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Fixed flake 8, changed sell_tag to exit_tag and fixed telegram functions

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This commit is contained in:
theluxaz
2021-10-18 23:56:41 +03:00
parent 0bb7ea10ab
commit 69a59cdf37
10 changed files with 206 additions and 221 deletions
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21
freqtrade/optimize/backtesting.py
View File

@@ -44,7 +44,8 @@ SELL_IDX = 4
LOW_IDX = 5
HIGH_IDX = 6
BUY_TAG_IDX = 7
SELL_TAG_IDX = 8
EXIT_TAG_IDX = 8
class Backtesting:
"""
@@ -247,7 +248,7 @@ 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', 'sell_tag']
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high', 'buy_tag', 'exit_tag']
data: Dict = {}
self.progress.init_step(BacktestState.CONVERT, len(processed))
@@ -259,7 +260,7 @@ 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
pair_data.loc[:, 'exit_tag'] = None # cleanup if exit_tag is exist
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair}).copy()
@@ -271,7 +272,7 @@ 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)
df_analyzed.loc[:, 'exit_tag'] = df_analyzed.loc[:, 'exit_tag'].shift(1)
# Update dataprovider cache
self.dataprovider._set_cached_df(pair, self.timeframe, df_analyzed)
@@ -359,8 +360,10 @@ 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]
if(sell_row[EXIT_TAG_IDX] is not None):
trade.exit_tag = sell_row[EXIT_TAG_IDX]
else:
trade.exit_tag = None
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)
@@ -389,7 +392,7 @@ class Backtesting:
detail_data = detail_data.loc[
(detail_data['date'] >= sell_candle_time) &
(detail_data['date'] < sell_candle_end)
].copy()
].copy()
if len(detail_data) == 0:
# Fall back to "regular" data if no detail data was found for this candle
return self._get_sell_trade_entry_for_candle(trade, sell_row)
@@ -435,7 +438,7 @@ 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
has_exit_tag = len(row) >= EXIT_TAG_IDX + 1
trade = LocalTrade(
pair=pair,
open_rate=row[OPEN_IDX],
@@ -446,7 +449,7 @@ 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,
exit_tag=row[EXIT_TAG_IDX] if has_exit_tag else None,
exchange='backtesting',
)
return trade

93
freqtrade/optimize/optimize_reports.py
View File

@@ -54,6 +54,7 @@ def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration',
'Win Draw Loss Win%']
def _get_line_header_sell(first_column: str, stake_currency: str) -> List[str]:
"""
Generate header lines (goes in line with _generate_result_line())
@@ -134,12 +135,13 @@ 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]:
def generate_tag_metrics(tag_type: 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
@@ -148,32 +150,6 @@ def generate_tag_metrics(tag_type:str, data: Dict[str, Dict], stake_currency: st
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():
@@ -188,6 +164,7 @@ def generate_tag_metrics(tag_type:str, data: Dict[str, Dict], stake_currency: st
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.
@@ -408,12 +385,10 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
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)
buy_tag_results = generate_tag_metrics("buy_tag", starting_balance=starting_balance,
results=results, skip_nan=False)
exit_tag_results = generate_tag_metrics("exit_tag", starting_balance=starting_balance,
results=results, skip_nan=False)
sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
results=results)
@@ -439,7 +414,7 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
'worst_pair': worst_pair,
'results_per_pair': pair_results,
'results_per_buy_tag': buy_tag_results,
'results_per_sell_tag': sell_tag_results,
'results_per_exit_tag': exit_tag_results,
'sell_reason_summary': sell_reason_stats,
'left_open_trades': left_open_results,
'total_trades': len(results),
@@ -609,30 +584,38 @@ 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:
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
"""
if(tag_type=="buy_tag"):
if(tag_type == "buy_tag"):
headers = _get_line_header("TAG", stake_currency)
else:
headers = _get_line_header_sell("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]
output = [
[
t['key'] if t['key'] is not None and len(
t['key']) > 0 else "OTHER",
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:
"""
Generate summary table per strategy
@@ -752,14 +735,19 @@ 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)
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_tags("sell_tag",results['results_per_sell_tag'], stake_currency=stake_currency)
table = text_table_tags(
"exit_tag",
results['results_per_exit_tag'],
stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0:
print(' SELL TAG STATS '.center(len(table.splitlines()[0]), '='))
@@ -771,10 +759,6 @@ def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency:
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]), '='))
@@ -785,12 +769,9 @@ def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency:
print(' SUMMARY METRICS '.center(len(table.splitlines()[0]), '='))
print(table)
if isinstance(table, str) and len(table) > 0:
print('=' * len(table.splitlines()[0]))
print()