feat: vertical 3-column timeline + MA regime bootstrap engine
- TimelineVertical: replace horizontal frise with Y=time vertical layout, 3 columns (Long/Medium/Short), auto-scroll to selected date, sub-columns for overlapping events, today/selected-date horizontal lines - ma_analyzer.py: detect MA50/MA200 crossovers + MA100 slope changes + MA20 direction swings on EUR/USD, Brent, Gold, S&P500, US10Y (5y history) with 5-bar confirmation, dedup, GPT-4o-mini enrichment, idempotent DB save - POST /api/timeline/bootstrap-ma endpoint to trigger analysis - Bootstrap MA button in Timeline page with loading state + result count Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
OpenSquared
@@ -209,3 +209,15 @@ def bootstrap_events() -> Dict[str, Any]:
|
||||
from services.timeline_service import bootstrap_events as _bootstrap
|
||||
count = _bootstrap()
|
||||
return {"seeded": count, "status": "ok" if count > 0 else "already_seeded"}
|
||||
|
||||
|
||||
@router.post("/bootstrap-ma")
|
||||
async def bootstrap_ma() -> Dict[str, Any]:
|
||||
"""Detect MA ruptures on 5 key underlyings and generate historical market events via GPT."""
|
||||
from services.ma_analyzer import bootstrap_ma_events
|
||||
try:
|
||||
result = await bootstrap_ma_events()
|
||||
return {**result, "status": "ok"}
|
||||
except Exception as e:
|
||||
logger.error(f"[Timeline] bootstrap-ma failed: {e}")
|
||||
raise HTTPException(500, str(e))
|
||||
|
||||
570
backend/services/ma_analyzer.py
Normal file
570
backend/services/ma_analyzer.py
Normal file
@@ -0,0 +1,570 @@
|
||||
"""
|
||||
MA Analyzer — Bootstrap historical market periods from price-based MA signals.
|
||||
|
||||
Fetches 5y daily prices for 5 key underlyings, detects MA ruptures (golden/death
|
||||
cross, MA100 slope changes, MA20 direction changes), enriches candidates with
|
||||
GPT-4o-mini, and persists them via save_market_event().
|
||||
"""
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import time
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import yfinance as yf
|
||||
|
||||
from services.database import count_market_events, save_market_event
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
UNDERLYINGS = [
|
||||
{"ticker": "EURUSD=X", "name": "EUR/USD", "category": "market", "assets": ["EUR/USD", "USD", "EUR"]},
|
||||
{"ticker": "BZ=F", "name": "Brent Crude", "category": "market", "assets": ["CL", "BZ", "Oil"]},
|
||||
{"ticker": "GC=F", "name": "Gold", "category": "market", "assets": ["GC", "Gold", "GLD"]},
|
||||
{"ticker": "^GSPC", "name": "S&P 500", "category": "market", "assets": ["SPX", "SPY", "QQQ"]},
|
||||
{"ticker": "^TNX", "name": "US 10Y Yield", "category": "market", "assets": ["TNX", "TLT", "Bonds"]},
|
||||
]
|
||||
|
||||
MA_PERIODS = [20, 50, 100, 200]
|
||||
CROSS_CONFIRM_DAYS = 5
|
||||
SLOPE_WINDOW = 10
|
||||
DEDUP_DAYS = 30
|
||||
MAX_CANDIDATES = 80
|
||||
GPT_BATCH_SIZE = 5
|
||||
|
||||
|
||||
def _fetch_prices() -> Dict[str, pd.Series]:
|
||||
"""Download 5y daily close prices for all underlyings. Returns ticker → pd.Series."""
