data, timerange
def _store_backtest_result(self, recordfilename: Path, results: DataFrame,
strategyname: Optional[str] = None) -> None:
records = [(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()]
if records:
if strategyname:
# Inject strategyname to filename
recordfilename = Path.joinpath(
recordfilename.parent,
f'{recordfilename.stem}-{strategyname}').with_suffix(recordfilename.suffix)
logger.info(f'Dumping backtest results to {recordfilename}')
file_dump_json(recordfilename, records)
def _get_ticker_list(self, processed: Dict) -> Dict[str, DataFrame]:
def _get_ohlcv_as_lists(self, processed: Dict) -> Dict[str, DataFrame]:
"""
Helper function to convert a processed tickerlist into a list for performance reasons.
Helper function to convert a processed dataframes into lists for performance reasons.
Used by backtest() - so keep this optimized for performance.
"""
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
ticker: Dict = {}
# Create ticker dict
data: Dict = {}
# Create dict with data
for pair, pair_data in processed.items():
pair_data.loc[:, 'buy'] = 0 # cleanup from previous run
pair_data.loc[:, 'sell'] = 0 # cleanup from previous run
ticker_data = self.strategy.advise_sell(
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
# to avoid using data from future, we buy/sell with signal from previous candle
ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
# To avoid using data from future, we use buy/sell signals shifted
# from the previous candle
df_analyzed.loc[:, 'buy'] = df_analyzed['buy'].shift(1)
df_analyzed.loc[:, 'sell'] = df_analyzed['sell'].shift(1)
ticker_data.drop(ticker_data.head(1).index, inplace=True)
df_analyzed.drop(df_analyzed.head(1).index, inplace=True)
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
ticker[pair] = [x for x in ticker_data.itertuples()]
return ticker
data[pair] = [x for x in df_analyzed.itertuples()]
return data
def _get_close_rate(self, sell_row, trade: Trade, sell: SellCheckTuple,
trade_dur: int) -> float:
@@ -220,7 +201,7 @@ class Backtesting:
def _get_sell_trade_entry(
self, pair: str, buy_row: DataFrame,
partial_ticker: List, trade_count_lock: Dict,
partial_ohlcv: List, trade_count_lock: Dict,
stake_amount: float, max_open_trades: int) -> Optional[BacktestResult]:
trade = Trade(
@@ -235,7 +216,7 @@ class Backtesting:
)
logger.debug(f"{pair} - Backtesting emulates creation of new trade: {trade}.")
# calculate win/lose forwards from buy point
for sell_row in partial_ticker:
for sell_row in partial_ohlcv:
if max_open_trades > 0:
# Increase trade_count_lock for every iteration
trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
@@ -259,9 +240,9 @@ class Backtesting:
close_rate=closerate,
sell_reason=sell.sell_type
)
if partial_ticker:
if partial_ohlcv:
# no sell condition found - trade stil open at end of backtest period
sell_row = partial_ticker[-1]
sell_row = partial_ohlcv[-1]
bt_res = BacktestResult(pair=pair,
profit_percent=trade.calc_profit_ratio(rate=sell_row.open),
profit_abs=trade.calc_profit(rate=sell_row.open),
@@ -308,8 +289,9 @@ class Backtesting:
trades = []
trade_count_lock: Dict = {}
# Dict of ticker-lists for performance (looping lists is a lot faster than dataframes)
ticker: Dict = self._get_ticker_list(processed)
# Use dict of lists with data for performance
# (looping lists is a lot faster than pandas DataFrames)
data: Dict = self._get_ohlcv_as_lists(processed)
lock_pair_until: Dict = {}
# Indexes per pair, so some pairs are allowed to have a missing start.
@@ -319,12 +301,12 @@ class Backtesting:
# Loop timerange and get candle for each pair at that point in time
while tmp < end_date:
for i, pair in enumerate(ticker):
for i, pair in enumerate(data):
if pair not in indexes:
indexes[pair] = 0
try:
row = ticker[pair][indexes[pair]]
row = data[pair][indexes[pair]]
except IndexError:
# missing Data for one pair at the end.
# Warnings for this are shown during data loading
@@ -352,7 +334,7 @@ class Backtesting:
# since indexes has been incremented before, we need to go one step back to
# also check the buying candle for sell conditions.
trade_entry = self._get_sell_trade_entry(pair, row, ticker[pair][indexes[pair]-1:],
trade_entry = self._get_sell_trade_entry(pair, row, data[pair][indexes[pair]-1:],
trade_count_lock, stake_amount,
max_open_trades)
@@ -395,7 +377,7 @@ class Backtesting:
self._set_strategy(strat)
# need to reprocess data every time to populate signals
preprocessed = self.strategy.tickerdata_to_dataframe(data)
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
@@ -416,44 +398,7 @@ class Backtesting:
position_stacking=position_stacking,
)
for strategy, results in all_results.items():
if self.config.get('export', False):
self._store_backtest_result(Path(self.config['exportfilename']), results,
strategy if len(self.strategylist) > 1 else None)
print(f"Result for strategy {strategy}")
table = generate_text_table(data, stake_currency=self.config['stake_currency'],
max_open_trades=self.config['max_open_trades'],
results=results)
if isinstance(table, str):
print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
table = generate_text_table_sell_reason(data,
stake_currency=self.config['stake_currency'],
max_open_trades=self.config['max_open_trades'],
results=results)
if isinstance(table, str):
print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
print(table)
table = generate_text_table(data,
stake_currency=self.config['stake_currency'],
max_open_trades=self.config['max_open_trades'],
results=results.loc[results.open_at_end], skip_nan=True)
if isinstance(table, str):
print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
if isinstance(table, str):
print('=' * len(table.splitlines()[0]))
print()
if len(all_results) > 1:
# Print Strategy summary table
table = generate_text_table_strategy(self.config['stake_currency'],
self.config['max_open_trades'],
all_results=all_results)
print(' STRATEGY SUMMARY '.center(len(table.splitlines()[0]), '='))
print(table)
print('=' * len(table.splitlines()[0]))
print('\nFor more details, please look at the detail tables above')
if self.config.get('export', False):
store_backtest_result(self.config['exportfilename'], all_results)
# Show backtest results
show_backtest_results(self.config, data, all_results)

