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V2.1.1

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Signed-off-by: TIANHE <TIANHE@GMAIL.COM>
This commit is contained in:
TIANHE
2026-01-31 02:59:49 +08:00
parent 2853e83885
commit 0b37aa4a67
58 changed files with 11231 additions and 8698 deletions
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backend_api_python/app/services/trading_executor.py
+86 -35
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@@ -555,6 +555,11 @@ class TradingExecutor:
trade_direction = 'long' # 现货只能做多
logger.info(f"Strategy {strategy_id} spot trading; force trade_direction=long")
# 获取市场类别（Crypto, USStock, Forex, Futures, AShare, HShare）
# 这决定了使用哪个数据源来获取价格和K线数据
market_category = (strategy.get('market_category') or 'Crypto').strip()
logger.info(f"Strategy {strategy_id} market_category: {market_category}")
# 初始化交易所连接（信号模式下无需真实连接）
exchange = None
@@ -611,7 +616,7 @@ class TradingExecutor:
# ============================================
# logger.info(f"策略 {strategy_id} 初始化：获取历史K线数据...")
history_limit = int(os.getenv('K_LINE_HISTORY_GET_NUMBER', 500))
klines = self._fetch_latest_kline(symbol, timeframe, limit=history_limit)
klines = self._fetch_latest_kline(symbol, timeframe, limit=history_limit, market_category=market_category)
if not klines or len(klines) < 2:
logger.error(f"Strategy {strategy_id} failed to fetch K-lines")
return
@@ -719,16 +724,16 @@ class TradingExecutor:
# ============================================
# 1. Fetch current price once per tick
# ============================================
current_price = self._fetch_current_price(exchange, symbol, market_type=market_type)
current_price = self._fetch_current_price(exchange, symbol, market_type=market_type, market_category=market_category)
if current_price is None:
logger.warning(f"Strategy {strategy_id} failed to fetch current price")
logger.warning(f"Strategy {strategy_id} failed to fetch current price for {market_category}:{symbol}")
continue
# ============================================
# 2. 检查是否需要更新K线（每个K线周期更新一次，从API拉取）
# ============================================
if current_time - last_kline_update_time >= kline_update_interval:
klines = self._fetch_latest_kline(symbol, timeframe, limit=history_limit)
klines = self._fetch_latest_kline(symbol, timeframe, limit=history_limit, market_category=market_category)
if klines and len(klines) >= 2:
df = self._klines_to_dataframe(klines)
if len(df) > 0:
@@ -978,6 +983,7 @@ class TradingExecutor:
leverage=leverage,
initial_capital=initial_capital,
market_type=market_type,
market_category=market_category,
execution_mode=execution_mode,
notification_config=notification_config,
trading_config=trading_config,
@@ -1041,7 +1047,8 @@ class TradingExecutor:
id, strategy_name, strategy_type, status,
initial_capital, leverage, decide_interval,
execution_mode, notification_config,
indicator_config, exchange_config, trading_config, ai_model_config
indicator_config, exchange_config, trading_config, ai_model_config,
market_category
FROM qd_strategies_trading
WHERE id = %s
"""
@@ -1104,25 +1111,39 @@ class TradingExecutor:
"""(Mock) 信号模式不需要真实交易所连接"""
return None
def _fetch_latest_kline(self, symbol: str, timeframe: str, limit: int = 500) -> List[Dict[str, Any]]:
"""获取最新K线数据（优先从缓存获取）"""
def _fetch_latest_kline(self, symbol: str, timeframe: str, limit: int = 500, market_category: str = 'Crypto') -> List[Dict[str, Any]]:
"""获取最新K线数据（优先从缓存获取）
Args:
symbol: 交易对/代码
timeframe: 时间周期
limit: 数据条数
market_category: 市场类型 (Crypto, USStock, Forex, Futures, AShare, HShare)
"""
try:
# 使用 KlineService 获取K线数据（自动处理缓存）
return self.kline_service.get_kline(
market='Crypto',
market=market_category,
symbol=symbol,
timeframe=timeframe,
limit=limit,
before_time=int(time.time())
)
except Exception as e:
logger.error(f"Failed to fetch K-lines: {str(e)}")
logger.error(f"Failed to fetch K-lines for {market_category}:{symbol}: {str(e)}")
return []
def _fetch_current_price(self, exchange: Any, symbol: str, market_type: str = None) -> Optional[float]:
"""获取当前价格 (改用 DataSource)"""
def _fetch_current_price(self, exchange: Any, symbol: str, market_type: str = None, market_category: str = 'Crypto') -> Optional[float]:
"""获取当前价格 (根据 market_category 选择正确的数据源)
Args:
exchange: 交易所实例（信号模式下为 None）
symbol: 交易对/代码
market_type: 交易类型 (swap/spot)
market_category: 市场类型 (Crypto, USStock, Forex, Futures, AShare, HShare)
"""
# Local in-memory cache first
cache_key = (symbol or "").strip().upper()
cache_key = f"{market_category}:{(symbol or '').strip().upper()}"
if cache_key and self._price_cache_ttl_sec > 0:
now = time.time()
try:
@@ -1138,12 +1159,9 @@ class TradingExecutor:
pass
try:
# 默认使用 binance 获取价格 (或者根据配置)
# 简单起见，这里硬编码或使用 generic source
ds = DataSourceFactory.get_data_source('binance')