|
||||
tickers = [u["ticker"] for u in UNDERLYINGS]
|
||||
prices: Dict[str, pd.Series] = {}
|
||||
|
||||
try:
|
||||
raw = yf.download(tickers, period="5y", interval="1d", progress=False, auto_adjust=True)
|
||||
if raw.empty:
|
||||
raise ValueError("Empty download result")
|
||||
close = raw["Close"] if isinstance(raw.columns, pd.MultiIndex) else raw
|
||||
for ticker in tickers:
|
||||
if ticker in close.columns:
|
||||
s = close[ticker].dropna()
|
||||
if not s.empty:
|
||||
prices[ticker] = s
|
||||
logger.info(f"[MA] {ticker}: {len(s)} bars")
|
||||
except Exception as e:
|
||||
logger.warning(f"[MA] Batch download failed ({e}), falling back to individual")
|
||||
for ticker in tickers:
|
||||
try:
|
||||
df = yf.download(ticker, period="5y", interval="1d", progress=False, auto_adjust=True)
|
||||
if df.empty:
|
||||
continue
|
||||
if isinstance(df.columns, pd.MultiIndex):
|
||||
df.columns = df.columns.get_level_values(0)
|
||||
s = df["Close"].dropna()
|
||||
if not s.empty:
|
||||
prices[ticker] = s
|
||||
logger.info(f"[MA] {ticker}: {len(s)} bars")
|
||||
except Exception as e2:
|
||||
logger.error(f"[MA] Failed to fetch {ticker}: {e2}")
|
||||
|
||||
return prices
|
||||
|
||||
|
||||
def _compute_mas(prices: pd.Series) -> pd.DataFrame:
|
||||
"""Return DataFrame with Close + MA20/50/100/200 columns."""
|
||||
df = prices.to_frame(name="Close")
|
||||
for p in MA_PERIODS:
|
||||
df[f"MA{p}"] = df["Close"].rolling(p).mean()
|
||||
return df
|
||||
|
||||
|
||||
def _date_str(ts) -> str:
|
||||
if hasattr(ts, "strftime"):
|
||||
return ts.strftime("%Y-%m-%d")
|
||||
return str(ts)[:10]
|
||||
|
||||
|
||||
def _price_move_pct(df: pd.DataFrame, start_idx: int, end_idx: int) -> float:
|
||||
"""Absolute % move of Close between two integer iloc positions."""
|
||||
try:
|
||||
p0 = float(df["Close"].iloc[start_idx])
|
||||
p1 = float(df["Close"].iloc[end_idx])
|
||||
if p0 == 0:
|
||||
return 0.0
|
||||
return abs((p1 - p0) / p0 * 100)
|
||||
except Exception:
|
||||
return 0.0
|
||||
|
||||
|
||||
def _detect_crossovers(df: pd.DataFrame, underlying: Dict[str, Any]) -> List[Dict[str, Any]]:
|
||||
"""Detect MA50/MA200 golden cross and death cross with 5-day confirmation."""
|
||||
candidates = []
|
||||
ma50 = df["MA50"]
|
||||
ma200 = df["MA200"]
|
||||
|
||||
valid = df.dropna(subset=["MA50", "MA200"])
|
||||
if len(valid) < CROSS_CONFIRM_DAYS + 2:
|
||||
return candidates
|
||||
|
||||
idx_list = valid.index.tolist()
|
||||
|
||||
i = 1
|
||||
while i < len(idx_list) - CROSS_CONFIRM_DAYS:
|
||||
prev_i = idx_list[i - 1]
|
||||
curr_i = idx_list[i]
|
||||
|
||||
prev_above = ma50[prev_i] > ma200[prev_i]
|
||||
curr_above = ma50[curr_i] > ma200[curr_i]
|
||||
|
||||
if prev_above == curr_above:
|
||||
i += 1
|
||||
continue
|
||||
|
||||
is_golden = curr_above # MA50 just crossed above MA200
|
||||
|
||||
# Confirm: next CROSS_CONFIRM_DAYS all maintain the new relationship
|
||||
confirm_ok = True
|
||||
for k in range(1, CROSS_CONFIRM_DAYS + 1):
|
||||
if i + k >= len(idx_list):
|
||||
confirm_ok = False
|
||||
break
|
||||
future_i = idx_list[i + k]
|
||||
if is_golden:
|
||||
if not (ma50[future_i] > ma200[future_i]):
|
||||
confirm_ok = False
|
||||
break
|
||||
else:
|
||||
if not (ma50[future_i] < ma200[future_i]):
|
||||
confirm_ok = False
|
||||
break
|
||||
|
||||
if confirm_ok:
|
||||
