573 lines
21 KiB
Python
573 lines
21 KiB
Python
"""
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MA Analyzer — Bootstrap historical market periods from price-based MA signals.
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Fetches 5y daily prices for 5 key underlyings, detects MA ruptures (golden/death
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cross, MA100 slope changes, MA20 direction changes), enriches candidates with
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GPT-4o-mini, and persists them via save_market_event().
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"""
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import json
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import logging
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import os
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import time
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from datetime import datetime, timedelta
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from typing import Any, Dict, List, Optional, Tuple
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import numpy as np
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import pandas as pd
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import yfinance as yf
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from services.database import count_market_events, save_market_event
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logger = logging.getLogger(__name__)
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UNDERLYINGS = [
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{"ticker": "EURUSD=X", "name": "EUR/USD", "category": "market", "assets": ["EUR/USD", "USD", "EUR"]},
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{"ticker": "BZ=F", "name": "Brent Crude", "category": "market", "assets": ["CL", "BZ", "Oil"]},
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{"ticker": "GC=F", "name": "Gold", "category": "market", "assets": ["GC", "Gold", "GLD"]},
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{"ticker": "^GSPC", "name": "S&P 500", "category": "market", "assets": ["SPX", "SPY", "QQQ"]},
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{"ticker": "^TNX", "name": "US 10Y Yield", "category": "market", "assets": ["TNX", "TLT", "Bonds"]},
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]
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MA_PERIODS = [20, 50, 100, 200]
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CROSS_CONFIRM_DAYS = 5
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SLOPE_WINDOW = 10
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DEDUP_DAYS = 30
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MAX_CANDIDATES = 80
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GPT_BATCH_SIZE = 5
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def _fetch_prices() -> Dict[str, pd.Series]:
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"""Download 5y daily close prices for all underlyings. Returns ticker → pd.Series."""
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tickers = [u["ticker"] for u in UNDERLYINGS]
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prices: Dict[str, pd.Series] = {}
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try:
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raw = yf.download(tickers, period="5y", interval="1d", progress=False, auto_adjust=True)
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if raw.empty:
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raise ValueError("Empty download result")
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close = raw["Close"] if isinstance(raw.columns, pd.MultiIndex) else raw
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for ticker in tickers:
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if ticker in close.columns:
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s = close[ticker].dropna()
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if not s.empty:
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prices[ticker] = s
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logger.info(f"[MA] {ticker}: {len(s)} bars")
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except Exception as e:
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logger.warning(f"[MA] Batch download failed ({e}), falling back to individual")
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for ticker in tickers:
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try:
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df = yf.download(ticker, period="5y", interval="1d", progress=False, auto_adjust=True)
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if df.empty:
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continue
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if isinstance(df.columns, pd.MultiIndex):
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df.columns = df.columns.get_level_values(0)
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s = df["Close"].dropna()
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if not s.empty:
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prices[ticker] = s
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logger.info(f"[MA] {ticker}: {len(s)} bars")
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except Exception as e2:
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logger.error(f"[MA] Failed to fetch {ticker}: {e2}")
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return prices
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def _compute_mas(prices: pd.Series) -> pd.DataFrame:
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"""Return DataFrame with Close + MA20/50/100/200 columns."""
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df = prices.to_frame(name="Close")
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for p in MA_PERIODS:
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df[f"MA{p}"] = df["Close"].rolling(p).mean()
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return df
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def _date_str(ts) -> str:
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if hasattr(ts, "strftime"):
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return ts.strftime("%Y-%m-%d")
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return str(ts)[:10]
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def _price_move_pct(df: pd.DataFrame, start_idx: int, end_idx: int) -> float:
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"""Absolute % move of Close between two integer iloc positions."""
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try:
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p0 = float(df["Close"].iloc[start_idx])
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p1 = float(df["Close"].iloc[end_idx])
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if p0 == 0:
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return 0.0
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return abs((p1 - p0) / p0 * 100)
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except Exception:
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return 0.0
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def _detect_crossovers(df: pd.DataFrame, underlying: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""Detect MA50/MA200 golden cross and death cross with 5-day confirmation."""
