feat: 首次提交，包含多因子状态机量化策略及 TG 机器人监听服务
This commit is contained in:
lzybetter
@@ -0,0 +1,210 @@
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import talib
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# 1. 指标引擎：未来所有新发掘的指标，全写在这里
|
||||
# =====================================================================
|
||||
class FeatureEngine:
|
||||
|
||||
def __init__(self, daily_df, min5_df):
|
||||
"""
|
||||
daily_df: 日线数据 DataFrame
|
||||
min5_df: 当天 5分钟 级别 K 线数据 DataFrame (需要包含 'close', 'volume')
|
||||
"""
|
||||
self.df = daily_df.copy()
|
||||
self.min5_df = min5_df.copy()
|
||||
self._extract_basic_arrays()
|
||||
|
||||
def _extract_basic_arrays(self):
|
||||
self.close = self.df["close"].astype(float).values
|
||||
self.high = self.df["high"].astype(float).values
|
||||
self.low = self.df["low"].astype(float).values
|
||||
self.amount = self.df["amount"].astype(float).values
|
||||
|
||||
def calculate_all_features(self):
|
||||
features = {}
|
||||
|
||||
# =====================================================================
|
||||
# 1. 🔄 【核心修改】：用 5分钟 K 线精确估算今日分时均价
|
||||
# =====================================================================
|
||||
if not self.min5_df.empty:
|
||||
# 计算每 5 分钟的成交金额（收盘价 * 成交量）
|
||||
# 注意：如果你的 5分钟接口直接自带 'amount'(成交额) 列，请直接用 self.min5_df['amount']
|
||||
m5_close = self.min5_df["close"].astype(float)
|
||||
m5_volume = self.min5_df["volume"].astype(float)
|
||||
|
||||
total_amount = (m5_close * m5_volume).sum()
|
||||
total_volume = m5_volume.sum()
|
||||
|
||||
if total_volume > 0:
|
||||
# 算出截止到当前（14:48）的 A 股全天分时均价
|
||||
vwap_today = total_amount / total_volume
|
||||
# 当前最新价格（最后一根 5分钟线的收盘价，最接近实时现价）
|
||||
current_price = m5_close.iloc[-1]
|
||||
|
||||
# 计算偏离度：(最新价 - 分时均价) / 分时均价
|
||||
features["min_bias"] = (current_price - vwap_today) / vwap_today
|
||||
else:
|
||||
features["min_bias"] = 0.0
|
||||
else:
|
||||
features["min_bias"] = 0.0
|
||||
|
||||
# =====================================================================
|
||||
# 2. 日线常规指标计算（保持你的经典布林带逻辑不变）
|
||||
# =====================================================================
|
||||
up, mid, low = talib.BBANDS(
|
||||
self.close, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0
|
||||
)
|
||||
self.df["bb_up"], self.df["bb_mid"], self.df["bb_low"] = up, mid, low
|
||||
|
||||
self.df["percent_b"] = (self.df["close"] - low) / (up - low)
|
||||
self.df["bandwidth"] = (up - low) / mid
|
||||
self.df["amount_avg_20d"] = (
|
||||
self.df["amount"].rolling(20).mean().shift(1)
|
||||
)
|
||||
self.df["price_max_60d"] = self.df["close"].rolling(60).max().shift(1)
|
||||
self.df["low_min_10d"] = self.df["low"].rolling(10).min().shift(1)
|
||||
|
||||
latest = self.df.iloc[-1]
|
||||
prev = self.df.iloc[-2]
|
||||
|
||||
# 如果 5分钟线数据拿到了，现价以 5分钟最新收盘价为准（更接近 14:48 真实盘面）
|
||||
# 如果没拿到，退化使用日线昨日收盘（做测试用）
|
||||
features["close"] = (
|
||||
current_price if not self.min5_df.empty else latest["close"]
|
||||
)
|
||||
|
||||
features["prev_close"] = prev["close"]
|
||||
features["amount"] = latest["amount"]
|
||||
features["amount_avg_20d"] = latest["amount_avg_20d"]
|
||||
features["ma5"] = latest["ma5"] if "ma5" in latest else latest["close"]
|
||||
features["ma10"] = latest["ma10"] if "ma10" in latest else latest["close"] # 👈 核心：补上这一行！
|
||||
features["ma20"] = latest["bb_mid"]
|
||||
features["ma60"] = latest["ma60"] if "ma60" in latest else latest["close"] # 👈 顺便把ma60也安全带上
|
||||
features["percent_b"] = latest["percent_b"]
|
||||
features["bandwidth"] = latest["bandwidth"]
|
||||
features["is_price_60d_max"] = (
|
||||
latest["close"] >= latest["price_max_60d"]
|
||||
)
|
||||
features["is_not_new_low_10d"] = latest["close"] > latest["low_min_10d"]