# start_date = date of the confirming bar (i + CROSS_CONFIRM_DAYS)
|
||||
confirmed_i = idx_list[i + CROSS_CONFIRM_DAYS]
|
||||
start_iloc = valid.index.get_loc(confirmed_i)
|
||||
|
||||
event_type = "golden_cross" if is_golden else "death_cross"
|
||||
name_prefix = "Golden Cross" if is_golden else "Death Cross"
|
||||
candidates.append({
|
||||
"_type": event_type,
|
||||
"_underlying": underlying,
|
||||
"_start_iloc": start_iloc,
|
||||
"start_date": _date_str(confirmed_i),
|
||||
"end_date": None,
|
||||
"level": "medium",
|
||||
"category": "market",
|
||||
"_move_magnitude": _price_move_pct(df, max(0, start_iloc - 20), start_iloc),
|
||||
"_name_hint": f"{underlying['name']} {name_prefix} MA50/MA200",
|
||||
})
|
||||
i += CROSS_CONFIRM_DAYS + 1
|
||||
else:
|
||||
i += 1
|
||||
|
||||
return candidates
|
||||
|
||||
|
||||
def _slope(series: pd.Series, window: int = SLOPE_WINDOW) -> pd.Series:
|
||||
"""Percentage slope over window: (val[t] - val[t-window]) / val[t-window] * 100."""
|
||||
return (series - series.shift(window)) / series.shift(window) * 100
|
||||
|
||||
|
||||
def _detect_ma100_slope_changes(df: pd.DataFrame, underlying: Dict[str, Any]) -> List[Dict[str, Any]]:
|
||||
"""Detect MA100 direction reversals (slope sign flips sustained over 5+ bars)."""
|
||||
candidates = []
|
||||
valid = df.dropna(subset=["MA100"])
|
||||
if len(valid) < SLOPE_WINDOW + 10:
|
||||
return candidates
|
||||
|
||||
slope = _slope(valid["MA100"], SLOPE_WINDOW)
|
||||
sign = np.sign(slope)
|
||||
|
||||
idx_list = valid.index.tolist()
|
||||
i = 1
|
||||
while i < len(idx_list) - 5:
|
||||
prev_sign = sign[idx_list[i - 1]]
|
||||
curr_sign = sign[idx_list[i]]
|
||||
|
||||
if prev_sign == 0 or curr_sign == 0 or prev_sign == curr_sign:
|
||||
i += 1
|
||||
continue
|
||||
|
||||
# Check 5-bar confirmation
|
||||
confirm_ok = all(
|
||||
sign[idx_list[i + k]] == curr_sign
|
||||
for k in range(1, 6)
|
||||
if i + k < len(idx_list)
|
||||
)
|
||||
if not confirm_ok:
|
||||
i += 1
|
||||
continue
|
||||
|
||||
start_idx = valid.index.get_loc(idx_list[i])
|
||||
|
||||
# Find end: next sign flip
|
||||
end_date = None
|
||||
duration_days = 0
|
||||
for j in range(i + 1, len(idx_list)):
|
||||
if sign[idx_list[j]] == -curr_sign:
|
||||
end_date = _date_str(idx_list[j])
|
||||
duration_days = (
|
||||
datetime.strptime(end_date, "%Y-%m-%d") -
|
||||
datetime.strptime(_date_str(idx_list[i]), "%Y-%m-%d")
|
||||
).days
|
||||
break
|
||||
|
||||
direction = "Bullish" if curr_sign > 0 else "Bearish"
|
||||
level = "long" if duration_days > 90 or end_date is None else "medium"
|
||||
|
||||
candidates.append({
|
||||
"_type": "ma100_slope_change",
|
||||
"_underlying": underlying,
|
||||
"_start_iloc": start_idx,
|
||||
"start_date": _date_str(idx_list[i]),
|
||||
"end_date": end_date,
|
||||
"level": level,
|
||||
"category": "market",
|
||||
"_move_magnitude": abs(float(slope[idx_list[i]])),
|
||||
"_name_hint": f"{underlying['name']} MA100 {direction} Trend",
|
||||
})
|
||||
|
||||
# Skip to next potential flip zone
|
||||
if end_date:
|
||||
while i < len(idx_list) and _date_str(idx_list[i]) < end_date:
|
||||
i += 1
|
||||
else:
|
||||
break
|
||||
|
||||
return candidates
|
||||
|
||||
|
||||
def _detect_ma20_direction_changes(df: pd.DataFrame, underlying: Dict[str, Any]) -> List[Dict[str, Any]]:
|
||||
"""Detect strong MA20 direction changes (> 2% in 10 days)."""
