refactor: update worker commands and add new scripts for API rebuilding and queue feeding
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ImBenji
@@ -0,0 +1,278 @@
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import requests
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import json
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import yfinance as yf
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import pandas as pd
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from datetime import datetime, timedelta
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BASE_URL = "https://duriin.imbenji.net"
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USERNAME = "admin"
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PASSWORD = "changeme"
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PRIVATE_KEYWORDS = {
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"OPENAI", "ANTHROPIC", "SPACEX", "BYTEDANCE", "HUAWEI", "DEEPSEEK",
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"MISTRAL", "COHERE", "GROQ", "INFLECTION", "STABILITY", "SCALEAI",
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"MCKINSEY", "DELOITTE", "BLUEORIGIN", "XAI", "HF", "RF", "DJI",
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"CBRS", "PRIVATE", "ORIGIN"
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}
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# tickers that need an exchange suffix appended
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EXCHANGE_MAP = {
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"005930": "005930.KS",
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"000660": "000660.KS",
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"002594": "002594.SZ",
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"688981": "688981.SS",
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"1810": "1810.HK",
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"0700": "0700.HK",
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"9984": "9984.T",
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"AIR": "AIR.PA",
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"DTE": "DTE.DE",
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"HSBA": "HSBA.L",
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"RHM": "RHM.DE",
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"VOW": "VOW.DE",
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}
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SQL_QUERY = """
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SELECT
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ep.id,
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ep.event_date,
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ep.direction,
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ep.magnitude,
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ep.timeframe,
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tc.name,
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tc.ticker
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FROM event_predictions ep
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JOIN tracked_companies tc ON ep.company_id = tc.id
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WHERE ep.event_date >= '2020-01-01'
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AND ep.event_date <= '2026-01-01'
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AND ep.direction IN ('positive', 'negative')
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ORDER BY ep.event_date DESC
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LIMIT 1000
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"""
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def fetch_predictions():
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resp = requests.post(
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f"{BASE_URL}/admin/api/sql",
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json={"sql": SQL_QUERY, "database": "intelligence"},
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auth=(USERNAME, PASSWORD),
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timeout=30
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)
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resp.raise_for_status()
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data = resp.json()
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result = data["results"][0]
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if "error" in result:
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raise RuntimeError(f"SQL error: {result['error']}")
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return result.get("rows", [])
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def resolve_ticker(raw):
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t = raw.strip()
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# already has a dot suffix (e.g. AIR.PA, 005930.KS)
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if "." in t:
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return t
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# explicit overrides
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if t in EXCHANGE_MAP:
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return EXCHANGE_MAP[t]
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return t
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def is_private(ticker, name):
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t = ticker.upper()
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n = (name or "").upper()
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for kw in PRIVATE_KEYWORDS:
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if kw in t or kw in n:
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return True
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# looks like a private-company placeholder (long all-caps word, no digits)
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if len(t) > 6 and t.isalpha():
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return True
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return False
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price_cache = {}
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def get_price(ticker, date_str):
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key = (ticker, date_str)
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if key in price_cache:
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return price_cache[key]
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try:
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dt = datetime.strptime(date_str, "%Y-%m-%d")
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start = (dt - timedelta(days=7)).strftime("%Y-%m-%d")
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end = (dt + timedelta(days=7)).strftime("%Y-%m-%d")
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hist = yf.Ticker(ticker).history(start=start, end=end, auto_adjust=True)
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if hist.empty:
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price_cache[key] = None
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return None
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hist.index = hist.index.tz_localize(None) if hist.index.tzinfo else hist.index
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target = pd.Timestamp(dt)
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after = hist[hist.index >= target]
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if after.empty:
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after = hist
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price = float(after["Close"].iloc[0])
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price_cache[key] = price
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return price
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except Exception:
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price_cache[key] = None
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return None
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def add_trading_days(date_str, n):
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dt = datetime.strptime(date_str, "%Y-%m-%d")
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count = 0
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while count < n:
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dt += timedelta(days=1)
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if dt.weekday() < 5:
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count += 1
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return dt.strftime("%Y-%m-%d")
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def main():
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print("Fetching predictions from remote...")
