refactor: update worker commands and add new scripts for API rebuilding and queue feeding
This commit is contained in:
ImBenji
@@ -0,0 +1,265 @@
|
||||
import requests
|
||||
import json
|
||||
import yfinance as yf
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
import time
|
||||
|
||||
BASE_URL = "https://duriin.imbenji.net"
|
||||
USERNAME = "admin"
|
||||
PASSWORD = "changeme"
|
||||
|
||||
SQL_QUERY = """
|
||||
SELECT
|
||||
ep.id,
|
||||
ep.event_date,
|
||||
ep.direction,
|
||||
ep.magnitude,
|
||||
ep.timeframe,
|
||||
substr(ep.rationale, 1, 100) as rationale,
|
||||
tc.name,
|
||||
tc.ticker
|
||||
FROM event_predictions ep
|
||||
JOIN tracked_companies tc ON ep.company_id = tc.id
|
||||
WHERE ep.event_date >= '2020-01-01'
|
||||
AND ep.event_date <= '2026-01-01'
|
||||
AND ep.direction IN ('positive', 'negative')
|
||||
AND tc.ticker NOT LIKE '%.%'
|
||||
AND tc.ticker NOT LIKE '%ORIGIN%'
|
||||
AND tc.ticker NOT LIKE '%PRIVATE%'
|
||||
AND tc.ticker NOT LIKE '%DEEPSEEK%'
|
||||
AND tc.ticker NOT LIKE '%ANTHROPIC%'
|
||||
AND tc.ticker NOT LIKE '%OPENAI%'
|
||||
AND tc.ticker NOT LIKE '%BYTEDANCE%'
|
||||
AND tc.ticker NOT LIKE '%HUAWEI%'
|
||||
AND tc.ticker NOT LIKE '%SCALEAI%'
|
||||
AND tc.ticker NOT LIKE '%MISTRAL%'
|
||||
AND tc.ticker NOT LIKE '%COHERE%'
|
||||
AND tc.ticker NOT LIKE '%GROQ%'
|
||||
AND tc.ticker NOT LIKE '%INFLECTION%'
|
||||
AND tc.ticker NOT LIKE '%STABILITY%'
|
||||
AND tc.ticker NOT LIKE '%SPACEX%'
|
||||
AND tc.ticker NOT LIKE '%MCKINSEY%'
|
||||
AND tc.ticker NOT LIKE '%DELOITTE%'
|
||||
AND tc.ticker NOT LIKE '%XAI%'
|
||||
AND length(tc.ticker) <= 5
|
||||
ORDER BY ep.event_date DESC
|
||||
LIMIT 500
|
||||
"""
|
||||
|
||||
|
||||
def fetch_predictions():
|
||||
resp = requests.post(
|
||||
f"{BASE_URL}/admin/api/sql",
|
||||
json={"sql": SQL_QUERY, "database": "intelligence"},
|
||||
auth=(USERNAME, PASSWORD),
|
||||
timeout=30
|
||||
)
|
||||
resp.raise_for_status()
|
||||
data = resp.json()
|
||||
|
||||
# shape: {"results": [{"sql": "...", "rows": [...]}], "elapsed": 0}
|
||||
if isinstance(data, list):
|
||||
return data
|
||||
if "results" in data and isinstance(data["results"], list) and len(data["results"]) > 0:
|
||||
result = data["results"][0]
|
||||
if "error" in result:
|
||||
raise RuntimeError(f"SQL error: {result['error']}")
|
||||
return result.get("rows", [])
|
||||
if "rows" in data:
|
||||
return data["rows"]
|
||||
return data
|
||||
|
||||
|
||||
price_cache = {}
|
||||
|
||||
def get_price(ticker, date_str):
|
||||
key = (ticker, date_str)
|
||||
if key in price_cache:
|
||||
return price_cache[key]
|
||||
|
||||
try:
|
||||
dt = datetime.strptime(date_str, "%Y-%m-%d")
|
||||
start = dt - timedelta(days=5)
|
||||
end = dt + timedelta(days=5)
|
||||
|
||||
hist = yf.Ticker(ticker).history(
|
||||
start=start.strftime("%Y-%m-%d"),
|
||||
end=end.strftime("%Y-%m-%d"),
|
||||
