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Refactor and translate comments and docstrings in utility modules to English for better clarity and maintainability. Update Gunicorn and application startup messages for consistency in language. Enhance documentation with English translations for better accessibility.

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dienakdz
2026-04-06 16:47:36 +07:00
parent 3ca291a346
commit 11e2e5aaa6
64 changed files with 2323 additions and 2336 deletions
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backend_api_python/app/services/backtest.py
+40 -40
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@@ -877,7 +877,7 @@ class BacktestService:
if i > 0 and i % progress_log_interval == 0:
progress_pct = (i / total_exec_candles) * 100
logger.info(f"Execution progress: {i}/{total_exec_candles} ({progress_pct:.1f}%), trades={executed_trades_count}, position={position}")
# 爆仓后直接停止回测，输出结果
# After the liquidation, the backtest will be stopped directly and the results will be output.
if is_liquidated:
break
@@ -1175,7 +1175,7 @@ class BacktestService:
executed_trades_count += 1
if executed_trades_count <= 10:
logger.info(f"Trade #{executed_trades_count}: close_short (before open_long) @ {timestamp}, price={close_price:.4f}, profit={close_profit:.2f}")
# 检查是否爆仓
# Check whether the position is liquidated
if capital < min_capital_to_trade:
is_liquidated = True
capital = 0
@@ -1232,7 +1232,7 @@ class BacktestService:
highest_since_entry = None
lowest_since_entry = None
pending_signal = None
# 检查是否爆仓
# Check whether the position is liquidated
if capital < min_capital_to_trade:
is_liquidated = True
capital = 0
@@ -1263,7 +1263,7 @@ class BacktestService:
executed_trades_count += 1
if executed_trades_count <= 10:
logger.info(f"Trade #{executed_trades_count}: close_long (before open_short) @ {timestamp}, price={close_price:.4f}, profit={close_profit:.2f}")
# 检查是否爆仓
# Check whether the position is liquidated
if capital < min_capital_to_trade:
is_liquidated = True
capital = 0
@@ -1321,7 +1321,7 @@ class BacktestService:
highest_since_entry = None
lowest_since_entry = None
pending_signal = None
# 检查是否爆仓
# Check whether the position is liquidated
if capital < min_capital_to_trade:
is_liquidated = True
capital = 0
@@ -1763,16 +1763,16 @@ class BacktestService:
local_vars['commission'] = backtest_params.get('commission', 0.0002)
local_vars['trade_direction'] = backtest_params.get('trade_direction', 'both')
# === 指标参数支持 ===
# 从 backtest_params 获取用户设置的指标参数
# === Indicator parameter support ===
# Get the indicator parameters set by the user from backtest_params
user_indicator_params = (backtest_params or {}).get('indicator_params', {})
# 解析指标代码中声明的参数
# Parse the parameters declared in the indicator code
declared_params = IndicatorParamsParser.parse_params(code)
# 合并参数（用户值优先，否则使用默认值）
# Merge parameters (user values ​​take precedence, otherwise default values ​​are used)
merged_params = IndicatorParamsParser.merge_params(declared_params, user_indicator_params)
local_vars['params'] = merged_params
# === 指标调用器支持 ===
# === Indicator caller support ===
user_id = (backtest_params or {}).get('user_id', 1)
indicator_id = (backtest_params or {}).get('indicator_id')
indicator_caller = IndicatorCaller(user_id, indicator_id)
@@ -2453,7 +2453,7 @@ import pandas as pd
add_short_price_arr = signals.get('add_short_price', pd.Series([0.0] * len(df))).values
for i, (timestamp, row) in enumerate(df.iterrows()):
# 爆仓后直接停止回测，输出结果
# After the liquidation, the backtest will be stopped directly and the results will be output.
if is_liquidated:
break
@@ -2471,7 +2471,7 @@ import pandas as pd
'balance': 0
})
equity_curve.append({'time': timestamp.strftime('%Y-%m-%d %H:%M'), 'value': 0})
