Trump Code — Market Signal Analysis

Skill by ara.so — Daily 2026 Skills collection.

Trump Code is an open-source system that applies brute-force computation to find statistically significant patterns between Trump's Truth Social/X posting behavior and S&P 500 movements. It has tested 31.5M model combinations, maintains 551 surviving rules, and has a verified 61.3% hit rate across 566 predictions (z=5.39, p<0.05).

Installation

git clone https://github.com/sstklen/trump-code.git
cd trump-code
pip install -r requirements.txt

Environment Variables

# Required for AI briefing and chatbot
export GEMINI_KEYS="key1,key2,key3"       # Comma-separated Gemini API keys

# Optional: for Claude Opus deep analysis
export ANTHROPIC_API_KEY="your-key-here"

# Optional: for Polymarket/Kalshi integration
export POLYMARKET_API_KEY="your-key-here"

CLI — Key Commands

# 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

Core Scripts

# 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

REST API (Live at trumpcode.washinmura.jp)

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()

AI Chatbot API

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"])

MCP Server (Claude Code / Cursor Integration)

Add to ~/.claude/settings.json:

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

Available MCP tools: signals, models, predict, arbitrage, health, events, dual_platform, crowd, full_report

Open Data Files

All data lives in data/ and is updated daily:

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())

Download Data via API

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)

Real Code Examples

Parse Today's Signals

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:", toda