Multi-filter institutional-grade algorithmic trading systems built for private clients (2023–2025)
A collection of algorithmic trading system architectures designed and delivered to private clients — incorporating post-earnings data analysis, multi-timeframe technical confirmation, and institutional signal filtering.
These systems were designed to replicate institutional-grade signal filtering at the retail level — combining fundamental catalysts (earnings, government disclosures) with multi-timeframe technical confirmation to generate high-conviction trade setups.
- Post-Earnings Drift Analysis — captures systematic price behavior following earnings surprises
- Government Disclosure Monitoring — tracks SEC filings, congressional trades, and institutional 13F changes
- Catalyst Detection — identifies FDA approvals, contract awards, and regulatory events as entry triggers
Multi-timeframe confirmation across:
- RSI (Relative Strength Index) — momentum and overbought/oversold detection
- MACD — trend direction and momentum crossovers
- Bollinger Bands — volatility-adjusted entry zones
- EMA Stacks — trend alignment across 9/21/50/200 EMA
- Volume Profile — institutional accumulation/distribution detection
- Position sizing based on ATR (Average True Range)
- Hard stop-loss and trailing stop logic
- Maximum drawdown circuit breakers
- Correlation-based portfolio exposure limits
- Signal generation → alert delivery pipeline
- Broker API integration (TD Ameritrade, Interactive Brokers)
- Paper trading mode for system validation
Confluence over frequency — systems only fire when multiple independent signals align, reducing false positives at the cost of trade frequency.
Asymmetric risk/reward — minimum 2:1 reward-to-risk ratio enforced at signal generation, not just position sizing.
Catalyst-driven entries — technical setups are only acted upon when a fundamental catalyst (earnings, government data, regulatory event) provides a directional thesis.
| Component | Technology |
|---|---|
| Data Feeds | Polygon.io, Alpaca, SEC EDGAR, Congressional disclosure APIs |
| Signal Processing | Python, pandas, numpy, ta-lib |
| Backtesting | Backtrader, custom vectorized backtesting engine |
| Execution | Alpaca API, TD Ameritrade thinkorswim API |
| Alerting | Webhook delivery, SMS (Twilio), Discord |
| Infrastructure | Docker, GitHub Actions, scheduled cron pipelines |
The government data monitoring and signal processing architecture from these systems directly informed the design of AlphaPipeline.ai — a production AI SaaS platform integrating 60+ government data sources with LLM materiality scoring, autonomous paper trading, and real-time signal delivery.
Designed and delivered by Ahmad Albaba for private clients. Not financial advice. Past performance does not guarantee future results.