break # 直接停止
break # Stop directly
# Use OHLC to evaluate triggers.
high = row['high']
@@ -3126,7 +3126,7 @@ import pandas as pd
lowest_since_entry = None
trend_add_times = dca_add_times = trend_reduce_times = adverse_reduce_times = 0
last_trend_add_anchor = last_dca_add_anchor = last_trend_reduce_anchor = last_adverse_reduce_anchor = None
# 检查是否爆仓
# Check whether the position is liquidated
if capital < min_capital_to_trade:
is_liquidated = True
capital = 0
@@ -3251,7 +3251,7 @@ import pandas as pd
lowest_since_entry = None
trend_add_times = dca_add_times = trend_reduce_times = adverse_reduce_times = 0
last_trend_add_anchor = last_dca_add_anchor = last_trend_reduce_anchor = last_adverse_reduce_anchor = None
# 检查是否爆仓
# Check whether the position is liquidated
if capital < min_capital_to_trade:
is_liquidated = True
capital = 0
@@ -3354,7 +3354,7 @@ import pandas as pd
# If liquidation hit, check SL signal first
if position != 0 and not is_liquidated:
if position_type == 'long' and low <= liquidation_price:
# Long触及爆仓线：检查是否有止损信号
# Long touches the liquidation line: check whether there is a stop loss signal
has_stop_loss = close_long_arr[i] and close_long_price_arr[i] > 0
stop_loss_price = close_long_price_arr[i] if has_stop_loss else 0
@@ -3397,7 +3397,7 @@ import pandas as pd
continue
elif position_type == 'short' and high >= liquidation_price:
# Short触及爆仓线：检查是否有止损信号
# Short hits the liquidation line: check if there is a stop loss signal
has_stop_loss = close_short_arr[i] and close_short_price_arr[i] > 0
stop_loss_price = close_short_price_arr[i] if has_stop_loss else 0
@@ -3530,7 +3530,7 @@ import pandas as pd
strategy_config: Optional[Dict[str, Any]] = None
) -> tuple:
"""
使用旧格式信号进行交易模拟（保持兼容性）
Trading simulation using old format signals (maintaining compatibility)
"""
equity_curve = []
trades = []
@@ -3628,7 +3628,7 @@ import pandas as pd
signals_exec = signals
for i, (timestamp, row) in enumerate(df.iterrows()):
# 爆仓后直接停止回测，输出结果
# After the liquidation, the backtest will be stopped directly and the results will be output.
if is_liquidated:
break
@@ -3775,7 +3775,7 @@ import pandas as pd
if signal == 0 and position != 0 and position_type in ['long', 'short'] and capital >= min_capital_to_trade:
# Long
if position_type == 'long' and position > 0:
# Trend add（顺势加仓：上涨触发）
# Trend add (increase positions with the trend: rising trigger)
if trend_add_enabled and trend_add_step_pct_eff > 0 and trend_add_size_pct > 0 and (trend_add_max_times == 0 or trend_add_times < trend_add_max_times):
anchor = last_trend_add_anchor if last_trend_add_anchor is not None else entry_price
trigger = anchor * (1 + trend_add_step_pct_eff)
@@ -3808,7 +3808,7 @@ import pandas as pd
'balance': round(max(0, capital), 2)
})
# DCA add（逆势加仓：下跌触发）
# DCA add (Add position against the trend: Triggered by decline)
if dca_add_enabled and dca_add_step_pct_eff > 0 and dca_add_size_pct > 0 and (dca_add_max_times == 0 or dca_add_times < dca_add_max_times):
anchor = last_dca_add_anchor if last_dca_add_anchor is not None else entry_price
trigger = anchor * (1 - dca_add_step_pct_eff)
@@ -3841,7 +3841,7 @@ import pandas as pd
'balance': round(max(0, capital), 2)
})
# Trend reduce（顺势减仓：上涨触发）
# Trend reduce (reduce positions with the trend: triggered by rising prices)
if trend_reduce_enabled and trend_reduce_step_pct_eff > 0 and trend_reduce_size_pct > 0 and (trend_reduce_max_times == 0 or trend_reduce_times < trend_reduce_max_times):
anchor = last_trend_reduce_anchor if last_trend_reduce_anchor is not None else entry_price
trigger = anchor * (1 + trend_reduce_step_pct_eff)
@@ -3876,7 +3876,7 @@ import pandas as pd
'balance': round(max(0, capital), 2)
})
# Adverse reduce（逆势减仓：下跌触发）
# Adverse reduce (against the trend: falling trigger)
if position_type == 'long' and position > 0 and adverse_reduce_enabled and adverse_reduce_step_pct_eff > 0 and adverse_reduce_size_pct > 0 and (adverse_reduce_max_times == 0 or adverse_reduce_times < adverse_reduce_max_times):
anchor = last_adverse_reduce_anchor if last_adverse_reduce_anchor is not None else entry_price
trigger = anchor * (1 - adverse_reduce_step_pct_eff)
@@ -3915,7 +3915,7 @@ import pandas as pd
if position_type == 'short' and position < 0:
shares_total = abs(position)
