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Added sell_tag and buy/sell telegram performance functions

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This commit is contained in:
theluxaz
2021-10-13 00:02:28 +03:00
parent 0a52d7c24f
commit b898f86364
11 changed files with 1673 additions and 18 deletions
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13
freqtrade/optimize/backtesting.py
View File

@@ -44,7 +44,7 @@ SELL_IDX = 4
LOW_IDX = 5
HIGH_IDX = 6
BUY_TAG_IDX = 7
SELL_TAG_IDX = 8
class Backtesting:
"""
@@ -218,7 +218,7 @@ 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', 'buy', 'open', 'close', 'sell', 'low', 'high', 'buy_tag']
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high', 'buy_tag', 'sell_tag']
data: Dict = {}
self.progress.init_step(BacktestState.CONVERT, len(processed))
@@ -230,6 +230,7 @@ class Backtesting:
pair_data.loc[:, 'buy'] = 0 # cleanup if buy_signal is exist
pair_data.loc[:, 'sell'] = 0 # cleanup if sell_signal is exist
pair_data.loc[:, 'buy_tag'] = None # cleanup if buy_tag is exist
pair_data.loc[:, 'sell_tag'] = None # cleanup if sell_tag is exist
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair}).copy()
@@ -241,6 +242,7 @@ class Backtesting:
df_analyzed.loc[:, 'buy'] = df_analyzed.loc[:, 'buy'].shift(1)
df_analyzed.loc[:, 'sell'] = df_analyzed.loc[:, 'sell'].shift(1)
df_analyzed.loc[:, 'buy_tag'] = df_analyzed.loc[:, 'buy_tag'].shift(1)
df_analyzed.loc[:, 'sell_tag'] = df_analyzed.loc[:, 'sell_tag'].shift(1)
# Update dataprovider cache
self.dataprovider._set_cached_df(pair, self.timeframe, df_analyzed)
@@ -319,6 +321,9 @@ class Backtesting:
return sell_row[OPEN_IDX]
def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX], # type: ignore
sell_candle_time, sell_row[BUY_IDX],
@@ -327,6 +332,8 @@ class Backtesting:
if sell.sell_flag:
trade.close_date = sell_candle_time
if(sell_row[SELL_TAG_IDX] is not None):
trade.sell_tag = sell_row[SELL_TAG_IDX]
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)
@@ -375,6 +382,7 @@ class Backtesting:
if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
# Enter trade
has_buy_tag = len(row) >= BUY_TAG_IDX + 1
has_sell_tag = len(row) >= SELL_TAG_IDX + 1
trade = LocalTrade(
pair=pair,
open_rate=row[OPEN_IDX],
@@ -385,6 +393,7 @@ class Backtesting:
fee_close=self.fee,
is_open=True,
buy_tag=row[BUY_TAG_IDX] if has_buy_tag else None,
sell_tag=row[SELL_TAG_IDX] if has_sell_tag else None,
exchange='backtesting',
)
return trade

