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ddrwode
2026-02-21 15:38:23 +08:00
parent 2d9914ec19
commit f0fe26acbf
5 changed files with 126 additions and 40 deletions
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models/scheme_b_last_model.joblib
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models/scheme_b_scaler.joblib
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71
strategy/ai_strategy.py
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@@ -9,8 +9,9 @@ import numpy as np
from pathlib import Path
from loguru import logger
from sklearn.utils.class_weight import compute_sample_weight
from sklearn.preprocessing import StandardScaler
from .config import MODEL_CONFIG as MC, PRIMARY_PERIOD, PROJECT_ROOT
from .config import MODEL_CONFIG as MC, FEATURE_CONFIG as FC, PRIMARY_PERIOD, PROJECT_ROOT
from .feature_engine import prepare_dataset, get_latest_feature_row
from .backtest import BacktestEngine, print_metrics
@@ -29,6 +30,8 @@ class AIStrategy:
"""
self.model_type = model_type
self.models = [] # 存储每个窗口训练的模型
self.scalers = [] # 与每个窗口模型对应的 scaler（若启用标准化）
self.last_scaler = None
self.feature_importance = None
def _create_model(self):
@@ -45,7 +48,8 @@ class AIStrategy:
raise ValueError(f"不支持的模型类型: {self.model_type}")
def walk_forward_train(self, X: pd.DataFrame, y: pd.Series,
confidence_threshold: float = 0.45) -> pd.Series:
confidence_threshold: float = 0.45,
normalize: bool = True) -> pd.Series:
"""
Walk-Forward 滚动训练与预测
:param confidence_threshold: 概率阈值，低于此值的预测设为0（观望）
@@ -56,27 +60,47 @@ class AIStrategy:
step = MC['walk_forward_step']
n = len(X)
all_preds = pd.Series(dtype=float)
all_preds = pd.Series(dtype=int)
window_count = 0
logger.info(f"Walk-Forward: 数据量={n}, 训练窗口={train_size}, "
f"测试窗口={test_size}, 步长={step}, 置信阈值={confidence_threshold}")
f"测试窗口={test_size}, 步长={step}, 置信阈值={confidence_threshold}, "
f"窗口内标准化={'是' if normalize else '否'}")
start = 0
while start + train_size + test_size <= n:
train_end = start + train_size
test_end = min(train_end + test_size, n)
X_train = X.iloc[start:train_end]
X_train_raw = X.iloc[start:train_end]
y_train = y.iloc[start:train_end]
X_test = X.iloc[train_end:test_end]
X_test_raw = X.iloc[train_end:test_end]
y_test = y.iloc[train_end:test_end]
# 每个窗口单独拟合 scaler，避免未来数据泄露
if normalize:
scaler = StandardScaler()
X_train = pd.DataFrame(
scaler.fit_transform(X_train_raw),
columns=X.columns,
index=X_train_raw.index,
)
X_test = pd.DataFrame(
scaler.transform(X_test_raw),
columns=X.columns,
index=X_test_raw.index,
)
else:
scaler = None
X_train = X_train_raw
X_test = X_test_raw
# 训练：使用类别平衡权重，避免模型偏向做多、很少出做空
model = self._create_model()
sw = compute_sample_weight('balanced', y_train)
model.fit(X_train, y_train, sample_weight=sw)
self.models.append(model)
self.scalers.append(scaler)
# 预测概率 + 置信度过滤
proba = model.predict_proba(X_test)
@@ -107,6 +131,7 @@ class AIStrategy:
self.feature_importance = pd.Series(
last_model.feature_importances_, index=X.columns
).sort_values(ascending=False)
self.last_scaler = self.scalers[-1] if self.scalers else None
logger.info(f"Walk-Forward 完成: {window_count} 个窗口, "
f"共 {len(all_preds)} 条预测")
@@ -141,10 +166,16 @@ class AIStrategy:
load_start = load_start_ts.strftime('%Y-%m-%d')