10
freqtrade/optimize/hyperopt.py
View File

@@ -79,8 +79,8 @@ class Hyperopt:
self.trials_file = (self.config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_results.pickle')
self.tickerdata_pickle = (self.config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_tickerdata.pkl')
self.data_pickle_file = (self.config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_tickerdata.pkl')
self.total_epochs = config.get('epochs', 0)
self.current_best_loss = 100
@@ -134,7 +134,7 @@ class Hyperopt:
"""
Remove hyperopt pickle files to restart hyperopt.
"""
for f in [self.tickerdata_pickle, self.trials_file]:
for f in [self.data_pickle_file, self.trials_file]:
p = Path(f)
if p.is_file():
logger.info(f"Removing `{p}`.")
@@ -533,7 +533,7 @@ class Hyperopt:
self.backtesting.strategy.trailing_only_offset_is_reached = \
d['trailing_only_offset_is_reached']
processed = load(self.tickerdata_pickle)
processed = load(self.data_pickle_file)
min_date, max_date = get_timerange(processed)
@@ -660,7 +660,7 @@ class Hyperopt:
'Hyperopting with data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
)
dump(preprocessed, self.tickerdata_pickle)
dump(preprocessed, self.data_pickle_file)
# We don't need exchange instance anymore while running hyperopt
self.backtesting.exchange = None # type: ignore

