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)