Trump Code 市场信号分析系统：特朗普发帖与标普500指数关联预测工具

trump-code-market-signals by aradotso/trending-skills

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安装命令

npx skills add https://github.com/aradotso/trending-skills --skill trump-code-market-signals

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🇨🇳中文介绍

Trump Code — 市场信号分析

技能由 ara.so 提供 — Daily 2026 技能集。

Trump Code 是一个开源系统，它应用暴力计算来寻找特朗普在 Truth Social/X 上的发帖行为与标普 500 指数走势之间具有统计显著性的模式。该系统已测试了 3150 万种模型组合，保留了 551 条有效规则，并在 566 次预测中验证了 61.3% 的命中率（z=5.39，p<0.05）。

安装

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

环境变量

# AI 简报和聊天机器人所需
export GEMINI_KEYS="key1,key2,key3"       # 逗号分隔的 Gemini API 密钥

# 可选：用于 Claude Opus 深度分析
export ANTHROPIC_API_KEY="your-key-here"

# 可选：用于 Polymarket/Kalshi 集成
export POLYMARKET_API_KEY="your-key-here"

命令行界面 — 关键命令

# 从特朗普帖子中检测到的今日信号
python3 trump_code_cli.py signals

# 模型性能排行榜（全部 11 个命名模型）
python3 trump_code_cli.py models

# 获取 LONG/SHORT 共识预测
python3 trump_code_cli.py predict

# 预测市场套利机会
python3 trump_code_cli.py arbitrage

# 系统健康检查（断路器状态）
python3 trump_code_cli.py health

# 完整每日报告（三语）
python3 trump_code_cli.py report

# 将所有数据转储为 JSON
python3 trump_code_cli.py json

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REST API（在线地址：trumpcode.washinmura.jp）

import requests

BASE = "https://trumpcode.washinmura.jp"

# 一次调用获取所有仪表板数据
data = requests.get(f"{BASE}/api/dashboard").json()

# 今日信号 + 7 天历史
signals = requests.get(f"{BASE}/api/signals").json()

# 模型性能排名
models = requests.get(f"{BASE}/api/models").json()

# 最新的 20 条特朗普帖子（带信号标签）
posts = requests.get(f"{BASE}/api/recent-posts").json()

# 实时的 Polymarket 特朗普预测市场（316+）
markets = requests.get(f"{BASE}/api/polymarket-trump").json()

# LONG/SHORT 策略手册
playbook = requests.get(f"{BASE}/api/playbook").json()

# 系统健康状态 / 断路器状态
status = requests.get(f"{BASE}/api/status").json()

AI 聊天机器人 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 服务器（Claude Code / Cursor 集成）

添加到 ~/.claude/settings.json：

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

可用的 MCP 工具：signals, models, predict, arbitrage, health, events, dual_platform, crowd, full_report

所有数据位于 data/ 目录，每日更新：

import json, pathlib

DATA = pathlib.Path("data")

# 44,000+ 条 Truth Social 帖子
posts = json.loads((DATA / "trump_posts_all.json").read_text())

# 已预标记信号的帖子
posts_lite = json.loads((DATA / "trump_posts_lite.json").read_text())

# 566 条已验证的预测及其结果
predictions = json.loads((DATA / "predictions_log.json").read_text())

# 551 条活跃规则（暴力搜索 + 进化）
rules = json.loads((DATA / "surviving_rules.json").read_text())

# 384 个特征 × 414 个交易日
features = json.loads((DATA / "daily_features.json").read_text())

# 标普 500 OHLC 历史数据
market = json.loads((DATA / "market_SP500.json").read_text())

# 断路器 / 系统健康状态
cb = json.loads((DATA / "circuit_breaker_state.json").read_text())

# 规则进化日志（交叉/变异）
evo = json.loads((DATA / "evolution_log.json").read_text())

通过 API 下载数据

import requests

BASE = "https://trumpcode.washinmura.jp"

# 列出可用数据集
catalog = requests.get(f"{BASE}/api/data").json()

# 下载特定文件
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", []))

从有效规则中寻找表现最佳的规则

import json

rules = json.loads(open("data/surviving_rules.json").read())

# 按命中率降序排序
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%}")

检查预测市场机会

import requests

arb = requests.get("https://trumpcode.washinmura.jp/api/insights").json()
markets = requests.get("https://trumpcode.washinmura.jp/api/polymarket-trump").json()

# 按交易量排序的市场
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}")

关联帖子特征与回报率

import json
import numpy as np

features = json.loads(open("data/daily_features.json").read())
market   = json.loads(open("data/market_SP500.json").read())

