trump-code-market-signals

# Today's detected signals from Trump's posts
python3 trump_code_cli.py signals
 
# Model performance leaderboard (all 11 named models)
python3 trump_code_cli.py models
 
# Get LONG/SHORT consensus prediction
python3 trump_code_cli.py predict
 
# Prediction market arbitrage opportunities
python3 trump_code_cli.py arbitrage
 
# System health check (circuit breaker state)
python3 trump_code_cli.py health
 
# Full daily report (trilingual)
python3 trump_code_cli.py report
 
# Dump all data as JSON
python3 trump_code_cli.py json

# Real-time Trump post monitor (polls every 5 min)
python3 realtime_loop.py
 
# Brute-force model search (~25 min, tests millions of combos)
python3 overnight_search.py
 
# Individual analyses
python3 analysis_06_market.py        # Posts vs S&P 500 correlation
python3 analysis_09_combo_score.py   # Multi-signal combo scoring
 
# Web dashboard + AI chatbot on port 8888
export GEMINI_KEYS="key1,key2,key3"
python3 chatbot_server.py
# → http://localhost:8888

import requests
 
BASE = "https://trumpcode.washinmura.jp"
 
# All dashboard data in one call
data = requests.get(f"{BASE}/api/dashboard").json()
 
# Today's signals + 7-day history
signals = requests.get(f"{BASE}/api/signals").json()
 
# Model performance rankings
models = requests.get(f"{BASE}/api/models").json()
 
# Latest 20 Trump posts with signal tags
posts = requests.get(f"{BASE}/api/recent-posts").json()
 
# Live Polymarket Trump prediction markets (316+)
markets = requests.get(f"{BASE}/api/polymarket-trump").json()
 
# LONG/SHORT playbooks
playbook = requests.get(f"{BASE}/api/playbook").json()
 
# System health / circuit breaker state
status = requests.get(f"{BASE}/api/status").json()

import requests
 
response = requests.post(
    "https://trumpcode.washinmura.jp/api/chat",
    json={"message": "What signals fired today and what's the consensus?"}
)
print(response.json()["reply"])

{
  "mcpServers": {
    "trump-code": {
      "command": "python3",
      "args": ["/path/to/trump-code/mcp_server.py"]
    }
  }
}

import json, pathlib
 
DATA = pathlib.Path("data")
 
# 44,000+ Truth Social posts
posts = json.loads((DATA / "trump_posts_all.json").read_text())
 
# Posts with signals pre-tagged
posts_lite = json.loads((DATA / "trump_posts_lite.json").read_text())
 
# 566 verified predictions with outcomes
predictions = json.loads((DATA / "predictions_log.json").read_text())
 
# 551 active rules (brute-force + evolved)
rules = json.loads((DATA / "surviving_rules.json").read_text())
 
# 384 features × 414 trading days
features = json.loads((DATA / "daily_features.json").read_text())
 
# S&P 500 OHLC history
market = json.loads((DATA / "market_SP500.json").read_text())
 
# Circuit breaker / system health
cb = json.loads((DATA / "circuit_breaker_state.json").read_text())
 
# Rule evolution log (crossover/mutation)
evo = json.loads((DATA / "evolution_log.json").read_text())

import requests
 
BASE = "https://trumpcode.washinmura.jp"
 
# List available datasets
catalog = requests.get(f"{BASE}/api/data").json()
 
# Download a specific file
raw = requests.get(f"{BASE}/api/data/surviving_rules.json").content
rules = json.loads(raw)

import requests
 
signals_data = requests.get("https://trumpcode.washinmura.jp/api/signals").json()
 
today = signals_data.get("today", {})
print("Signals fired today:", today.get("signals", []))
print("Consensus:", today.get("consensus"))        # "LONG" / "SHORT" / "NEUTRAL"
print("Confidence:", today.get("confidence"))      # 0.0–1.0
print("Active models:", today.get("active_models", []))

import json
 
rules = json.loads(open("data/surviving_rules.json").read())
 
# Sort by hit rate descending
top_rules = sorted(rules, key=lambda r: r.get("hit_rate", 0), reverse=True)
 
for rule in top_rules[:10]:
    print(f"Rule: {rule['id']} | Hit Rate: {rule['hit_rate']:.1%} | "
          f"Trades: {rule['n_trades']} | Avg Return: {rule['avg_return']:.3%}")

import requests
 
arb = requests.get("https://trumpcode.washinmura.jp/api/insights").json()
markets = requests.get("https://trumpcode.washinmura.jp/api/polymarket-trump").json()
 
# Markets sorted by volume
active = [m for m in markets.get("markets", []) if m.get("active")]
by_volume = sorted(active, key=lambda m: m.get("volume", 0), reverse=True)
 
for m in by_volume[:5]:
    print(f"{m['title']}: YES={m['yes_price']:.0%} | Vol=${m['volume']:,.0f}")

import json
import numpy as np
 
features = json.loads(open("data/daily_features.json").read())
market   = json.loads(open("data/market_SP500.json").read())
 
# Build date-indexed return map
returns = {d["date"]: d["close_pct"] for d in market}
 
# Example: correlate post_count with next-day return
xs, ys = [], []
for day in features:
    date = day["date"]
    if date in returns:
        xs.append(day.get("post_count", 0))
        ys.append(returns[date])
 
correlation = np.corrcoef(xs, ys)[0, 1]
print(f"Post count vs same-day return: r={correlation:.3f}")

import json
 
posts    = json.loads(open("data/trump_posts_lite.json").read())
market   = json.loads(open("data/market_SP500.json").read())
 
returns  = {d["date"]: d["close_pct"] for d in market}
 
# Find days with RELIEF signal before 9:30 AM ET
relief_days = [
    p["date"] for p in posts
    if "RELIEF" in p.get("signals", []) and p.get("hour", 24) < 9
]
 
hits = [returns[d] for d in relief_days if d in returns]
if hits:
    print(f"RELIEF pre-market: n={len(hits)}, "
          f"avg={sum(hits)/len(hits):.3%}, "
          f"hit_rate={sum(1 for h in hits if h > 0)/len(hits):.1%}")

Truth Social post detected (every 5 min)
    → Classify signals (RELIEF / TARIFF / DEAL / CHINA / etc.)
    → Dual-platform boost (TS-only China = 1.5× weight)
    → Snapshot Polymarket + S&P 500
    → Run 551 surviving rules → generate prediction
    → Track at 1h / 3h / 6h
    → Verify outcome → update rule weights
    → Circuit breaker: if system degrades → pause/invert
    → Daily: evolve rules (crossover / mutation / distillation)
    → Sync data to GitHub