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candidates = []
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ma50 = df["MA50"]
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ma200 = df["MA200"]
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valid = df.dropna(subset=["MA50", "MA200"])
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if len(valid) < CROSS_CONFIRM_DAYS + 2:
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return candidates
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idx_list = valid.index.tolist()
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i = 1
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while i < len(idx_list) - CROSS_CONFIRM_DAYS:
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prev_i = idx_list[i - 1]
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curr_i = idx_list[i]
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prev_above = ma50[prev_i] > ma200[prev_i]
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curr_above = ma50[curr_i] > ma200[curr_i]
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if prev_above == curr_above:
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i += 1
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continue
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is_golden = curr_above # MA50 just crossed above MA200
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# Confirm: next CROSS_CONFIRM_DAYS all maintain the new relationship
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confirm_ok = True
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for k in range(1, CROSS_CONFIRM_DAYS + 1):
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if i + k >= len(idx_list):
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confirm_ok = False
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break
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future_i = idx_list[i + k]
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if is_golden:
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if not (ma50[future_i] > ma200[future_i]):
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confirm_ok = False
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break
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else:
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if not (ma50[future_i] < ma200[future_i]):
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confirm_ok = False
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break
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if confirm_ok:
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# start_date = date of the confirming bar (i + CROSS_CONFIRM_DAYS)
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confirmed_i = idx_list[i + CROSS_CONFIRM_DAYS]
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start_iloc = valid.index.get_loc(confirmed_i)
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event_type = "golden_cross" if is_golden else "death_cross"
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name_prefix = "Golden Cross" if is_golden else "Death Cross"
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candidates.append({
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"_type": event_type,
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"_underlying": underlying,
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"_start_iloc": start_iloc,
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"start_date": _date_str(confirmed_i),
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"end_date": None,
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"level": "medium",
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"category": "market",
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"_move_magnitude": _price_move_pct(df, max(0, start_iloc - 20), start_iloc),
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"_name_hint": f"{underlying['name']} {name_prefix} MA50/MA200",
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})
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i += CROSS_CONFIRM_DAYS + 1
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else:
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i += 1
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return candidates
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def _slope(series: pd.Series, window: int = SLOPE_WINDOW) -> pd.Series:
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"""Percentage slope over window: (val[t] - val[t-window]) / val[t-window] * 100."""
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return (series - series.shift(window)) / series.shift(window) * 100
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def _detect_ma100_slope_changes(df: pd.DataFrame, underlying: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""Detect MA100 direction reversals (slope sign flips sustained over 5+ bars)."""
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candidates = []
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valid = df.dropna(subset=["MA100"])
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if len(valid) < SLOPE_WINDOW + 10:
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return candidates
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slope = _slope(valid["MA100"], SLOPE_WINDOW)
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sign = np.sign(slope)
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idx_list = valid.index.tolist()
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i = 1
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while i < len(idx_list) - 5:
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prev_sign = sign[idx_list[i - 1]]
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curr_sign = sign[idx_list[i]]
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if prev_sign == 0 or curr_sign == 0 or prev_sign == curr_sign:
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i += 1
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continue
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# Check 5-bar confirmation
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confirm_ok = all(
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sign[idx_list[i + k]] == curr_sign
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for k in range(1, 6)
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if i + k < len(idx_list)
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)
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if not confirm_ok:
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i += 1
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continue
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start_idx = valid.index.get_loc(idx_list[i])
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# Find end: next sign flip
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end_date = None
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duration_days = 0
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for j in range(i + 1, len(idx_list)):
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if sign[idx_list[j]] == -curr_sign:
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end_date = _date_str(idx_list[j])
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duration_days = (
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datetime.strptime(end_date, "%Y-%m-%d") -
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datetime.strptime(_date_str(idx_list[i]), "%Y-%m-%d")
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).days
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break
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direction = "Bullish" if curr_sign > 0 else "Bearish"
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level = "long" if duration_days > 90 or end_date is None else "medium"
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candidates.append({
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"_type": "ma100_slope_change",
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"_underlying": underlying,
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"_start_iloc": start_idx,
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"start_date": _date_str(idx_list[i]),
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"end_date": end_date,
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"level": level,
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"category": "market",
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"_move_magnitude": abs(float(slope[idx_list[i]])),
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"_name_hint": f"{underlying['name']} MA100 {direction} Trend",
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})
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# Skip to next potential flip zone
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if end_date:
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while i < len(idx_list) and _date_str(idx_list[i]) < end_date:
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i += 1
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else:
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break
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return candidates
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def _detect_ma20_direction_changes(df: pd.DataFrame, underlying: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""Detect strong MA20 direction changes (> 2% in 10 days)."""