# 构建日期索引的回报率映射
returns = {d["date"]: d["close_pct"] for d in market}

# 示例：关联 post_count 与次日回报率
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}")

对自定义信号进行回测

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}

# 找出美国东部时间上午 9:30 前出现 RELIEF 信号的日子
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%}")

信号	描述	典型影响
`RELIEF` 盘前	上午 9:30 前的"宽慰"类语言	当日平均 +1.12%
`TARIFF` 交易时段	交易时间内提及关税	次日平均 -0.758%
`DEAL`	交易/协议类语言	52.2% 命中率
`CHINA`（仅 Truth Social）	提及中国（X 上从不出现）	1.5 倍权重提升
`SILENCE`	零发帖日	80% 看涨，平均 +0.409%
爆发 → 沉默	快速发帖后转为安静	65.3% LONG 信号

模型	策略	命中率	平均回报
A3	盘前 RELIEF → 上涨	72.7%	+1.206%
D3	交易量激增 → 恐慌性底部	70.2%	+0.306%
D2	签名切换 → 正式声明	70.0%	+0.472%
C1	爆发 → 长时间沉默 → LONG	65.3%	+0.145%
C3 ⚠️	深夜关税（反向指标）	37.5%	−0.414%

注意： C3 是一个反向指标 — 如果它触发，断路器会自动将其反转为 LONG（反转后准确率为 62%）。

Truth Social 帖子检测（每 5 分钟）
    → 信号分类（RELIEF / TARIFF / DEAL / CHINA / 等）
    → 双平台增强（仅 TS 的中国提及 = 1.5× 权重）
    → 快照 Polymarket + 标普 500
    → 运行 551 条有效规则 → 生成预测
    → 在 1h / 3h / 6h 跟踪
    → 验证结果 → 更新规则权重
    → 断路器：如果系统性能下降 → 暂停/反转
    → 每日：进化规则（交叉 / 变异 / 蒸馏）
    → 同步数据到 GitHub

realtime_loop.py 未检测到新帖子

检查您对 Truth Social 爬虫端点的网络访问
验证 data/trump_posts_all.json 的时间戳是否为最近
运行 python3 trump_code_cli.py health 查看断路器状态

chatbot_server.py 启动失败

确保设置了 GEMINI_KEYS 环境变量：export GEMINI_KEYS="key1,key2"
端口 8888 可能被占用：lsof -i :8888

overnight_search.py 内存不足

运行约 3150 万种组合 — 需要约 4GB 内存
在 8GB+ 内存的机器上运行，或在脚本配置中减少搜索空间

命中率降至 55% 以下

检查 data/circuit_breaker_state.json — 系统可能已自动暂停
查看 data/learning_report.json 了解被降级的规则
重新运行 overnight_search.py 以刷新有效规则

data/ 目录中的数据陈旧

如果运行正常，每日流水线会自动同步到 GitHub
手动触发：python3 trump_code_cli.py report 强制刷新
或从远程拉取最新：git pull origin main

🇺🇸English

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:", today.get("active_models", []))

Find Top Performing Rules from Surviving Rules

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%}")

Check Prediction Market Opportunities

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}")

Correlate Post Features with Returns

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}")

Run a Backtest on a Custom Signal

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%}")

Key Signal Types

Signal	Description	Typical Impact
`RELIEF` pre-market	"Relief" language before 9:30 AM	Avg +1.12% same-day
`TARIFF` market hours	Tariff mention during trading	Avg -0.758% next day
`DEAL`	Deal/agreement language	52.2% hit rate
`CHINA` (Truth Social only)	China mentions (never on X)	1.5× weight boost
`SILENCE`	Zero-post day

Model Reference

Model	Strategy	Hit Rate	Avg Return
A3	Pre-market RELIEF → surge	72.7%	+1.206%
D3	Volume spike → panic bottom	70.2%	+0.306%
D2	Signature switch → formal statement	70.0%	+0.472%
C1	Burst → long silence → LONG	65.3%	+0.145%
C3 ⚠️	Late-night tariff (anti-indicator)	37.5%	−0.414%

Note: C3 is an anti-indicator — if it fires, the circuit breaker auto-inverts it to LONG (62% accuracy after inversion).

System Architecture Flow

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

Troubleshooting

realtime_loop.py not detecting new posts

Check your network access to Truth Social scraper endpoints
Verify data/trump_posts_all.json timestamp is recent
Run python3 trump_code_cli.py health to see circuit breaker state

chatbot_server.py fails to start

Ensure GEMINI_KEYS env var is set: export GEMINI_KEYS="key1,key2"
Port 8888 may be in use: lsof -i :8888

overnight_search.py runs out of memory

Runs ~31.5M combinations — needs ~4GB RAM
Run on a machine with 8GB+ or reduce search space in script config

Hit rate dropping below 55%

Check data/circuit_breaker_state.json — system may have auto-paused
Review data/learning_report.json for demoted rules
Re-run overnight_search.py to refresh surviving rules

Stale data indata/ directory

Daily pipeline syncs to GitHub automatically if running
Manually trigger: python3 trump_code_cli.py report to force refresh
Or pull latest from remote: git pull origin main

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