|
||||
candidates = []
|
||||
valid = df.dropna(subset=["MA20"])
|
||||
if len(valid) < SLOPE_WINDOW + 10:
|
||||
return candidates
|
||||
|
||||
slope = _slope(valid["MA20"], SLOPE_WINDOW)
|
||||
THRESHOLD = 2.0
|
||||
|
||||
idx_list = valid.index.tolist()
|
||||
last_added_date = None
|
||||
|
||||
for i in range(1, len(idx_list) - 5):
|
||||
prev_slope = float(slope[idx_list[i - 1]])
|
||||
curr_slope = float(slope[idx_list[i]])
|
||||
|
||||
if abs(curr_slope) < THRESHOLD:
|
||||
continue
|
||||
if np.sign(prev_slope) == np.sign(curr_slope):
|
||||
continue
|
||||
|
||||
start_date = _date_str(idx_list[i])
|
||||
|
||||
# Dedup: skip if too close to last added
|
||||
if last_added_date:
|
||||
gap = (
|
||||
datetime.strptime(start_date, "%Y-%m-%d") -
|
||||
datetime.strptime(last_added_date, "%Y-%m-%d")
|
||||
).days
|
||||
if gap < DEDUP_DAYS:
|
||||
continue
|
||||
|
||||
# Find end: slope returns to < 0.5 or reverses
|
||||
end_date = None
|
||||
duration_days = 0
|
||||
for j in range(i + 1, len(idx_list)):
|
||||
future_slope = float(slope[idx_list[j]])
|
||||
if abs(future_slope) < 0.5 or np.sign(future_slope) != np.sign(curr_slope):
|
||||
end_date = _date_str(idx_list[j])
|
||||
duration_days = (
|
||||
datetime.strptime(end_date, "%Y-%m-%d") -
|
||||
datetime.strptime(start_date, "%Y-%m-%d")
|
||||
).days
|
||||
break
|
||||
|
||||
direction = "Bullish" if curr_slope > 0 else "Bearish"
|
||||
level = "short" if (end_date and duration_days < 30) else "medium"
|
||||
|
||||
candidates.append({
|
||||
"_type": "ma20_direction_change",
|
||||
"_underlying": underlying,
|
||||
"_start_iloc": valid.index.get_loc(idx_list[i]),
|
||||
"start_date": start_date,
|
||||
"end_date": end_date,
|
||||
"level": level,
|
||||
"category": "market",
|
||||
"_move_magnitude": abs(curr_slope),
|
||||
"_name_hint": f"{underlying['name']} MA20 {direction} Swing",
|
||||
})
|
||||
last_added_date = start_date
|
||||
|
||||
return candidates
|
||||
|
||||
|
||||
def _dedup_candidates(candidates: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
|
||||
"""Remove near-duplicate events (same underlying + type, dates < 30 days apart).
|
||||
Keep the one with the largest _move_magnitude."""