|
||||
|
||||
history_bw = self.df["bandwidth"].iloc[-250:]
|
||||
features["bw_quantile"] = (history_bw < latest["bandwidth"]).mean()
|
||||
|
||||
return features
|
||||
|
||||
# =====================================================================
|
||||
# 2. 状态规则书：这里只根据指标数据定义状态切换门槛
|
||||
# =====================================================================
|
||||
class StateRuleBook:
|
||||
|
||||
@staticmethod
|
||||
def evaluate_next_state(current_state, f):
|
||||
"""f 传入的是 FeatureEngine 计算出来的最新特征字典"""
|
||||
|
||||
# 计算主升浪基础多头条件
|
||||
is_ma_bull = (f["ma5"] > f["ma10"] > f["ma20"]) and (
|
||||
f["ma5"] > f["prev_ma5"]
|
||||
)
|
||||
|
||||
# 核心决策流：利用解耦后的字典 f 进行条件拆解
|
||||
# 【判断是否从主升浪跌破】
|
||||
if (
|
||||
current_state == "STATE_3_MAIN_WAVE"
|
||||
and f["close"] < f["ma10"]
|
||||
and f["prev_close"] > f["prev_ma10"]
|
||||
):
|
||||
return (
|
||||
"STATE_4_WAVE_END",
|
||||
"⚠️ 主升浪确认结束！清空做T仓，准备重新激活做T。",
|
||||
)
|
||||
|
||||
# 【判断是否爆发主升浪】
|
||||
if (
|
||||
is_ma_bull
|
||||
and f["is_price_60d_max"]
|
||||
and (f["amount"] > f["amount_avg_20d"] * 1.8)
|
||||
):
|
||||
return (
|
||||
"STATE_3_MAIN_WAVE",
|
||||
"🚀 主升浪开启 / 放量向上突破！做T脚本自动拉闸休眠，锁仓死拿！",
|
||||
)
|
||||
|
||||
# 【判断是否向下破位】
|
||||
if f["percent_b"] < 0.0: # 跌破布林下轨
|
||||
return "STATE_2_DOWN_BREAK", "❌ 向下破位！停止低吸做T，转为观望。"
|
||||
|
||||
# 【判断是否低量筑底】
|
||||
is_low_volume = f["amount"] < (f["amount_avg_20d"] * 0.5)
|
||||
is_ma_converge = abs(f["ma5"] - f["ma20"]) / f["ma20"] < 0.03
|
||||
if f["is_not_new_low_10d"] and is_low_volume and is_ma_converge:
|
||||
return (
|
||||
"STATE_5_BOTTOMING",
|
||||
"🌱 下跌结束，正在地量筑底。允许开始尝试轻仓做T。",
|
||||
)
|
||||
|
||||
# 【判断是否处于变盘前夜】
|
||||
if f["bw_quantile"] < 0.12:
|
||||
return (
|
||||
"STATE_1_OSCILLATION_SQUEEZE",
|
||||
"🎚️ 变盘前夜：带宽极度压缩。保持做T，但防范单边突破。",
|
||||
)
|
||||
|
||||
# 默认返回横盘震荡
|
||||
return (
|
||||
"STATE_1_OSCILLATION",
|
||||
"☕ 正常横盘震荡期。激活做T脚本，正常执行高抛低吸。",
|
||||
)
|
||||
|
||||
|
||||
# =====================================================================
|
||||
# 3. 策略状态机：调度核心
|
||||
# =====================================================================
|
||||
class QuantStateMachine:
|
||||
|
||||
def __init__(self, initial_state="STATE_1_OSCILLATION"):
|
||||
self.current_state = initial_state
|
||||
|
||||
def run_daily_diagnostic(self, daily_df, min_df=None):
|
||||
# 1. 扔给传感器计算指标
|
||||
engine = FeatureEngine(daily_df, min_df)
|
||||
features = engine.calculate_all_features()
|
||||
|
||||
# 2. 扔给规则书判定状态
|
||||
next_state, comment = StateRuleBook.evaluate_next_state(
|
||||
self.current_state, features
|
||||
)
|
||||
|
||||
# 3. 更新并固化状态
|
||||
self.current_state = next_state
|
||||
|
||||
# 4. 根据最终状态，分发当天的交易指令
|
||||
self._execute_trading_action(features, comment)
|
||||
|
||||
def _execute_trading_action(self, f, comment):
|
||||
print(f"\n[当前系统状态]: {self.current_state}")
|
||||
print(f"[状态诊断提示]: {comment}")
|
||||
|
||||
# 具体的交易动作分发
|
||||
if self.current_state in [
|
||||
"STATE_1_OSCILLATION",
|
||||
"STATE_1_OSCILLATION_SQUEEZE",
|
||||
]:
|
||||
# 只有在震荡期，才读取 %B 或分时执行做T
|
||||
if f["percent_b"] >= 1.0 or f["min_bias"] > 0.035:
|
||||
print(">>> 💰 【执行动作】：尾盘高抛，卖出 20% 网格仓。")
|
||||
elif f["percent_b"] <= 0.0 or f["min_bias"] < -0.035:
|
||||
print(">>> 🛒 【执行动作】：尾盘低吸，接回 20% 网格仓。")
|
||||
else:
|
||||
print(">>> ☕ 【执行动作】：未触及极端做T边界，长线持股观望。")
|
||||
|
||||
elif self.current_state == "STATE_3_MAIN_WAVE":
|
||||
print(">>> 🔒 【执行动作】：主升浪锁仓护航中，禁止任何人乱动做T筹码。")
|
||||
|
||||
elif self.current_state == "STATE_2_DOWN_BREAK":
|
||||
print(">>> 🛑 【执行动作】：市场向下破位，做T有被套风险，禁止低吸！")
|
||||
|
||||
elif self.current_state == "STATE_5_BOTTOMING":
|
||||
print(">>> 🔬 【执行动作】：地量筑底阶段，允许小仓位底部分批低吸。")
|
||||
Reference in New Issue
Block a user