# normalized symbol handling is tricky without exchange object.
# But usually DataSource expects standard 'BTC/USDT'
ticker = ds.get_ticker(symbol)
# 根据 market_category 选择正确的数据源
# 支持: Crypto, USStock, Forex, Futures, AShare, HShare
ticker = DataSourceFactory.get_ticker(market_category, symbol)
if ticker:
price = float(ticker.get('last') or ticker.get('close') or 0)
if price > 0:
@@ -1155,7 +1173,7 @@ class TradingExecutor:
pass
return price
except Exception as e:
logger.warning(f"Failed to fetch price: {e}")
logger.warning(f"Failed to fetch price for {market_category}:{symbol}: {e}")
return None
@@ -1887,6 +1905,7 @@ class TradingExecutor:
leverage: int,
initial_capital: float,
market_type: str = 'swap',
market_category: str = 'Crypto',
margin_mode: str = 'cross',
stop_loss_price: float = None,
take_profit_price: float = None,
@@ -2013,6 +2032,7 @@ class TradingExecutor:
amount=amount,
ref_price=float(current_price or 0.0),
market_type=market_type,
market_category=market_category,
leverage=leverage,
execution_mode=execution_mode,
notification_config=notification_config,
@@ -2049,16 +2069,28 @@ class TradingExecutor:
)
elif sig.startswith("reduce_"):
# Partial scale-out: reduce position size, keep entry price unchanged.
self._record_trade(
strategy_id=strategy_id, symbol=symbol, type=signal_type,
price=current_price, amount=amount, value=amount*current_price
)
# 信号模式下计算部分平仓盈亏
side = 'short' if 'short' in signal_type else 'long'
old_pos = next((p for p in current_positions if p.get('side') == side), None)
if not old_pos:
return True
old_size = float(old_pos.get('size') or 0.0)
old_entry = float(old_pos.get('entry_price') or 0.0)
# 计算减仓部分的盈亏（信号模式下，不含手续费）
reduce_profit = None
if old_entry > 0 and amount > 0:
if side == 'long':
reduce_profit = (current_price - old_entry) * amount
else:
reduce_profit = (old_entry - current_price) * amount
self._record_trade(
strategy_id=strategy_id, symbol=symbol, type=signal_type,
price=current_price, amount=amount, value=amount*current_price,
profit=reduce_profit
)
new_size = max(0.0, old_size - float(amount or 0.0))
if new_size <= old_size * 0.001:
self._close_position(strategy_id, symbol, side)
@@ -2068,11 +2100,25 @@ class TradingExecutor:
size=new_size, entry_price=old_entry, current_price=current_price
)
elif 'close' in sig:
# 信号模式下计算平仓盈亏
side = 'short' if 'short' in signal_type else 'long'
old_pos = next((p for p in current_positions if p.get('side') == side), None)
# 计算盈亏（信号模式下，不含手续费）
close_profit = None
if old_pos:
entry_price = float(old_pos.get('entry_price') or 0)
if entry_price > 0 and amount > 0:
if side == 'long':
close_profit = (current_price - entry_price) * amount
else:
close_profit = (entry_price - current_price) * amount
self._record_trade(
strategy_id=strategy_id, symbol=symbol, type=signal_type,
price=current_price, amount=amount, value=amount*current_price
price=current_price, amount=amount, value=amount*current_price,
profit=close_profit
)
side = 'short' if 'short' in signal_type else 'long'
self._close_position(strategy_id, symbol, side)
return True
@@ -2145,25 +2191,29 @@ class TradingExecutor:
language = str(language or "zh-CN")
try:
# Lazy import to avoid circular deps + heavy init unless the filter is enabled and entry signal happens.
from app.services.analysis import AnalysisService
# 使用新的 FastAnalysisService (单次LLM调用，更快更稳定)
from app.services.fast_analysis import get_fast_analysis_service
service = AnalysisService()
service = get_fast_analysis_service()
result = service.analyze(market, symbol, language, model=model)
if isinstance(result, dict) and result.get("error"):
return False, {"ai_decision": "", "reason": "analysis_error", "analysis_error": str(result.get("error") or "")}
ai_dec = self._extract_ai_trade_decision(result)
if not ai_dec:
return False, {"ai_decision": "", "reason": "missing_ai_decision"}
# FastAnalysisService 直接返回 decision 字段
ai_dec = str(result.get("decision", "")).strip().upper()
if not ai_dec or ai_dec not in ("BUY", "SELL", "HOLD"):
return False, {"ai_decision": ai_dec, "reason": "missing_ai_decision"}
expected = "BUY" if signal_type == "open_long" else "SELL"
confidence = result.get("confidence", 50)
summary = result.get("summary", "")
if ai_dec == expected:
return True, {"ai_decision": ai_dec, "reason": "match"}
return True, {"ai_decision": ai_dec, "reason": "match", "confidence": confidence, "summary": summary}
if ai_dec == "HOLD":
return False, {"ai_decision": ai_dec, "reason": "ai_hold"}
return False, {"ai_decision": ai_dec, "reason": "direction_mismatch"}
return False, {"ai_decision": ai_dec, "reason": "ai_hold", "confidence": confidence, "summary": summary}
return False, {"ai_decision": ai_dec, "reason": "direction_mismatch", "confidence": confidence, "summary": summary}
except Exception as e:
return False, {"ai_decision": "", "reason": "analysis_exception", "analysis_error": str(e)}
@@ -2262,6 +2312,7 @@ class TradingExecutor:
amount: float,
ref_price: Optional[float] = None,
market_type: str = 'swap',
market_category: str = 'Crypto',
leverage: float = 1.0,
margin_mode: str = 'cross',
stop_loss_price: float = None,
@@ -2291,7 +2342,7 @@ class TradingExecutor:
try:
# Reference price at enqueue time: use current tick price if provided to avoid extra fetch.
if ref_price is None:
ref_price = self._fetch_current_price(None, symbol) or 0.0
ref_price = self._fetch_current_price(None, symbol, market_category=market_category) or 0.0
ref_price = float(ref_price or 0.0)
extra_payload = {