logger.info(f"回测区间 [{start_date} ~ {end_date}]，向前加载 {warm_months} 月至 {load_start} 用于训练")
X, y, feature_names, scaler = prepare_dataset(period, load_start, load_end)
# AI 策略使用窗口内标准化，prepare_dataset 此处返回未标准化特征，避免全样本泄露
X, y, feature_names, _ = prepare_dataset(period, load_start, load_end, normalize=False)
# 2. Walk-Forward 训练
predictions = self.walk_forward_train(X, y)
predictions = self.walk_forward_train(
X,
y,
confidence_threshold=MC.get('confidence_threshold', 0.45),
normalize=bool(FC.get('normalize', True)),
)
# 3. 回测仅用用户指定区间；将预测对齐到该区间的每根K线
df = load_kline(period, start_date, end_date)
@@ -172,13 +203,18 @@ class AIStrategy:
logger.info("-" * 50)
# 5. 保存最后一窗模型、scaler、特征列（供实盘 get_live_signal 使用）
if self.models and scaler is not None:
if self.models:
SCHEME_B_MODEL_DIR.mkdir(parents=True, exist_ok=True)
joblib.dump(self.models[-1], SCHEME_B_MODEL_FILE)
joblib.dump(scaler, SCHEME_B_SCALER_FILE)
# 若未启用标准化，保存 None，实盘推理时将自动跳过 transform
joblib.dump(self.last_scaler, SCHEME_B_SCALER_FILE)
with open(SCHEME_B_FEATURES_FILE, 'w', encoding='utf-8') as f:
json.dump(feature_names, f, ensure_ascii=False)
logger.info(f"已保存方案B模型: {SCHEME_B_MODEL_FILE}, scaler, {len(feature_names)} 个特征")
logger.info(
f"已保存方案B模型: {SCHEME_B_MODEL_FILE}, "
f"scaler={'启用' if self.last_scaler is not None else '无'}, "
f"{len(feature_names)} 个特征"
)
# 6. 输出特征重要性
top_feat = self.get_top_features(15)
@@ -210,18 +246,23 @@ def get_live_signal(period: int = None, model_type: str = 'lightgbm',
"""
if period is None:
period = PRIMARY_PERIOD
if not SCHEME_B_MODEL_FILE.exists() or not SCHEME_B_SCALER_FILE.exists() or not SCHEME_B_FEATURES_FILE.exists():
if not SCHEME_B_MODEL_FILE.exists() or not SCHEME_B_FEATURES_FILE.exists():
logger.warning("方案B模型未找到，请先运行 AI 策略训练保存模型")
return 0
model = joblib.load(SCHEME_B_MODEL_FILE)
scaler = joblib.load(SCHEME_B_SCALER_FILE)
scaler = None
if SCHEME_B_SCALER_FILE.exists():
scaler = joblib.load(SCHEME_B_SCALER_FILE)
with open(SCHEME_B_FEATURES_FILE, 'r', encoding='utf-8') as f:
feature_cols = json.load(f)
X_last = get_latest_feature_row(period, feature_cols, start_date, end_date)
if X_last.empty:
return 0
X_scaled = scaler.transform(X_last)
X_scaled_df = pd.DataFrame(X_scaled, columns=feature_cols, index=X_last.index)
if scaler is not None:
X_scaled = scaler.transform(X_last)
X_scaled_df = pd.DataFrame(X_scaled, columns=feature_cols, index=X_last.index)
else:
X_scaled_df = X_last.copy()
proba = model.predict_proba(X_scaled_df)[0] # (p0, p1, p2)
pred = model.predict(X_scaled_df)[0]
confidence_threshold = MC.get('confidence_threshold', 0.45)

31
strategy/feature_engine.py
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@@ -19,13 +19,14 @@ def _add_intra15m_1m_pullback_features(df: pd.DataFrame, primary_df: pd.DataFram
"""
if one_min_df.empty or 'high' not in one_min_df.columns or 'low' not in one_min_df.columns:
return df
# 每根 1m K 线归属到其所在 15m 的结束时间
# 每根 1m K 线归属到其所在 15m 的起始时间（数据库 15m id 与 bar 起始时刻对齐）
one_min = one_min_df[['high', 'low']].copy()
one_min['15m_end'] = one_min.index.ceil('15min')
agg = one_min.groupby('15m_end').agg(intra_high_1m=('high', 'max'), intra_low_1m=('low', 'min'))
one_min['15m_start'] = one_min.index.floor('15min')
