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01 Algorithmic & AI Trading Systems
Fully automated Python trading engines wired to live news feeds, trained on real trading-class material, and journalled to a web dashboard you can open from anywhere. The bot trades. The journal proves it.
02 Capabilities
In production, not in a backtest. Each part is built to survive real markets, bad data and a model that occasionally makes things up.
The trading core is pure Python — connects to your broker API, runs the strategy loop, executes orders, handles retries and reconnects. Runs 24/7 in a Docker container with auto-restart, no babysitting required.
Pulls real-time market headlines and macro events from sources like Polygon, Alpha Vantage, NewsAPI, and Benzinga. Sentiment is scored and fused with price action so the bot trades on what's actually happening, not yesterday's chart.
Your course notes, mentor playbooks, and the psychology behind disciplined trading are ingested as the bot's knowledge base. The model learns rules it can quote — entries, exits, risk caps, position sizing — not just patterns it guessed at.
Every move — entries, exits, the news the bot saw, the reasoning it gave — is written to a clean web dashboard. Open it on any device. Search, filter, replay. No CSVs, no terminal logs. Your performance history, readable.
A nightly review loop replays the day's trades, scores what worked and what didn't, and adjusts confidence weights for setups, time-of-day, and news categories. The bot gets sharper at reading your market, not a generic one.
Two brains, your call. Hosted LLM (Claude / GPT) with a RAG layer that reasons in natural language and trades like a disciplined human, or a classical ML model (gradient boosted trees, LSTM) for pure-signal latency-sensitive setups. Either way, the Python execute engine has final say.
03 Live demos Simulated market
Same logic, simulated money. A real strategy loop, a real safety layer and a real journal — running on a simulated market in your browser. Nothing here is financial advice.
Demo A · Paper trading
The price ticks, headlines arrive with a sentiment score, and the bot decides: enter, skip or wait. Every trade is sized to your risk, protected by a stop, and written to the journal below.
Simulated prices · demo only · not financial advice
Demo B · Zero hallucinations
LLMs hallucinate. They can invent ticker symbols, miscount lots, or talk themselves into oversized positions. That's fine — because the LLM never touches your broker. Pick a proposal, or edit the JSON yourself, and run it through the engine.
Demo C · Risk lab
Position size comes from your risk, not your mood. Then the same edge is played out 40 times over 100 trades — so you can see the drawdowns before your account does.
04 Architecture
Scroll through the pipeline. Red packets are hallucinated proposals — watch the safety gate stop them.
Price ticks from the broker, headlines from news APIs, macro calendar events. Normalised into a unified stream with timestamps, source, and confidence.
polygon.io · alpha_vantageRetrieves relevant rules from your trading class & mindset notes. Reads the news, scores the setup, proposes an action with a written rationale and confidence.
claude · gpt · vector_dbValidates the trade against hard rules — position size, risk caps, market hours, instrument whitelist. Rejects or trims anything the LLM hallucinated.
execute_trade.pyOrder writes to the broker. Every detail — inputs, reasoning, fill, P&L — writes to Postgres. The web journal updates live. The nightly loop reviews.
postgres · web_journal05 Web Journal
The bot writes a structured entry for every action it considers — not just fills. Skipped setups, rejected hallucinations, take-profits, partial closes. Trades from the simulator above appear here live.
06 Pick Your Brain
A hosted Claude or GPT model with a retrieval layer over your trading class, mentor notes, and historical journal. It reads the news, recalls the rule, writes the rationale, proposes the trade.
Best for: swing trades, news-driven setups, daily review
Classical machine learning — gradient boosted trees, LSTM, or a custom ensemble — trained on your historical signals and labels. No language, no narration. Pattern in, probability out.
Best for: intraday, scalping, latency-sensitive plays
// You can also run both — LLM for daily macro, ML for intraday execution. I've shipped that hybrid before.
07 Build Process
I sit with you, your class notes, and your existing playbook. Every rule becomes a testable spec.
Historical price + news data assembled. Strategy back-tested across multiple regimes before any live capital.
Bot runs live but trades a simulated account for 2–4 weeks. Journal proves it follows the rules under real conditions.
Capital allocated in stages. Safety caps tight at first, widened as the journal shows consistent behaviour.
Nightly review loop tunes confidence weights. Monthly model retraining. New class material rolls in as RAG context.
08 Contact Open to full-stack roles · Dresden / remote