76
freqtrade/optimize/optimize_reports.py
View File

@@ -1,9 +1,37 @@
import logging
from datetime import timedelta
from pathlib import Path
from typing import Dict
from pandas import DataFrame
from tabulate import tabulate
from freqtrade.misc import file_dump_json
logger = logging.getLogger(__name__)
def store_backtest_result(recordfilename: Path, all_results: Dict[str, DataFrame]) -> None:
"""
Stores backtest results to file (one file per strategy)
:param recordfilename: Destination filename
:param all_results: Dict of Dataframes, one results dataframe per strategy
"""
for strategy, results in all_results.items():
records = [(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()]
if records:
if len(all_results) > 1:
# Inject strategy to filename
recordfilename = Path.joinpath(
recordfilename.parent,
f'{recordfilename.stem}-{strategy}').with_suffix(recordfilename.suffix)
logger.info(f'Dumping backtest results to {recordfilename}')
file_dump_json(recordfilename, records)
def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_trades: int,
results: DataFrame, skip_nan: bool = False) -> str:
@@ -69,12 +97,12 @@ def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_tra
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
def generate_text_table_sell_reason(
data: Dict[str, Dict], stake_currency: str, max_open_trades: int, results: DataFrame
) -> str:
def generate_text_table_sell_reason(stake_currency: str, max_open_trades: int,
results: DataFrame) -> str:
"""
Generate small table outlining Backtest results
:param data: Dict of <pair: dataframe> containing data that was used during backtesting.
:param stake_currency: Stakecurrency used
:param max_open_trades: Max_open_trades parameter
:param results: Dataframe containing the backtest results
:return: pretty printed table with tabulate as string
"""
@@ -173,3 +201,43 @@ def generate_edge_table(results: dict) -> str:
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(tabular_data, headers=headers,
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
def show_backtest_results(config: Dict, btdata: Dict[str, DataFrame],
all_results: Dict[str, DataFrame]):
for strategy, results in all_results.items():
print(f"Result for strategy {strategy}")
table = generate_text_table(btdata, stake_currency=config['stake_currency'],
max_open_trades=config['max_open_trades'],
results=results)
if isinstance(table, str):
print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
table = generate_text_table_sell_reason(stake_currency=config['stake_currency'],
max_open_trades=config['max_open_trades'],
results=results)
if isinstance(table, str):
print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
print(table)
table = generate_text_table(btdata,
stake_currency=config['stake_currency'],
max_open_trades=config['max_open_trades'],
results=results.loc[results.open_at_end], skip_nan=True)
if isinstance(table, str):
print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
if isinstance(table, str):
print('=' * len(table.splitlines()[0]))
print()
if len(all_results) > 1:
# Print Strategy summary table
table = generate_text_table_strategy(config['stake_currency'],
config['max_open_trades'],
all_results=all_results)
print(' STRATEGY SUMMARY '.center(len(table.splitlines()[0]), '='))
print(table)
print('=' * len(table.splitlines()[0]))
print('\nFor more details, please look at the detail tables above')

11
freqtrade/persistence.py
View File

@@ -86,7 +86,7 @@ def check_migrate(engine) -> None:
logger.debug(f'trying {table_back_name}')
# Check for latest column
if not has_column(cols, 'open_trade_price'):
if not has_column(cols, 'close_profit_abs'):
logger.info(f'Running database migration - backup available as {table_back_name}')
fee_open = get_column_def(cols, 'fee_open', 'fee')
@@ -106,6 +106,9 @@ def check_migrate(engine) -> None:
ticker_interval = get_column_def(cols, 'ticker_interval', 'null')
open_trade_price = get_column_def(cols, 'open_trade_price',
f'amount * open_rate * (1 + {fee_open})')
close_profit_abs = get_column_def(
cols, 'close_profit_abs',
f"(amount * close_rate * (1 - {fee_close})) - {open_trade_price}")
# Schema migration necessary
engine.execute(f"alter table trades rename to {table_back_name}")
@@ -123,7 +126,7 @@ def check_migrate(engine) -> None:
stop_loss, stop_loss_pct, initial_stop_loss, initial_stop_loss_pct,
stoploss_order_id, stoploss_last_update,
max_rate, min_rate, sell_reason, strategy,
ticker_interval, open_trade_price
ticker_interval, open_trade_price, close_profit_abs
)
select id, lower(exchange),
case
@@ -143,7 +146,7 @@ def check_migrate(engine) -> None:
{stoploss_order_id} stoploss_order_id, {stoploss_last_update} stoploss_last_update,
{max_rate} max_rate, {min_rate} min_rate, {sell_reason} sell_reason,
{strategy} strategy, {ticker_interval} ticker_interval,
{open_trade_price} open_trade_price
{open_trade_price} open_trade_price, {close_profit_abs} close_profit_abs
from {table_back_name}
""")
@@ -190,6 +193,7 @@ class Trade(_DECL_BASE):
close_rate = Column(Float)
close_rate_requested = Column(Float)
close_profit = Column(Float)
close_profit_abs = Column(Float)
stake_amount = Column(Float, nullable=False)
amount = Column(Float)
open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
@@ -334,6 +338,7 @@ class Trade(_DECL_BASE):
"""
self.close_rate = Decimal(rate)
self.close_profit = self.calc_profit_ratio()
self.close_profit_abs = self.calc_profit()
self.close_date = datetime.utcnow()
self.is_open = False
self.open_order_id = None