138
freqtrade/optimize/optimize_reports.py
View File

@@ -82,7 +82,7 @@ def _generate_result_line(result: DataFrame, starting_balance: int, first_column
'profit_sum_pct': round(profit_sum * 100.0, 2),
'profit_total_abs': result['profit_abs'].sum(),
'profit_total': profit_total,
'profit_total_pct': round(profit_total * 100.0, 2),
'profit_total_pct': round(profit_sum * 100.0, 2),
'duration_avg': str(timedelta(
minutes=round(result['trade_duration'].mean()))
) if not result.empty else '0:00',
@@ -126,6 +126,92 @@ def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, starting_b
tabular_data.append(_generate_result_line(results, starting_balance, 'TOTAL'))
return tabular_data
def generate_tag_metrics(tag_type:str, data: Dict[str, Dict], stake_currency: str, starting_balance: int,
results: DataFrame, skip_nan: bool = False) -> List[Dict]:
"""
Generates and returns a list of metrics for the given tag trades and the results dataframe
:param data: Dict of <pair: dataframe> containing data that was used during backtesting.
:param stake_currency: stake-currency - used to correctly name headers
:param starting_balance: Starting balance
:param results: Dataframe containing the backtest results
:param skip_nan: Print "left open" open trades
:return: List of Dicts containing the metrics per pair
"""
tabular_data = []
# for tag, count in results[tag_type].value_counts().iteritems():
# result = results.loc[results[tag_type] == tag]
#
# profit_mean = result['profit_ratio'].mean()
# profit_sum = result['profit_ratio'].sum()
# profit_total = profit_sum / max_open_trades
#
# tabular_data.append(
# {
# 'sell_reason': tag,
# 'trades': count,
# 'wins': len(result[result['profit_abs'] > 0]),
# 'draws': len(result[result['profit_abs'] == 0]),
# 'losses': len(result[result['profit_abs'] < 0]),
# 'profit_mean': profit_mean,
# 'profit_mean_pct': round(profit_mean * 100, 2),
# 'profit_sum': profit_sum,
# 'profit_sum_pct': round(profit_sum * 100, 2),
# 'profit_total_abs': result['profit_abs'].sum(),
# 'profit_total': profit_total,
# 'profit_total_pct': round(profit_total * 100, 2),
# }
# )
#
# tabular_data = []
for tag, count in results[tag_type].value_counts().iteritems():
result = results[results[tag_type] == tag]
if skip_nan and result['profit_abs'].isnull().all():
continue
tabular_data.append(_generate_tag_result_line(result, starting_balance, tag))
# Sort by total profit %:
tabular_data = sorted(tabular_data, key=lambda k: k['profit_total_abs'], reverse=True)
# Append Total
tabular_data.append(_generate_result_line(results, starting_balance, 'TOTAL'))
return tabular_data
def _generate_tag_result_line(result: DataFrame, starting_balance: int, first_column: str) -> Dict:
"""
Generate one result dict, with "first_column" as key.
"""
profit_sum = result['profit_ratio'].sum()
# (end-capital - starting capital) / starting capital
profit_total = result['profit_abs'].sum() / starting_balance
return {
'key': first_column,
'trades': len(result),
'profit_mean': result['profit_ratio'].mean() if len(result) > 0 else 0.0,
'profit_mean_pct': result['profit_ratio'].mean() * 100.0 if len(result) > 0 else 0.0,
'profit_sum': profit_sum,
'profit_sum_pct': round(profit_sum * 100.0, 2),
'profit_total_abs': result['profit_abs'].sum(),
'profit_total': profit_total,
'profit_total_pct': round(profit_total * 100.0, 2),
'duration_avg': str(timedelta(
minutes=round(result['trade_duration'].mean()))
) if not result.empty else '0:00',
# 'duration_max': str(timedelta(
# minutes=round(result['trade_duration'].max()))
# ) if not result.empty else '0:00',
# 'duration_min': str(timedelta(
# minutes=round(result['trade_duration'].min()))
# ) if not result.empty else '0:00',
'wins': len(result[result['profit_abs'] > 0]),
'draws': len(result[result['profit_abs'] == 0]),
'losses': len(result[result['profit_abs'] < 0]),
}
def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List[Dict]:
"""
@@ -313,6 +399,13 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
starting_balance=starting_balance,
results=results, skip_nan=False)
buy_tag_results = generate_tag_metrics("buy_tag",btdata, stake_currency=stake_currency,
starting_balance=starting_balance,
results=results, skip_nan=False)
sell_tag_results = generate_tag_metrics("sell_tag",btdata, stake_currency=stake_currency,
starting_balance=starting_balance,
results=results, skip_nan=False)
sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
results=results)
left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
@@ -336,6 +429,8 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
'best_pair': best_pair,
'worst_pair': worst_pair,
'results_per_pair': pair_results,
'results_per_buy_tag': buy_tag_results,
'results_per_sell_tag': sell_tag_results,
'sell_reason_summary': sell_reason_stats,
'left_open_trades': left_open_results,
'total_trades': len(results),
@@ -504,6 +599,27 @@ def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_curren
] for t in sell_reason_stats]
return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
def text_table_tags(tag_type:str, tag_results: List[Dict[str, Any]], stake_currency: str) -> str:
"""
Generates and returns a text table for the given backtest data and the results dataframe
:param pair_results: List of Dictionaries - one entry per pair + final TOTAL row
:param stake_currency: stake-currency - used to correctly name headers
:return: pretty printed table with tabulate as string
"""
headers = _get_line_header("TAG", stake_currency)
floatfmt = _get_line_floatfmt(stake_currency)
output = [[
t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
t['profit_total_pct'], t['duration_avg'],
_generate_wins_draws_losses(t['wins'], t['draws'], t['losses'])
] for t in tag_results]
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(output, headers=headers,
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
def text_table_strategy(strategy_results, stake_currency: str) -> str:
"""
@@ -624,12 +740,24 @@ def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency:
print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_tags("buy_tag", results['results_per_buy_tag'], stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0:
print(' BUY TAG STATS '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_sell_reason(sell_reason_stats=results['sell_reason_summary'],
stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0:
print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0:
print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
@@ -640,8 +768,16 @@ def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency:
print(' SUMMARY METRICS '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_tags("sell_tag",results['results_per_sell_tag'], stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0:
print(' SELL TAG STATS '.center(len(table.splitlines()[0]), '='))
print(table)
if isinstance(table, str) and len(table) > 0:
print('=' * len(table.splitlines()[0]))
print()