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candidates = []
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valid = df.dropna(subset=["MA20"])
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if len(valid) < SLOPE_WINDOW + 10:
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return candidates
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slope = _slope(valid["MA20"], SLOPE_WINDOW)
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THRESHOLD = 2.0
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idx_list = valid.index.tolist()
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last_added_date = None
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for i in range(1, len(idx_list) - 5):
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prev_slope = float(slope[idx_list[i - 1]])
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curr_slope = float(slope[idx_list[i]])
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if abs(curr_slope) < THRESHOLD:
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continue
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if np.sign(prev_slope) == np.sign(curr_slope):
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continue
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start_date = _date_str(idx_list[i])
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# Dedup: skip if too close to last added
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if last_added_date:
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gap = (
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datetime.strptime(start_date, "%Y-%m-%d") -
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datetime.strptime(last_added_date, "%Y-%m-%d")
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).days
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if gap < DEDUP_DAYS:
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continue
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# Find end: slope returns to < 0.5 or reverses
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end_date = None
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duration_days = 0
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for j in range(i + 1, len(idx_list)):
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future_slope = float(slope[idx_list[j]])
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if abs(future_slope) < 0.5 or np.sign(future_slope) != np.sign(curr_slope):
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end_date = _date_str(idx_list[j])
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duration_days = (
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datetime.strptime(end_date, "%Y-%m-%d") -
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datetime.strptime(start_date, "%Y-%m-%d")
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).days
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break
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direction = "Bullish" if curr_slope > 0 else "Bearish"
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level = "short" if (end_date and duration_days < 30) else "medium"
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candidates.append({
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"_type": "ma20_direction_change",
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"_underlying": underlying,
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"_start_iloc": valid.index.get_loc(idx_list[i]),
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"start_date": start_date,
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"end_date": end_date,
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"level": level,
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"category": "market",
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"_move_magnitude": abs(curr_slope),
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"_name_hint": f"{underlying['name']} MA20 {direction} Swing",
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})
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last_added_date = start_date
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return candidates
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def _dedup_candidates(candidates: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""Remove near-duplicate events (same underlying + type, dates < 30 days apart).
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Keep the one with the largest _move_magnitude."""
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kept: List[Dict[str, Any]] = []
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for ev in candidates:
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ticker = ev["_underlying"]["ticker"]
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ev_type = ev["_type"]
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ev_start = ev["start_date"]
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duplicate_of = None
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for k in kept:
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if k["_underlying"]["ticker"] != ticker:
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continue
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if k["_type"] != ev_type:
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continue
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gap = abs(
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(datetime.strptime(ev_start, "%Y-%m-%d") -
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datetime.strptime(k["start_date"], "%Y-%m-%d")).days
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)
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if gap < DEDUP_DAYS:
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duplicate_of = k
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break
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if duplicate_of is None:
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kept.append(ev)
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elif ev.get("_move_magnitude", 0) > duplicate_of.get("_move_magnitude", 0):
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kept.remove(duplicate_of)
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kept.append(ev)
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return kept
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def _assign_end_dates(candidates: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""For events without an end_date, set it to the next same-type event on same underlying."""
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for i, ev in enumerate(candidates):
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if ev.get("end_date"):
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continue
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ticker = ev["_underlying"]["ticker"]
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ev_type = ev["_type"]
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ev_start = ev["start_date"]
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# Find the next event of same type/underlying that comes after
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next_start = None
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for j, other in enumerate(candidates):
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if i == j:
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continue
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if other["_underlying"]["ticker"] != ticker:
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continue
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if other["_type"] != ev_type:
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continue
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if other["start_date"] > ev_start:
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if next_start is None or other["start_date"] < next_start:
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next_start = other["start_date"]
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ev["end_date"] = next_start # None if it's the last (ongoing)
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return candidates
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def _build_gpt_prompt(batch: List[Dict[str, Any]], df_map: Dict[str, pd.DataFrame]) -> str:
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items = []
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for i, ev in enumerate(batch):
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underlying = ev["_underlying"]
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ticker = underlying["ticker"]
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df = df_map.get(ticker)
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start_price = end_price = None
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if df is not None:
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try:
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start_idx = df.index.searchsorted(pd.Timestamp(ev["start_date"]))
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start_price = float(df["Close"].iloc[min(start_idx, len(df) - 1)])
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except Exception:
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pass
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if ev.get("end_date"):
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try:
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end_idx = df.index.searchsorted(pd.Timestamp(ev["end_date"]))
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end_price = float(df["Close"].iloc[min(end_idx, len(df) - 1)])
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except Exception:
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pass
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items.append({
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"index": i,
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"underlying": underlying["name"],
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"type": ev["_type"],
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"start_date": ev["start_date"],
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"end_date": ev.get("end_date"),
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"level": ev["level"],
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"name_hint": ev["_name_hint"],
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"price_start": round(start_price, 4) if start_price else None,
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"price_end": round(end_price, 4) if end_price else None,
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})
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return f"""You are a senior macro analyst specializing in technical market regimes.