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raw_preds = fetch_predictions()
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print(f"Got {len(raw_preds)} predictions from DB")
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# filter private companies
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preds = []
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for p in raw_preds:
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if is_private(p["ticker"], p.get("name", "")):
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continue
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preds.append(p)
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print(f"After filtering private companies: {len(preds)}\n")
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results = []
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skipped = 0
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for i, p in enumerate(preds):
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if i > 0 and i % 50 == 0:
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print(f" Progress: {i}/{len(preds)} — evaluated: {len(results)}, skipped: {skipped}")
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raw_ticker = p["ticker"]
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ticker = resolve_ticker(raw_ticker)
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event_date = p["event_date"][:10]
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direction = p["direction"]
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date_5d = add_trading_days(event_date, 5)
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date_10d = add_trading_days(event_date, 10)
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date_20d = add_trading_days(event_date, 20)
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price_0 = get_price(ticker, event_date)
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if price_0 is None:
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skipped += 1
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continue
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price_5 = get_price(ticker, date_5d)
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price_10 = get_price(ticker, date_10d)
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price_20 = get_price(ticker, date_20d)
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def ret(px):
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if px is None:
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return None
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return (px - price_0) / price_0 * 100
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r5 = ret(price_5)
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r10 = ret(price_10)
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r20 = ret(price_20)
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def correct(r):
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if r is None:
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return None
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return (r > 0) if direction == "positive" else (r < 0)
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results.append({
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"id": p["id"],
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"ticker": ticker,
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"name": p["name"],
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"event_date": event_date,
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"direction": direction,
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"magnitude": p.get("magnitude", ""),
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"timeframe": p.get("timeframe", ""),
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"price_0": round(price_0, 4),
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"price_5d": round(price_5, 4) if price_5 else None,
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"price_10d": round(price_10, 4) if price_10 else None,
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"price_20d": round(price_20, 4) if price_20 else None,
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"5d_return": round(r5, 4) if r5 is not None else None,
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"10d_return": round(r10, 4) if r10 is not None else None,
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"20d_return": round(r20, 4) if r20 is not None else None,
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"correct_5d": correct(r5),
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"correct_10d": correct(r10),
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"correct_20d": correct(r20),
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})
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df = pd.DataFrame(results)
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print(f"\n{'='*62}")
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print("BACKTEST RESULTS — ALL TRACKED COMPANIES")
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print(f"{'='*62}")
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print(f"Total predictions fetched: {len(raw_preds)}")
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print(f"After private-company filter: {len(preds)}")
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print(f"Skipped (no price data): {skipped}")
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print(f"Evaluated: {len(df)}")
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print(f"Random baseline: 50.0%")
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print()
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def acc(col):
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sub = df[df[col].notna()]
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if len(sub) == 0:
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return 0.0, 0
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return sub[col].mean() * 100, len(sub)
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a5, n5 = acc("correct_5d")
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a10, n10 = acc("correct_10d")
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a20, n20 = acc("correct_20d")
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print("OVERALL DIRECTIONAL ACCURACY")
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print(f" 5-day: {a5:.1f}% (n={n5})")
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print(f" 10-day: {a10:.1f}% (n={n10})")
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print(f" 20-day: {a20:.1f}% (n={n20})")
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print()
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print("BY MAGNITUDE (10-day accuracy)")
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for mag in ["low", "medium", "high"]:
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sub = df[(df["magnitude"] == mag) & df["correct_10d"].notna()]
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if len(sub) == 0:
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continue
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print(f" {mag:<10} {sub['correct_10d'].mean()*100:.1f}% (n={len(sub)})")
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print()
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print("BY DIRECTION (10-day accuracy)")
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for d in ["positive", "negative"]:
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sub = df[(df["direction"] == d) & df["correct_10d"].notna()]
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if len(sub) == 0:
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continue
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print(f" {d:<12} {sub['correct_10d'].mean()*100:.1f}% (n={len(sub)})")
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print()
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print("BY TIMEFRAME (10-day accuracy)")
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for tf in ["short", "medium", "long"]:
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sub = df[(df["timeframe"] == tf) & df["correct_10d"].notna()]
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if len(sub) == 0:
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continue
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print(f" {tf:<10} {sub['correct_10d'].mean()*100:.1f}% (n={len(sub)})")
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print()
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sample = df[df["correct_10d"].notna()].head(30)
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print("SAMPLE (30 most recent predictions)")
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print(f"{'Ticker':<12} {'Date':<12} {'Dir':<10} {'Mag':<8} {'5d%':>7} {'10d%':>7} {'20d%':>7} @10d")
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print("-" * 72)
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for _, row in sample.iterrows():
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r5s = f"{row['5d_return']:+.2f}" if row['5d_return'] is not None else "N/A"
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r10s = f"{row['10d_return']:+.2f}" if row['10d_return'] is not None else "N/A"
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r20s = f"{row['20d_return']:+.2f}" if row['20d_return'] is not None else "N/A"
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ok = "YES" if row["correct_10d"] else "NO"
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print(f"{str(row['ticker']):<12} {row['event_date']:<12} {row['direction']:<10} {str(row['magnitude']):<8} {r5s:>7} {r10s:>7} {r20s:>7} {ok}")
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df.to_csv("backtest_results_full.csv", index=False)
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print(f"\nFull results saved to backtest_results_full.csv ({len(df)} rows)")
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if __name__ == "__main__":
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main()
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