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Merge branch 'feat/short' into pr/samgermain/5378

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Matthias
2021-08-24 06:28:16 +02:00
parent faf5cfa66d 957551ea97
commit 7a977a8eaf
57 changed files with 1076 additions and 811 deletions
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40
freqtrade/optimize/backtesting.py
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@@ -133,6 +133,9 @@ class Backtesting:
self.abort = False
def __del__(self):
self.cleanup()
def cleanup(self):
LoggingMixin.show_output = True
PairLocks.use_db = True
Trade.use_db = True
@@ -219,7 +222,7 @@ class Backtesting:
# Every change to this headers list must evaluate further usages of the resulting tuple
# and eventually change the constants for indexes at the top
headers = ['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
'enter_short', 'exit_short']
'enter_short', 'exit_short', 'long_tag', 'short_tag']
data: Dict = {}
self.progress.init_step(BacktestState.CONVERT, len(processed))
@@ -227,21 +230,15 @@ class Backtesting:
for pair, pair_data in processed.items():
self.check_abort()
self.progress.increment()
has_buy_tag = 'long_tag' in pair_data
has_short_tag = 'short_tag' in pair_data
headers = headers + ['long_tag'] if has_buy_tag else headers
headers = headers + ['short_tag'] if has_short_tag else headers
if not pair_data.empty:
# Cleanup from prior runs
pair_data.loc[:, 'buy'] = 0 # TODO: Should be renamed to enter_long
pair_data.loc[:, 'enter_short'] = 0
pair_data.loc[:, 'sell'] = 0 # TODO: should be renamed to exit_long
pair_data.loc[:, 'exit_short'] = 0
# pair_data.loc[:, 'sell'] = 0
if has_buy_tag:
pair_data.loc[:, 'long_tag'] = None # cleanup if buy_tag is exist
if has_short_tag:
pair_data.loc[:, 'short_tag'] = None # cleanup if short_tag is exist
pair_data.loc[:, 'long_tag'] = None # cleanup if buy_tag is exist
pair_data.loc[:, 'short_tag'] = None # cleanup if short_tag is exist
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}),
@@ -256,14 +253,15 @@ class Backtesting:
df_analyzed.loc[:, 'enter_short'] = df_analyzed.loc[:, 'enter_short'].shift(1)
df_analyzed.loc[:, 'exit_long'] = df_analyzed.loc[:, 'exit_long'].shift(1)
df_analyzed.loc[:, 'exit_short'] = df_analyzed.loc[:, 'exit_short'].shift(1)
if has_buy_tag:
df_analyzed.loc[:, 'long_tag'] = df_analyzed.loc[:, 'long_tag'].shift(1)
df_analyzed.loc[:, 'long_tag'] = df_analyzed.loc[:, 'long_tag'].shift(1)
df_analyzed.drop(df_analyzed.head(1).index, inplace=True)
# Update dataprovider cache
self.dataprovider._set_cached_df(pair, self.timeframe, df_analyzed)
df_analyzed = df_analyzed.drop(df_analyzed.head(1).index)
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
data[pair] = df_analyzed[headers].values.tolist()
@@ -337,13 +335,14 @@ class Backtesting:
def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
# TODO: short exits
sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX], # type: ignore
sell_row[DATE_IDX].to_pydatetime(), sell_row[BUY_IDX],
sell_candle_time, sell_row[BUY_IDX],
sell_row[SELL_IDX],
low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX])
if sell.sell_flag:
trade.close_date = sell_row[DATE_IDX].to_pydatetime()
trade.close_date = sell_candle_time
trade.sell_reason = sell.sell_reason
trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
@@ -355,7 +354,7 @@ class Backtesting:
rate=closerate,
time_in_force=time_in_force,
sell_reason=sell.sell_reason,
current_time=sell_row[DATE_IDX].to_pydatetime()):
current_time=sell_candle_time):
return None
trade.close(closerate, show_msg=False)
@@ -494,6 +493,8 @@ class Backtesting:
for i, pair in enumerate(data):
row_index = indexes[pair]
try:
# Row is treated as "current incomplete candle".
# Buy / sell signals are shifted by 1 to compensate for this.
row = data[pair][row_index]
except IndexError:
# missing Data for one pair at the end.
@@ -505,8 +506,8 @@ class Backtesting:
continue
row_index += 1
self.dataprovider._set_dataframe_max_index(row_index)
indexes[pair] = row_index
self.dataprovider._set_dataframe_max_index(row_index)
# without positionstacking, we can only have one open trade per pair.
# max_open_trades must be respected
@@ -530,7 +531,7 @@ class Backtesting:
open_trades[pair].append(trade)
LocalTrade.add_bt_trade(trade)
for trade in open_trades[pair]:
for trade in list(open_trades[pair]):
# also check the buying candle for sell conditions.
trade_entry = self._get_sell_trade_entry(trade, row)
# Sell occurred
@@ -561,7 +562,8 @@ class Backtesting:
'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
}
def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, Any], timerange: TimeRange):
def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, DataFrame],
timerange: TimeRange):
self.progress.init_step(BacktestState.ANALYZE, 0)
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
@@ -580,7 +582,7 @@ class Backtesting:
max_open_trades = 0
# need to reprocess data every time to populate signals
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
preprocessed = self.strategy.advise_all_indicators(data)
# Trim startup period from analyzed dataframe
preprocessed_tmp = trim_dataframes(preprocessed, timerange, self.required_startup)