|
||||
kept: List[Dict[str, Any]] = []
|
||||
for ev in candidates:
|
||||
ticker = ev["_underlying"]["ticker"]
|
||||
ev_type = ev["_type"]
|
||||
ev_start = ev["start_date"]
|
||||
|
||||
duplicate_of = None
|
||||
for k in kept:
|
||||
if k["_underlying"]["ticker"] != ticker:
|
||||
continue
|
||||
if k["_type"] != ev_type:
|
||||
continue
|
||||
gap = abs(
|
||||
(datetime.strptime(ev_start, "%Y-%m-%d") -
|
||||
datetime.strptime(k["start_date"], "%Y-%m-%d")).days
|
||||
)
|
||||
if gap < DEDUP_DAYS:
|
||||
duplicate_of = k
|
||||
break
|
||||
|
||||
if duplicate_of is None:
|
||||
kept.append(ev)
|
||||
elif ev.get("_move_magnitude", 0) > duplicate_of.get("_move_magnitude", 0):
|
||||
kept.remove(duplicate_of)
|
||||
kept.append(ev)
|
||||
|
||||
return kept
|
||||
|
||||
|
||||
def _assign_end_dates(candidates: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
|
||||
"""For events without an end_date, set it to the next same-type event on same underlying."""
|
||||
for i, ev in enumerate(candidates):
|
||||
if ev.get("end_date"):
|
||||
continue
|
||||
ticker = ev["_underlying"]["ticker"]
|
||||
ev_type = ev["_type"]
|
||||
ev_start = ev["start_date"]
|
||||
|
||||
# Find the next event of same type/underlying that comes after
|
||||
next_start = None
|
||||
for j, other in enumerate(candidates):
|
||||
if i == j:
|
||||
continue
|
||||
if other["_underlying"]["ticker"] != ticker:
|
||||
continue
|
||||
if other["_type"] != ev_type:
|
||||
continue
|
||||
if other["start_date"] > ev_start:
|
||||
if next_start is None or other["start_date"] < next_start:
|
||||
next_start = other["start_date"]
|
||||
|
||||
ev["end_date"] = next_start # None if it's the last (ongoing)
|
||||
|
||||
return candidates
|
||||
|
||||
|
||||
def _build_gpt_prompt(batch: List[Dict[str, Any]], df_map: Dict[str, pd.DataFrame]) -> str:
|
||||
items = []
|
||||
for i, ev in enumerate(batch):
|
||||
underlying = ev["_underlying"]
|
||||
ticker = underlying["ticker"]
|
||||
df = df_map.get(ticker)
|
||||
start_price = end_price = None
|
||||
if df is not None:
|
||||
try:
|
||||
start_idx = df.index.searchsorted(pd.Timestamp(ev["start_date"]))
|
||||
start_price = float(df["Close"].iloc[min(start_idx, len(df) - 1)])
|
||||
except Exception:
|
||||
pass
|
||||
if ev.get("end_date"):
|
||||
try:
|
||||
end_idx = df.index.searchsorted(pd.Timestamp(ev["end_date"]))
|
||||
end_price = float(df["Close"].iloc[min(end_idx, len(df) - 1)])
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
items.append({
|
||||
"index": i,
|
||||
"underlying": underlying["name"],
|
||||
"type": ev["_type"],
|
||||
"start_date": ev["start_date"],
|
||||
"end_date": ev.get("end_date"),
|
||||
"level": ev["level"],
|
||||
"name_hint": ev["_name_hint"],
|
||||
"price_start": round(start_price, 4) if start_price else None,
|
||||
"price_end": round(end_price, 4) if end_price else None,
|
||||
})
|
||||
|
||||
return f"""You are a senior macro analyst specializing in technical market regimes.
|
||||
Analyze these {len(batch)} market events detected via Moving Average signals.