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Analyze these {len(batch)} market events detected via Moving Average signals.
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For each, return a JSON object with:
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- index: (same as input)
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- name: concise English event name (ex: "S&P 500 Golden Cross Bull Trend")
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- description: 2-3 sentences explaining market context and significance
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- market_impact: 1 sentence on trading/volatility impact
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- affected_assets: list of 3-6 ticker symbols most affected
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- impact_score: float 0.0-1.0 (0=minor technical, 1=major structural)
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Events to analyze:
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{json.dumps(items, indent=2)}
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Return a JSON array of {len(batch)} objects, one per input event, in the same order.
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Array only, no wrapping key."""
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def _enrich_with_gpt(candidates: List[Dict[str, Any]], df_map: Dict[str, pd.DataFrame]) -> List[Dict[str, Any]]:
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"""Call GPT-4o-mini in batches of GPT_BATCH_SIZE to enrich candidates."""
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api_key = os.environ.get("OPENAI_API_KEY", "")
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if not api_key:
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logger.warning("[MA] No OPENAI_API_KEY — skipping GPT enrichment")
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for ev in candidates:
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ev["name"] = ev["_name_hint"]
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ev["description"] = f"MA signal detected on {ev['_underlying']['name']}."
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ev["market_impact"] = "Technical price action signal."
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ev["affected_assets"] = ev["_underlying"]["assets"]
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ev["impact_score"] = 0.4
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return candidates
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from openai import OpenAI
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client = OpenAI(api_key=api_key)
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for batch_start in range(0, len(candidates), GPT_BATCH_SIZE):
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batch = candidates[batch_start: batch_start + GPT_BATCH_SIZE]
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try:
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prompt = _build_gpt_prompt(batch, df_map)
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response = client.chat.completions.create(
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model="gpt-4o-mini",
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messages=[{"role": "user", "content": prompt}],
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temperature=0.3,
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max_tokens=1200,
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)
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raw = response.choices[0].message.content.strip()
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# Strip markdown code block if present
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if raw.startswith("```"):
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raw = raw.split("```")[1]
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if raw.startswith("json"):
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raw = raw[4:]
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results = json.loads(raw)
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for item in results:
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idx = item.get("index")
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if idx is not None and 0 <= idx < len(batch):
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ev = batch[idx]
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ev["name"] = item.get("name", ev["_name_hint"])
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ev["description"] = item.get("description", "")
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ev["market_impact"] = item.get("market_impact", "")
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ev["affected_assets"] = item.get("affected_assets", ev["_underlying"]["assets"])
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ev["impact_score"] = float(item.get("impact_score", 0.4))
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except Exception as e:
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logger.error(f"[MA] GPT batch {batch_start // GPT_BATCH_SIZE + 1} failed: {e}")
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for ev in batch:
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if "name" not in ev:
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ev["name"] = ev["_name_hint"]
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ev["description"] = f"MA signal on {ev['_underlying']['name']}."
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ev["market_impact"] = "Technical signal."
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ev["affected_assets"] = ev["_underlying"]["assets"]
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ev["impact_score"] = 0.4
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time.sleep(1)
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# Ensure all events have required fields
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for ev in candidates:
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if "name" not in ev:
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ev["name"] = ev["_name_hint"]
|
|
ev["description"] = f"MA signal on {ev['_underlying']['name']}."
|
|
ev["market_impact"] = "Technical signal."
|
|
ev["affected_assets"] = ev["_underlying"]["assets"]
|
|
ev["impact_score"] = 0.4
|
|
|
|
return candidates
|
|
|
|
|
|
def _to_event_dict(ev: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Convert internal candidate to the format expected by save_market_event."""