61
freqtrade/plot/plotting.py
View File

@@ -6,10 +6,11 @@ import pandas as pd
from freqtrade.configuration import TimeRange
from freqtrade.data.btanalysis import (calculate_max_drawdown,
combine_tickers_with_mean,
combine_dataframes_with_mean,
create_cum_profit,
extract_trades_of_period, load_trades)
from freqtrade.data.converter import trim_dataframe
from freqtrade.exchange import timeframe_to_prev_date
from freqtrade.data.history import load_data
from freqtrade.misc import pair_to_filename
from freqtrade.resolvers import StrategyResolver
@@ -29,7 +30,7 @@ except ImportError:
def init_plotscript(config):
"""
Initialize objects needed for plotting
:return: Dict with tickers, trades and pairs
:return: Dict with candle (OHLCV) data, trades and pairs
"""
if "pairs" in config:
@@ -40,7 +41,7 @@ def init_plotscript(config):
# Set timerange to use
timerange = TimeRange.parse_timerange(config.get("timerange"))
tickers = load_data(
data = load_data(
datadir=config.get("datadir"),
pairs=pairs,
timeframe=config.get('ticker_interval', '5m'),
@@ -48,12 +49,22 @@ def init_plotscript(config):
data_format=config.get('dataformat_ohlcv', 'json'),
)
trades = load_trades(config['trade_source'],
db_url=config.get('db_url'),
exportfilename=config.get('exportfilename'),
)
no_trades = False
if config.get('no_trades', False):
no_trades = True
elif not config['exportfilename'].is_file() and config['trade_source'] == 'file':
logger.warning("Backtest file is missing skipping trades.")
no_trades = True
trades = load_trades(
config['trade_source'],
db_url=config.get('db_url'),
exportfilename=config.get('exportfilename'),
no_trades=no_trades
)
trades = trim_dataframe(trades, timerange, 'open_time')
return {"tickers": tickers,
return {"ohlcv": data,
"trades": trades,
"pairs": pairs,
}
@@ -112,7 +123,8 @@ def add_profit(fig, row, data: pd.DataFrame, column: str, name: str) -> make_sub
return fig
def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame) -> make_subplots:
def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
timeframe: str) -> make_subplots:
"""
Add scatter points indicating max drawdown
"""
@@ -122,12 +134,12 @@ def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame) -> m
drawdown = go.Scatter(
x=[highdate, lowdate],
y=[
df_comb.loc[highdate, 'cum_profit'],
df_comb.loc[lowdate, 'cum_profit'],
df_comb.loc[timeframe_to_prev_date(timeframe, highdate), 'cum_profit'],
df_comb.loc[timeframe_to_prev_date(timeframe, lowdate), 'cum_profit'],
],
mode='markers',
name=f"Max drawdown {max_drawdown:.2f}%",
text=f"Max drawdown {max_drawdown:.2f}%",
name=f"Max drawdown {max_drawdown * 100:.2f}%",
text=f"Max drawdown {max_drawdown * 100:.2f}%",
marker=dict(
symbol='square-open',
size=9,
@@ -368,10 +380,13 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
return fig
def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
trades: pd.DataFrame, timeframe: str) -> go.Figure:
# Combine close-values for all pairs, rename columns to "pair"
df_comb = combine_tickers_with_mean(tickers, "close")
df_comb = combine_dataframes_with_mean(data, "close")
# Trim trades to available OHLCV data
trades = extract_trades_of_period(df_comb, trades, date_index=True)
# Add combined cumulative profit
df_comb = create_cum_profit(df_comb, trades, 'cum_profit', timeframe)
@@ -395,7 +410,7 @@ def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
fig.add_trace(avgclose, 1, 1)
fig = add_profit(fig, 2, df_comb, 'cum_profit', 'Profit')
fig = add_max_drawdown(fig, 2, trades, df_comb)
fig = add_max_drawdown(fig, 2, trades, df_comb, timeframe)
for pair in pairs:
profit_col = f'cum_profit_{pair}'
@@ -439,7 +454,7 @@ def load_and_plot_trades(config: Dict[str, Any]):
"""
From configuration provided
- Initializes plot-script
- Get tickers data
- Get candle (OHLCV) data
- Generate Dafaframes populated with indicators and signals based on configured strategy
- Load trades excecuted during the selected period
- Generate Plotly plot objects
@@ -451,19 +466,17 @@ def load_and_plot_trades(config: Dict[str, Any]):
plot_elements = init_plotscript(config)
trades = plot_elements['trades']
pair_counter = 0
for pair, data in plot_elements["tickers"].items():
for pair, data in plot_elements["ohlcv"].items():
pair_counter += 1
logger.info("analyse pair %s", pair)
tickers = {}
tickers[pair] = data
dataframe = strategy.analyze_ticker(tickers[pair], {'pair': pair})
df_analyzed = strategy.analyze_ticker(data, {'pair': pair})
trades_pair = trades.loc[trades['pair'] == pair]
trades_pair = extract_trades_of_period(dataframe, trades_pair)
trades_pair = extract_trades_of_period(df_analyzed, trades_pair)
fig = generate_candlestick_graph(
pair=pair,
data=dataframe,
data=df_analyzed,
trades=trades_pair,
indicators1=config.get("indicators1", []),
indicators2=config.get("indicators2", []),
@@ -494,7 +507,7 @@ def plot_profit(config: Dict[str, Any]) -> None:
# Create an average close price of all the pairs that were involved.
# this could be useful to gauge the overall market trend
fig = generate_profit_graph(plot_elements["pairs"], plot_elements["tickers"],
fig = generate_profit_graph(plot_elements["pairs"], plot_elements["ohlcv"],
trades, config.get('ticker_interval', '5m'))
store_plot_file(fig, filename='freqtrade-profit-plot.html',
directory=config['user_data_dir'] / "plot", auto_open=True)