agg = one_min.groupby('15m_start').agg(intra_high_1m=('high', 'max'), intra_low_1m=('low', 'min'))
# 对齐到主周期索引
agg = agg.reindex(primary_df.index)
agg = agg.ffill().bfill() # 边界缺失时前后填充
# 仅前向填充，避免使用未来数据回填到过去
agg = agg.ffill()
h15 = primary_df['high'].values
l15 = primary_df['low'].values
c15 = primary_df['close'].values
@@ -126,10 +127,10 @@ def generate_labels(df: pd.DataFrame, forward_periods: int = None,
def prepare_dataset(period: int = None, start_date: str = None, end_date: str = None,
has_volume: bool = False) -> tuple:
has_volume: bool = False, normalize: bool = None) -> tuple:
"""
一键准备训练数据集
:return: (X, y, feature_names) — 已去NaN、已标准化
:return: (X, y, feature_names, scaler) — 已去NaN；是否标准化由 normalize 决定
"""
if period is None:
period = PRIMARY_PERIOD
@@ -193,9 +194,11 @@ def prepare_dataset(period: int = None, start_date: str = None, end_date: str =
X = df[feature_cols].copy()
y = df['label'].astype(int)
# 标准化
# 标准化（可选）
scaler = None
if FC['normalize']:
if normalize is None:
normalize = FC['normalize']
if normalize:
scaler = StandardScaler()
X = pd.DataFrame(scaler.fit_transform(X), columns=feature_cols, index=X.index)
logger.info("特征已标准化")
@@ -228,12 +231,14 @@ def get_latest_feature_row(period: int = None, feature_cols: list = None,
except Exception:
pass
df = build_features(primary_df, aux_dfs, has_volume=False)
labels = generate_labels(df)
df = df.copy()
df['label'] = labels
df.dropna(inplace=True)
missing = [c for c in feature_cols if c not in df.columns]
if missing:
logger.warning(f"get_latest_feature_row: 缺少特征列 {missing[:5]}{'...' if len(missing) > 5 else ''}")
return pd.DataFrame()
return df[feature_cols].iloc[-1:].copy()
X = df[feature_cols].copy()
X.replace([np.inf, -np.inf], np.nan, inplace=True)
X.dropna(inplace=True)
if X.empty:
logger.warning("get_latest_feature_row: 可用于预测的特征为空（数据不足或存在NaN）")
return pd.DataFrame()
return X.iloc[-1:].copy()

64
四分之一，五分钟，反手条件充足.py
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@@ -275,14 +275,44 @@ class BitmartFuturesTransaction:
return False
def 平仓(self):
"""平仓操作"""
"""平仓操作（优先显式平仓按钮，失败时回退到反向市价）"""
self.click_safe('x://span[normalize-space(text()) ="市价"]')
# 优先点击页面上的平仓按钮，避免误开反向仓
close_selectors = [
'x://span[contains(normalize-space(text()),"一键平仓")]',
'x://button[contains(normalize-space(text()),"一键平仓")]',
'x://span[contains(normalize-space(text()),"全部平仓")]',
'x://button[contains(normalize-space(text()),"全部平仓")]',
'x://span[contains(normalize-space(text()),"平多")]',
'x://button[contains(normalize-space(text()),"平多")]',
'x://span[contains(normalize-space(text()),"平空")]',
'x://button[contains(normalize-space(text()),"平空")]',
]
for xpath in close_selectors:
if self.click_safe(xpath):
logger.info(f"平仓点击成功: {xpath}")
return True
# 回退：若没有显式平仓按钮，使用反向市价尝试平仓
if self.start == 1 and self.click_safe('x://span[normalize-space(text()) ="卖出/做空"]'):
logger.info("未找到显式平仓按钮，使用反向市价卖出平多")
return True
if self.start == -1 and self.click_safe('x://span[normalize-space(text()) ="买入/做多"]'):
logger.info("未找到显式平仓按钮，使用反向市价买入平空")
return True
logger.warning("平仓操作未触发任何按钮")
return False
def 开单(self, marketPriceLongOrder=0, limitPriceShortOrder=0, size=None, price=None):
"""
marketPriceLongOrder 市价做多或者做空，1是做多，-1是做空
limitPriceShortOrder 限价做多或者做空
"""
if size is not None:
self.input_size(size)
time.sleep(0.2)
if marketPriceLongOrder == -1:
# self.click_safe('x://button[normalize-space(text()) ="市价"]')
# self.page.ele('x://*[@id="size_0"]').input(vals=size, clear=True)
@@ -456,7 +486,7 @@ class BitmartFuturesTransaction:
return False
return True
def can_reverse(self, current_price, trigger_price):
def can_reverse(self, current_price, trigger_price=None):
"""反手前过滤：冷却时间 + 最小价差"""
now = time.time()
if self.last_reverse_time and now - self.last_reverse_time < self.reverse_cooldown_seconds:
@@ -464,9 +494,12 @@ class BitmartFuturesTransaction:
logger.info(f"反手冷却中，剩余 {remain:.0f} 秒")
return False
if trigger_price and trigger_price > 0:
if trigger_price is not None and trigger_price > 0:
move_pct = abs(current_price - trigger_price) / trigger_price * 100
if move_pct < self.reverse_min_move_pct:
# 触发价与现价相同（或近似相同）时，不做最小价差过滤，避免策略被永久拦截
if abs(current_price - trigger_price) < 1e-9:
logger.debug("反手价差过滤跳过：触发价与现价一致")
elif move_pct < self.reverse_min_move_pct:
logger.info(f"反手价差不足: {move_pct:.4f}% < {self.reverse_min_move_pct}%")
return False
@@ -514,10 +547,11 @@ class BitmartFuturesTransaction:
"""执行交易。size 不传或为 None 时使用 default_order_size。"""
signal_type, trigger_price = signal
size = self.default_order_size if size is None else size
trigger_price_text = f"{trigger_price:.2f}" if isinstance(trigger_price, (int, float)) else "N/A"
if signal_type == 'long':
# 开多前先确认无持仓
logger.info(f"准备开多，触发价: {trigger_price:.2f}")
logger.info(f"准备开多，触发价: {trigger_price_text}")
if not self.get_position_status():
logger.error("开仓前查询持仓状态失败，放弃开仓")
return False
@@ -543,7 +577,7 @@ class BitmartFuturesTransaction:
elif signal_type == 'short':
# 开空前先确认无持仓
logger.info(f"准备开空，触发价: {trigger_price:.2f}")
logger.info(f"准备开空，触发价: {trigger_price_text}")
if not self.get_position_status():
logger.error("开仓前查询持仓状态失败，放弃开仓")
return False
@@ -569,8 +603,10 @@ class BitmartFuturesTransaction:
elif signal_type == 'reverse_long':
# 平空 + 开多（反手做多）：先平仓，确认无仓后再开多，避免双向持仓
logger.info(f"执行反手做多，触发价: {trigger_price:.2f}")
self.平仓()
logger.info(f"执行反手做多，触发价: {trigger_price_text}")
if not self.平仓():
logger.warning("反手做多：平仓按钮未触发，放弃本次反手")
return False
time.sleep(1) # 给交易所处理平仓的时间
# 轮询确认已无持仓再开多（最多等约 10 秒）
for _ in range(10):
@@ -597,8 +633,10 @@ class BitmartFuturesTransaction:
elif signal_type == 'reverse_short':
# 平多 + 开空（反手做空）：先平仓，确认无仓后再开空
logger.info(f"执行反手做空，触发价: {trigger_price:.2f}")
self.平仓()
logger.info(f"执行反手做空，触发价: {trigger_price_text}")
if not self.平仓():
logger.warning("反手做空：平仓按钮未触发，放弃本次反手")
return False
time.sleep(1)
for _ in range(10):
if self.get_position_status() and self.start == 0:
@@ -717,12 +755,14 @@ class BitmartFuturesTransaction:
if self.start == 0:
signal = ('long', current_price)
elif self.start == -1:
signal = ('reverse_long', current_price)
reverse_trigger = self.open_avg_price if self.open_avg_price else None
signal = ('reverse_long', reverse_trigger)
elif raw == 2:
if self.start == 0:
signal = ('short', current_price)
elif self.start == 1:
signal = ('reverse_short', current_price)
reverse_trigger = self.open_avg_price if self.open_avg_price else None
signal = ('reverse_short', reverse_trigger)
# 5. 反手过滤：冷却时间 + 最小价差
if signal and signal[0].startswith('reverse_'):