# Trend add（顺势加空：下跌触发）
# Trend add (Add short with the trend: triggered by decline)
if trend_add_enabled and trend_add_step_pct_eff > 0 and trend_add_size_pct > 0 and (trend_add_max_times == 0 or trend_add_times < trend_add_max_times):
anchor = last_trend_add_anchor if last_trend_add_anchor is not None else entry_price
trigger = anchor * (1 - trend_add_step_pct_eff)
@@ -3949,7 +3949,7 @@ import pandas as pd
'balance': round(max(0, capital), 2)
})
# DCA add（逆势加空：上涨触发）
# DCA add (Add short against the trend: rise trigger)
if dca_add_enabled and dca_add_step_pct_eff > 0 and dca_add_size_pct > 0 and (dca_add_max_times == 0 or dca_add_times < dca_add_max_times):
anchor = last_dca_add_anchor if last_dca_add_anchor is not None else entry_price
trigger = anchor * (1 + dca_add_step_pct_eff)
@@ -3983,7 +3983,7 @@ import pandas as pd
'balance': round(max(0, capital), 2)
})
# Trend reduce（顺势减空：下跌触发，回补一部分）
# Trend reduce (short reduction: triggered by decline, covering part of it)
if trend_reduce_enabled and trend_reduce_step_pct_eff > 0 and trend_reduce_size_pct > 0 and (trend_reduce_max_times == 0 or trend_reduce_times < trend_reduce_max_times):
anchor = last_trend_reduce_anchor if last_trend_reduce_anchor is not None else entry_price
trigger = anchor * (1 - trend_reduce_step_pct_eff)
@@ -4019,7 +4019,7 @@ import pandas as pd
'balance': round(max(0, capital), 2)
})
# Adverse reduce（逆势减空：上涨触发）
# Adverse reduce (against the trend: rising trigger)
if position_type == 'short' and position < 0 and adverse_reduce_enabled and adverse_reduce_step_pct_eff > 0 and adverse_reduce_size_pct > 0 and (adverse_reduce_max_times == 0 or adverse_reduce_times < adverse_reduce_max_times):
anchor = last_adverse_reduce_anchor if last_adverse_reduce_anchor is not None else entry_price
trigger = anchor * (1 + adverse_reduce_step_pct_eff)
@@ -4464,7 +4464,7 @@ import pandas as pd
# Note: check after all signals, SL/TP takes priority
if position != 0 and not is_liquidated:
if position_type == 'long':
# Long爆仓：价格跌破爆仓线
# Long liquidation: the price falls below the liquidation line
if price <= liquidation_price:
logger.warning(f"Long liquidation! entry={entry_price:.2f}, current={price:.2f}, liq_price={liquidation_price:.2f}")
is_liquidated = True
@@ -4486,7 +4486,7 @@ import pandas as pd
})
continue
elif position_type == 'short':
# Short爆仓：价格涨破爆仓线
# Short liquidation: the price rises above the liquidation line
if price >= liquidation_price:
logger.warning(f"Short liquidation! entry={entry_price:.2f}, current={price:.2f}, liq_price={liquidation_price:.2f}")
is_liquidated = True
@@ -4603,7 +4603,7 @@ import pandas as pd
end_date: datetime,
total_commission: float = 0
) -> Dict:
"""计算回测指标"""
"""Calculate backtest indicators"""
if not equity_curve:
return {}
@@ -4670,7 +4670,7 @@ import pandas as pd
}
def _calculate_max_drawdown(self, values: List[float]) -> float:
"""计算最大回撤"""
"""Calculate maximum drawdown"""
if not values:
return 0
@@ -4688,12 +4688,12 @@ import pandas as pd
def _calculate_sharpe(self, values: List[float], timeframe: str = '1D', risk_free_rate: float = 0.02) -> float:
"""
计算夏普比率
Calculate Sharpe Ratio
Args:
values: 权益曲线数值列表
timeframe: 时间周期
risk_free_rate: 无风险收益率（年化）
values: list of equity curve values
timeframe: time period
risk_free_rate: risk-free rate of return (annualized)
"""
if len(values) < 2:
return 0
@@ -4706,11 +4706,11 @@ import pandas as pd
# Determine annualization factor by timeframe
annualization_factor = {
'1m': 252 * 24 * 60, # 1m candle: ~362,880
'5m': 252 * 24 * 12, # 5分钟K：约72,576
'15m': 252 * 24 * 4, # 15分钟K：约24,192
'30m': 252 * 24 * 2, # 30分钟K：约12,096
'5m': 252 * 24 * 12, # 5 minutes K: about 72,576
'15m': 252 * 24 * 4, # 15 minutes K: about 24,192
'30m': 252 * 24 * 2, # 30 minutes K: about 12,096
'1H': 252 * 24, # 1H candle: 6,048
'4H': 252 * 6, # 4小时K：1,512
'4H': 252 * 6, # 4 hours K: 1,512
'1D': 252, # 1D candle: 252
'1W': 52 # 1W candle: 52
}.get(timeframe, 252)
@@ -4746,7 +4746,7 @@ import pandas as pd
equity_curve: List,
trades: List
) -> Dict[str, Any]:
"""格式化回测结果"""
"""Format backtest results"""
# Simplify equity curve
max_points = 500
if len(equity_curve) > max_points:
@@ -4755,7 +4755,7 @@ import pandas as pd
# Clean NaN/Inf values for JSON serialization
def clean_value(value):
"""清理数值，将NaN/Inf转换为0"""
"""Clean values, convert NaN/Inf to 0"""
if isinstance(value, float):
if np.isnan(value) or np.isinf(value):
return 0