auto_adjust=True
|
||||
)
|
||||
|
||||
if hist.empty:
|
||||
price_cache[key] = None
|
||||
return None
|
||||
|
||||
# nearest trading day on or after date
|
||||
hist.index = hist.index.tz_localize(None) if hist.index.tzinfo else hist.index
|
||||
target = pd.Timestamp(dt)
|
||||
after = hist[hist.index >= target]
|
||||
if after.empty:
|
||||
after = hist
|
||||
price = float(after["Close"].iloc[0])
|
||||
price_cache[key] = price
|
||||
return price
|
||||
|
||||
except Exception:
|
||||
price_cache[key] = None
|
||||
return None
|
||||
|
||||
|
||||
def add_trading_days(date_str, n):
|
||||
dt = datetime.strptime(date_str, "%Y-%m-%d")
|
||||
count = 0
|
||||
while count < n:
|
||||
dt += timedelta(days=1)
|
||||
if dt.weekday() < 5: # mon-fri
|
||||
count += 1
|
||||
return dt.strftime("%Y-%m-%d")
|
||||
|
||||
|
||||
def main():
|
||||
print("Fetching predictions from remote...")
|
||||
preds = fetch_predictions()
|
||||
print(f"Got {len(preds)} predictions\n")
|
||||
|
||||
results = []
|
||||
skipped = 0
|
||||
|
||||
for i, p in enumerate(preds):
|
||||
if i > 0 and i % 50 == 0:
|
||||
print(f" Progress: {i}/{len(preds)} — skipped so far: {skipped}")
|
||||
|
||||
ticker = p["ticker"]
|
||||
event_date = p["event_date"][:10] # trim time if present
|
||||
direction = p["direction"]
|
||||
|
||||
date_5d = add_trading_days(event_date, 5)
|
||||
date_10d = add_trading_days(event_date, 10)
|
||||
date_20d = add_trading_days(event_date, 20)
|
||||
|
||||
price_0 = get_price(ticker, event_date)
|
||||
if price_0 is None:
|
||||
skipped += 1
|
||||
continue
|
||||
|
||||
price_5 = get_price(ticker, date_5d)
|
||||
price_10 = get_price(ticker, date_10d)
|
||||
price_20 = get_price(ticker, date_20d)
|
||||
|
||||
def ret(px):
|
||||
if px is None:
|
||||
return None
|
||||
return (px - price_0) / price_0 * 100
|
||||
|
||||
r5 = ret(price_5)
|
||||
r10 = ret(price_10)
|
||||
r20 = ret(price_20)
|
||||
|
||||
def correct(r):
|
||||
if r is None:
|
||||
return None
|
||||
if direction == "positive":
|
||||
return r > 0
|
||||
else:
|
||||
return r < 0
|
||||
|
||||
results.append({
|
||||
"id": p["id"],
|
||||
"ticker": ticker,
|
||||
"name": p["name"],
|
||||
"event_date": event_date,
|
||||
"direction": direction,
|
||||
"magnitude": p.get("magnitude", ""),
|
||||
"timeframe": p.get("timeframe", ""),
|
||||
"rationale": p.get("rationale", ""),
|
||||
"price_0": round(price_0, 4),
|
||||
"price_5d": round(price_5, 4) if price_5 else None,
|
||||
"price_10d": round(price_10, 4) if price_10 else None,
|
||||
"price_20d": round(price_20, 4) if price_20 else None,
|
||||
"5d_return": round(r5, 4) if r5 is not None else None,
|
||||
"10d_return": round(r10, 4) if r10 is not None else None,
|
||||
"20d_return": round(r20, 4) if r20 is not None else None,
|
||||
"correct_5d": correct(r5),
|
||||
"correct_10d": correct(r10),
|
||||
"correct_20d": correct(r20),