|
||||
For each, return a JSON object with:
|
||||
- index: (same as input)
|
||||
- name: concise English event name (ex: "S&P 500 Golden Cross Bull Trend")
|
||||
- description: 2-3 sentences explaining market context and significance
|
||||
- market_impact: 1 sentence on trading/volatility impact
|
||||
- affected_assets: list of 3-6 ticker symbols most affected
|
||||
- impact_score: float 0.0-1.0 (0=minor technical, 1=major structural)
|
||||
|
||||
Events to analyze:
|
||||
{json.dumps(items, indent=2)}
|
||||
|
||||
Return a JSON array of {len(batch)} objects, one per input event, in the same order.
|
||||
Array only, no wrapping key."""
|
||||
|
||||
|
||||
def _enrich_with_gpt(candidates: List[Dict[str, Any]], df_map: Dict[str, pd.DataFrame]) -> List[Dict[str, Any]]:
|
||||
"""Call GPT-4o-mini in batches of GPT_BATCH_SIZE to enrich candidates."""
|
||||
api_key = os.environ.get("OPENAI_API_KEY", "")
|
||||
if not api_key:
|
||||
logger.warning("[MA] No OPENAI_API_KEY — skipping GPT enrichment")
|
||||
for ev in candidates:
|
||||
ev["name"] = ev["_name_hint"]
|
||||
ev["description"] = f"MA signal detected on {ev['_underlying']['name']}."
|
||||
ev["market_impact"] = "Technical price action signal."
|
||||
ev["affected_assets"] = ev["_underlying"]["assets"]
|
||||
ev["impact_score"] = 0.4
|
||||
return candidates
|
||||
|
||||
from openai import OpenAI
|
||||
client = OpenAI(api_key=api_key)
|
||||
|
||||
for batch_start in range(0, len(candidates), GPT_BATCH_SIZE):
|
||||
batch = candidates[batch_start: batch_start + GPT_BATCH_SIZE]
|
||||
try:
|
||||
prompt = _build_gpt_prompt(batch, df_map)
|
||||
response = client.chat.completions.create(
|
||||
model="gpt-4o-mini",
|
||||
messages=[{"role": "user", "content": prompt}],
|
||||
temperature=0.3,
|
||||
max_tokens=1200,
|
||||
)
|
||||
raw = response.choices[0].message.content.strip()
|
||||
# Strip markdown code block if present
|
||||
if raw.startswith("```"):
|
||||
raw = raw.split("```")[1]
|
||||
if raw.startswith("json"):
|
||||
raw = raw[4:]
|
||||
results = json.loads(raw)
|
||||
for item in results:
|
||||
idx = item.get("index")
|
||||
if idx is not None and 0 <= idx < len(batch):
|
||||
ev = batch[idx]
|
||||
ev["name"] = item.get("name", ev["_name_hint"])
|
||||
ev["description"] = item.get("description", "")
|
||||
ev["market_impact"] = item.get("market_impact", "")
|
||||
ev["affected_assets"] = item.get("affected_assets", ev["_underlying"]["assets"])
|
||||
ev["impact_score"] = float(item.get("impact_score", 0.4))
|
||||
except Exception as e:
|
||||
logger.error(f"[MA] GPT batch {batch_start // GPT_BATCH_SIZE + 1} failed: {e}")
|
||||
for ev in batch:
|
||||
if "name" not in ev:
|
||||
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
|
||||
|
||||
time.sleep(1)
|
||||
|
||||
# Ensure all events have required fields
|
||||
for ev in candidates:
|
||||
if "name" not in ev:
|
||||
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": [],
|
||||
}
|
||||
|
||||
|
||||
async def bootstrap_ma_events() -> Dict[str, Any]:
|
||||
"""
|
||||
Main entrypoint. Detects MA ruptures, enriches with GPT, saves to DB.
|
||||
Idempotent: skips if DB already has > 100 events.
|
||||
Returns {"detected": N, "saved": N, "skipped": N}.
|
||||
"""
|
||||
existing = count_market_events()
|
||||
if existing > 100:
|
||||
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}
|
||||
Reference in New Issue
Block a user