6
freqtrade/rpc/rpc.py
View File

@@ -197,7 +197,7 @@ class RPC:
Trade.close_date >= profitday,
Trade.close_date < (profitday + timedelta(days=1))
]).order_by(Trade.close_date).all()
curdayprofit = sum(trade.calc_profit() for trade in trades)
curdayprofit = sum(trade.close_profit_abs for trade in trades)
profit_days[profitday] = {
'amount': f'{curdayprofit:.8f}',
'trades': len(trades)
@@ -246,8 +246,8 @@ class RPC:
durations.append((trade.close_date - trade.open_date).total_seconds())
if not trade.is_open:
profit_ratio = trade.calc_profit_ratio()
profit_closed_coin.append(trade.calc_profit())
profit_ratio = trade.close_profit
profit_closed_coin.append(trade.close_profit_abs)
profit_closed_ratio.append(profit_ratio)
else:
# Get current rate

67
freqtrade/strategy/interface.py
View File

@@ -16,6 +16,7 @@ from freqtrade.data.dataprovider import DataProvider
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.persistence import Trade
from freqtrade.wallets import Wallets
from freqtrade.exceptions import DependencyException
logger = logging.getLogger(__name__)
@@ -59,7 +60,7 @@ class IStrategy(ABC):
Attributes you can use:
minimal_roi -> Dict: Minimal ROI designed for the strategy
stoploss -> float: optimal stoploss designed for the strategy
ticker_interval -> str: value of the ticker interval to use for the strategy
ticker_interval -> str: value of the timeframe (ticker interval) to use with the strategy
"""
# Strategy interface version
# Default to version 2
@@ -125,7 +126,7 @@ class IStrategy(ABC):
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Populate indicators that will be used in the Buy and Sell strategy
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param dataframe: DataFrame with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
@@ -200,11 +201,11 @@ class IStrategy(ABC):
def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Parses the given ticker history and returns a populated DataFrame
Parses the given candle (OHLCV) data and returns a populated DataFrame
add several TA indicators and buy signal to it
:param dataframe: Dataframe containing ticker data
:param dataframe: Dataframe containing data from exchange
:param metadata: Metadata dictionary with additional data (e.g. 'pair')
:return: DataFrame with ticker data and indicator data
:return: DataFrame of candle (OHLCV) data with indicator data and signals added
"""
logger.debug("TA Analysis Launched")
dataframe = self.advise_indicators(dataframe, metadata)
@@ -214,12 +215,12 @@ class IStrategy(ABC):
def _analyze_ticker_internal(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Parses the given ticker history and returns a populated DataFrame
Parses the given candle (OHLCV) data and returns a populated DataFrame
add several TA indicators and buy signal to it
WARNING: Used internally only, may skip analysis if `process_only_new_candles` is set.
:param dataframe: Dataframe containing ticker data
:param dataframe: Dataframe containing data from exchange
:param metadata: Metadata dictionary with additional data (e.g. 'pair')
:return: DataFrame with ticker data and indicator data
:return: DataFrame of candle (OHLCV) data with indicator data and signals added
"""
pair = str(metadata.get('pair'))
@@ -241,8 +242,26 @@ class IStrategy(ABC):
return dataframe
def get_signal(self, pair: str, interval: str,
dataframe: DataFrame) -> Tuple[bool, bool]:
@staticmethod
def preserve_df(dataframe: DataFrame) -> Tuple[int, float, datetime]:
""" keep some data for dataframes """
return len(dataframe), dataframe["close"].iloc[-1], dataframe["date"].iloc[-1]
@staticmethod
def assert_df(dataframe: DataFrame, df_len: int, df_close: float, df_date: datetime):
""" make sure data is unmodified """
message = ""
if df_len != len(dataframe):
message = "length"
elif df_close != dataframe["close"].iloc[-1]:
message = "last close price"
elif df_date != dataframe["date"].iloc[-1]:
message = "last date"
if message:
raise DependencyException("Dataframe returned from strategy has mismatching "
f"{message}.")
def get_signal(self, pair: str, interval: str, dataframe: DataFrame) -> Tuple[bool, bool]:
"""
Calculates current signal based several technical analysis indicators
:param pair: pair in format ANT/BTC
@@ -251,21 +270,25 @@ class IStrategy(ABC):
:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
"""
if not isinstance(dataframe, DataFrame) or dataframe.empty:
logger.warning('Empty ticker history for pair %s', pair)
logger.warning('Empty candle (OHLCV) data for pair %s', pair)
return False, False
latest_date = dataframe['date'].max()
try:
df_len, df_close, df_date = self.preserve_df(dataframe)
dataframe = self._analyze_ticker_internal(dataframe, {'pair': pair})
self.assert_df(dataframe, df_len, df_close, df_date)
except ValueError as error:
logger.warning(
'Unable to analyze ticker for pair %s: %s',
pair,
str(error)
)
logger.warning('Unable to analyze candle (OHLCV) data for pair %s: %s',
pair, str(error))
return False, False
except DependencyException as error:
logger.warning("Unable to analyze candle (OHLCV) data for pair %s: %s",
pair, str(error))
return False, False
except Exception as error:
logger.exception(
'Unexpected error when analyzing ticker for pair %s: %s',
'Unexpected error when analyzing candle (OHLCV) data for pair %s: %s',
pair,
str(error)
)
@@ -275,7 +298,7 @@ class IStrategy(ABC):
logger.warning('Empty dataframe for pair %s', pair)
return False, False
latest = dataframe.iloc[-1]
latest = dataframe.loc[dataframe['date'] == latest_date].iloc[-1]
# Check if dataframe is out of date
signal_date = arrow.get(latest['date'])
@@ -440,19 +463,19 @@ class IStrategy(ABC):
else:
return current_profit > roi
def tickerdata_to_dataframe(self, tickerdata: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
def ohlcvdata_to_dataframe(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
"""
Creates a dataframe and populates indicators for given ticker data
Creates a dataframe and populates indicators for given candle (OHLCV) data
Used by optimize operations only, not during dry / live runs.
"""
return {pair: self.advise_indicators(pair_data, {'pair': pair})
for pair, pair_data in tickerdata.items()}
for pair, pair_data in data.items()}
def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Populate indicators that will be used in the Buy and Sell strategy
This method should not be overridden.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""

6
freqtrade/templates/base_hyperopt.py.j2
View File

@@ -66,6 +66,9 @@ class {{ hyperopt }}(IHyperOpt):
dataframe['close'], dataframe['sar']
))
# Check that the candle had volume
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
@@ -111,6 +114,9 @@ class {{ hyperopt }}(IHyperOpt):
dataframe['sar'], dataframe['close']
))
# Check that the candle had volume
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),

2
freqtrade/templates/base_strategy.py.j2
View File

@@ -99,7 +99,7 @@ class {{ strategy }}(IStrategy):
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""

6
freqtrade/templates/sample_hyperopt.py
View File

@@ -78,6 +78,9 @@ class SampleHyperOpt(IHyperOpt):
dataframe['close'], dataframe['sar']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
@@ -138,6 +141,9 @@ class SampleHyperOpt(IHyperOpt):
dataframe['sar'], dataframe['close']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),

6
freqtrade/templates/sample_hyperopt_advanced.py
View File

@@ -93,6 +93,9 @@ class AdvancedSampleHyperOpt(IHyperOpt):
dataframe['close'], dataframe['sar']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
@@ -153,6 +156,9 @@ class AdvancedSampleHyperOpt(IHyperOpt):
dataframe['sar'], dataframe['close']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),

2
freqtrade/templates/sample_strategy.py
View File

@@ -116,7 +116,7 @@ class SampleStrategy(IStrategy):
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""