|
|
return {
|
|
"name": ev["name"],
|
|
"start_date": ev["start_date"],
|
|
"end_date": ev.get("end_date"),
|
|
"level": ev["level"],
|
|
"category": ev.get("category", "market"),
|
|
"description": ev.get("description", ""),
|
|
"market_impact": ev.get("market_impact", ""),
|
|
"affected_assets": ev.get("affected_assets", []),
|
|
"impact_score": ev.get("impact_score", 0.4),
|
|
"absorption_pct": None,
|
|
"relevant_indicators": [],
|
|
"origin": "bootstrap_ma",
|
|
}
|
|
|
|
|
|
async def bootstrap_ma_events(force: bool = False) -> Dict[str, Any]:
|
|
"""
|
|
Main entrypoint. Detects MA ruptures, enriches with GPT, saves to DB.
|
|
Idempotent by default: skips if DB already has > 100 events. Pass force=True
|
|
to bypass this guard (same convention as bootstrap_macro_events/bootstrap_eco_events).
|
|
Returns {"detected": N, "saved": N, "skipped": N}.
|
|
"""
|
|
existing = count_market_events()
|
|
if existing > 100 and not force:
|
|
logger.info(f"[MA] DB already has {existing} events — skipping bootstrap")
|
|
return {"detected": 0, "saved": 0, "skipped": existing}
|
|
|
|
logger.info("[MA] Fetching price data...")
|
|
prices = _fetch_prices()
|
|
if not prices:
|
|
logger.error("[MA] No price data fetched — aborting")
|
|
return {"detected": 0, "saved": 0, "skipped": 0}
|
|
|
|
df_map: Dict[str, pd.DataFrame] = {}
|
|
all_candidates: List[Dict[str, Any]] = []
|
|
|
|
for underlying in UNDERLYINGS:
|
|
ticker = underlying["ticker"]
|
|
if ticker not in prices:
|
|
logger.warning(f"[MA] No data for {ticker}, skipping")
|
|
continue
|
|
|
|
try:
|
|
df = _compute_mas(prices[ticker])
|
|
df_map[ticker] = df
|
|
|
|
cross_evs = _detect_crossovers(df, underlying)
|
|
slope_evs = _detect_ma100_slope_changes(df, underlying)
|
|
swing_evs = _detect_ma20_direction_changes(df, underlying)
|
|
|
|
logger.info(
|
|
f"[MA] {underlying['name']}: crossovers={len(cross_evs)}, "
|
|
f"slope={len(slope_evs)}, swing={len(swing_evs)}"
|
|
)
|
|
all_candidates.extend(cross_evs + slope_evs + swing_evs)
|
|
except Exception as e:
|
|
logger.error(f"[MA] Detection failed for {underlying['name']}: {e}")
|
|
|
|
logger.info(f"[MA] Raw candidates: {len(all_candidates)}")
|
|
|
|
# Dedup + assign end dates
|
|
candidates = _dedup_candidates(all_candidates)
|
|
candidates = _assign_end_dates(candidates)
|
|
|
|
# Sort by start_date ascending for GPT context coherence
|
|
candidates.sort(key=lambda x: x["start_date"])
|
|
|
|
# Cap at MAX_CANDIDATES (keep most significant by _move_magnitude)
|
|
if len(candidates) > MAX_CANDIDATES:
|
|
candidates.sort(key=lambda x: x.get("_move_magnitude", 0), reverse=True)
|
|
candidates = candidates[:MAX_CANDIDATES]
|
|
candidates.sort(key=lambda x: x["start_date"])
|
|
|
|
detected = len(candidates)
|
|
logger.info(f"[MA] After dedup/cap: {detected} candidates — enriching with GPT...")
|
|
|
|
candidates = _enrich_with_gpt(candidates, df_map)
|
|
|
|
saved = 0
|
|
skipped_save = 0
|
|
for ev in candidates:
|
|
try:
|
|
save_market_event(_to_event_dict(ev))
|
|
saved += 1
|
|
except Exception as e:
|
|
logger.error(f"[MA] Failed to save '{ev.get('name', '?')}': {e}")
|
|
skipped_save += 1
|
|
|
|
logger.info(f"[MA] Bootstrap complete: detected={detected} saved={saved} skipped={skipped_save}")
|
|
return {"detected": detected, "saved": saved, "skipped": skipped_save}
|