AutoSignals

Monitors and controls the AutoSignals autonomous research loop.

AutoSignals - Autonomous Trading Signal Optimization

Monitors and controls the AutoSignals autonomous research loop.

What It Is

AutoSignals is an adaptation of Karpathy's autoresearch pattern for trading signal optimization. An autonomous loop runs continuously, spawning sub-agents to modify signals.py, backtesting changes, and keeping improvements.

Architecture:

• signals.py — The ONE file agents can modify (factor weights, thresholds, indicators, scoring)
• backtest.py — Fixed evaluation engine (5-year backtest, composite score metric)
• prepare.py — Data download (S&P 500 + held tickers)
• program.md — Instructions for research agents
• run.py — Autonomous loop controller
• experiments.jsonl — Full experiment log

Location: /Users/clawdiri/Projects/autosignals/

How to Use

Check Status

bash

bash /Users/clawdiri/Projects/autosignals/status.sh

Shows:

• Running status (PID, uptime)
• Best composite score achieved
• Total experiments run
• Last 10 experiments with outcomes
• Score trend (last 20)
• Any errors

Start the Loop

bash

bash /Users/clawdiri/Projects/autosignals/start.sh

Starts the autonomous loop in the background. Runs forever until stopped.

Stop the Loop

bash

kill $(cat /Users/clawdiri/Projects/autosignals/autosignals.pid)

View Logs

bash

tail -f /Users/clawdiri/Projects/autosignals/logs/autosignals.log

View Best Signals

bash

cat /Users/clawdiri/Projects/autosignals/best_score.json

Then read the corresponding commit:

bash

cd /Users/clawdiri/Projects/autosignals
git show <commit_hash>:signals.py

Monitoring Script (for DaVinci heartbeats)

bash

bash /Users/clawdiri/Projects/autosignals/monitor.sh

Returns JSON with:

• running: bool
• experiment_count: int
• best_score: float
• best_commit: str
• trend: "improving" | "declining" | "flat"
• errors: list of recent errors

Evaluation Metric

text

composite_score = (0.35 * sharpe_normalized) + 
                  (0.25 * (1 - max_drawdown)) + 
                  (0.20 * win_rate) + 
                  (0.20 * profit_factor_normalized)

All components normalized to [0, 1].

Baseline targets:

• Sharpe: 1.57 / 1.46 / 1.24
• Starting weights: 40% Insider / 35% Earnings / 25% Sector Rotation

Good: Beat baseline Great: Sharpe > 2.0, drawdown < 15% Exceptional: Sharpe > 2.5, drawdown < 10%

Data

• Price data: 5 years daily OHLCV for S&P 500 + META, GOOG, AMZN, TSLA, BTC-USD, IAU
• Factor data: Currently mock (insider, earnings, sector). Can be enhanced with real API data.
• Cache: /Users/clawdiri/Projects/autosignals/data/prices.parquet

Refresh data:

bash

cd /Users/clawdiri/Projects/autosignals
source .venv/bin/activate
python prepare.py

Design Principles (from Karpathy)

1. Single modifiable file — agents only edit signals.py
2. Fixed evaluation — backtest.py is immutable truth
3. Self-contained — no external API calls during backtest (cached data only)
4. Git-tracked progress — every improvement is a commit
5. Resilient loop — individual failures don't stop the system

Alert Conditions (for DaVinci)

• Loop stopped unexpectedly → WhatsApp alert
• No experiments in last 30 minutes (if running) → check logs
• Error rate > 50% (last 10 experiments) → investigate
• New best score achieved → celebrate 🎉

When to Intervene

Hands-off:

• Normal operation (experiments running, mix of keep/discard)
• Gradual improvement trend
• Low error rate

Check it out:

• All experiments failing (agent spawn issues? data corruption?)
• Score trend declining over 20+ experiments (overfitting? bad hypothesis?)
• Loop stopped (crash? resource exhaustion?)

Celebrate:

• New all-time best score
• Sharpe > 2.0 achieved
• Major breakthrough (e.g., 10%+ score improvement)

Future Enhancements

• Real factor data integration (Finnhub insider API, FMP earnings, sector ETF momentum)
• Multi-ticker portfolio optimization (vs current single-ticker signals)
• Walk-forward validation (rolling window backtest to prevent overfitting)
• Ensemble signals (combine multiple top-performing signal variants)
• Risk-adjusted position sizing (Kelly criterion, volatility targeting)
• Live paper trading integration (Alpaca API)