|
||||
})
|
||||
|
||||
df = pd.DataFrame(results)
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print("BACKTEST RESULTS")
|
||||
print(f"{'='*60}")
|
||||
print(f"Total predictions fetched: {len(preds)}")
|
||||
print(f"Skipped (no price data): {skipped}")
|
||||
print(f"Evaluated: {len(df)}")
|
||||
print(f"Random baseline: 50.0%")
|
||||
print()
|
||||
|
||||
|
||||
def acc(col):
|
||||
sub = df[df[col].notna()]
|
||||
if len(sub) == 0:
|
||||
return 0, 0
|
||||
pct = sub[col].mean() * 100
|
||||
return pct, len(sub)
|
||||
|
||||
a5, n5 = acc("correct_5d")
|
||||
a10, n10 = acc("correct_10d")
|
||||
a20, n20 = acc("correct_20d")
|
||||
|
||||
print("OVERALL DIRECTIONAL ACCURACY")
|
||||
print(f" 5-day: {a5:.1f}% (n={n5})")
|
||||
print(f" 10-day: {a10:.1f}% (n={n10})")
|
||||
print(f" 20-day: {a20:.1f}% (n={n20})")
|
||||
print()
|
||||
|
||||
# by magnitude
|
||||
print("BY MAGNITUDE (10-day accuracy)")
|
||||
for mag in sorted(df["magnitude"].dropna().unique()):
|
||||
sub = df[(df["magnitude"] == mag) & df["correct_10d"].notna()]
|
||||
if len(sub) == 0:
|
||||
continue
|
||||
pct = sub["correct_10d"].mean() * 100
|
||||
print(f" {mag:<12} {pct:.1f}% (n={len(sub)})")
|
||||
print()
|
||||
|
||||
# by direction
|
||||
print("BY DIRECTION (10-day accuracy)")
|
||||
for d in ["bull", "bear"]:
|
||||
sub = df[(df["direction"] == d) & df["correct_10d"].notna()]
|
||||
if len(sub) == 0:
|
||||
continue
|
||||
pct = sub["correct_10d"].mean() * 100
|
||||
print(f" {d:<8} {pct:.1f}% (n={len(sub)})")
|
||||
print()
|
||||
|
||||
# by timeframe
|
||||
print("BY TIMEFRAME (10-day accuracy)")
|
||||
for tf in sorted(df["timeframe"].dropna().unique()):
|
||||
sub = df[(df["timeframe"] == tf) & df["correct_10d"].notna()]
|
||||
if len(sub) == 0:
|
||||
continue
|
||||
pct = sub["correct_10d"].mean() * 100
|
||||
print(f" {tf:<12} {pct:.1f}% (n={len(sub)})")
|
||||
print()
|
||||
|
||||
# sample table
|
||||
sample = df[df["correct_10d"].notna()].head(30)
|
||||
print("SAMPLE (30 predictions)")
|
||||
print(f"{'Company':<12} {'Date':<12} {'Dir':<5} {'Mag':<8} {'5d%':>7} {'10d%':>7} {'20d%':>7} Correct@10d")
|
||||
print("-" * 75)
|
||||
for _, row in sample.iterrows():
|
||||
r5s = f"{row['5d_return']:+.2f}" if row['5d_return'] is not None else "N/A"
|
||||
r10s = f"{row['10d_return']:+.2f}" if row['10d_return'] is not None else "N/A"
|
||||
r20s = f"{row['20d_return']:+.2f}" if row['20d_return'] is not None else "N/A"
|
||||
ok = "YES" if row["correct_10d"] else "NO"
|
||||
name_short = str(row["ticker"])[:11]
|
||||
print(f"{name_short:<12} {row['event_date']:<12} {row['direction']:<5} {str(row['magnitude']):<8} {r5s:>7} {r10s:>7} {r20s:>7} {ok}")
|
||||
|
||||
df.to_csv("backtest_results.csv", index=False)
|
||||
print(f"\nFull results saved to backtest_results.csv ({len(df)} rows)")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user