An autonomous AI trading agent that reads charts, remembers outcomes, and learns from every trade — powered by Google Gemini, ChromaDB vector memory, and a multi-stage reasoning pipeline.
Every 4 hours, the bot executes a full analysis pipeline — from raw market data to a reasoned trading decision with SL/TP levels.
CCXT pulls 999 candles, a 50-level order book, funding rate and 24h volume from the exchange. Numba JIT computes 50+ indicators and dozens of deterministic chart patterns in milliseconds — the bot sees both the raw numbers and their technical context.
Gemini receives the Full HD chart image plus the indicator stack plus fresh news and Reddit sentiment. It argues the bull case, then the bear case, then evaluates expected value against its own trade history — and must pass a falsification check before a signal is accepted.
Every signal runs a guard chain: symbol whitelist, position cap, cooldown window, R:R gate. Stop-loss scales with ATR volatility, take-profit targets minimum 1.5 R:R. The decision, reasoning, and risk parameters are all logged to the audit trail.
When a trade closes, its outcome is embedded as a 768D vector in ChromaDB. The reflection engine periodically synthesizes new rules from what worked and what didn't — flagging surprising outcomes so luck isn't confused with skill. Next cycle, the bot remembers.
Not just another trading bot. This one reasons, debates, remembers, and improves.
Google Gemini reads candlestick charts visually AND numerically — interpreting 50+ indicators simultaneously and spotting patterns a human would miss.
A Full HD chart image with SMA, RSI and volume overlay is sent to the model alongside the raw indicator values. It sees the shape of the wicks, the volume context, the pattern structure — the same picture a human trader sees. And it's not taken on faith: every numeric claim is cross-checked against computed values.
Before every decision, the LLM argues BOTH sides. Bull case optimistic, Bear case pessimistic. Then it decides. More balanced, less emotional.
The debate is followed by an expected-value calculation using real trade history — probability of win × average win, minus probability of loss × average loss, minus fees. A falsification check then rejects any signal the LLM cannot defend. A missed +EV opportunity is treated as mathematically identical to a realized loss.
Every closed trade is embedded as a vector in a semantic database. On the next cycle, the bot queries its own brain: "Have I seen this pattern before? What happened?"
Trade experiences, semantic rules, blocked trades, and confidence statistics live in four ChromaDB collections, embedded with BAAI/bge-base-en-v1.5 at 768 dimensions. Before each decision the brain retrieves the top-5 most similar past trades plus the rules matched to current conditions — scored by similarity, evidence quality, and timeframe freshness. The bot literally reviews its own history before acting.
ATR-scaled stop-loss and take-profit, portfolio-percentage position sizing, and risk profiles that adapt automatically to market conditions and its own track record.
Three risk modes — aggressive, conservative, neutral — switch automatically based on win rate, average P&L, and market trend strength (ADX). Position size scales with confidence: fallback sizes of 1% / 2% / 3% of capital for LOW / MEDIUM / HIGH conviction. Stop-loss distance scales with volatility (ATR), take-profit targets minimum 1.5 R:R at structure, and friction from position-size clamping is measured and reported rather than silently ignored.
Live news feeds (CoinDesk, CoinTelegraph, Decrypt) and Reddit community sentiment — all scraped directly. No API keys, no subscription fees.
Every 4-hour cycle ingests up to 5 top articles, scored for relevance before they reach the model. Fundamentals come from CoinGecko and DeFiLlama, plus the Alternative.me Fear & Greed index. X/Twitter was tried and dropped — without paid auth it isn't reliably scrapable, so the bot honestly runs on Reddit + RSS rather than pretending otherwise.
Cooldown windows prevent overtrading. Position caps, symbol whitelist, and validated SL/TP levels enforced before every order. Paper trading by default.
Every signal passes a pre-execution guard chain: symbol whitelist → maximum position size → cooldown window. If governance or risk validation can't decide safely, the system fails closed — no order, no exceptions. Soft exits trigger at candle close, hard exits check against live ticker prices at 15-minute intervals. Real exchange execution is being built as a dedicated executor service — a separate component that places and manages live orders under the same guard chain, with capital simulated until the pipeline proves itself.
Live WebSocket dashboard with candlestick charts, decision graphs, and the exact prompt, reasoning, and response behind every trade.
The FastAPI dashboard streams brain activity, position state, performance statistics, news, market data, and the memory bank across ten tabs. Every decision's full prompt and LLM response is visible — plus an admin console for configuration, force-analysis, and log streaming. No black boxes.
Trains on real order book dynamics — counterparty orders, funding rate traps, liquidity squeezes. Not just abstract lines on a chart.
The system prompt teaches the LLM how real markets behave: where counterparty orders sit, how funding rates trap leveraged positions, how liquidity squeezes play out. A post-mortem is written after every closed trade and stored with the experience — so the bot learns market behavior from its own results, not from theory.
Freqtrade, 3Commas, Cryptohopper, Gunbot, Hummingbot — none of them do what this bot does. Here's the honest comparison.
The difference isn't one feature — it's the whole architecture. Most bots are a strategy engine: fixed rules, fixed size, no memory, no context. This bot is a reasoning system: it reads the chart as an image, recalls its own trade history, argues both market directions, and is forced to defend every signal before it's executed. Below is the same comparison every bot vendor hopes you never see.
1,270+ automated tests. Deterministic outputs. CI-ready. A trading engine engineered to the same standard as production banking software — not a weekend script.
This is not a mature industry with proven products. It's a frontier — and that's exactly why this project exists.
Classical ML — XGBoost, random forests, feature-engineered signals — has a real, decades-long track record in quant trading. LLM-based autonomous trading does not. That distinction matters, and we don't blur it.
LLM trading has no decade of backtests, no academic consensus, no established best practices. The research papers on LLM agents for trading are barely two years old. Every builder is figuring it out from scratch — including us.
If a vendor tells you their LLM bot has a "proven" record — they don't. The field is too young for one to exist. The only honest response is what we do: run in the open, show every trade, and let the data speak.
You're not buying a finished product here. You're watching a new category get figured out in real time — mistakes, lessons, and all. That's a feature, not a bug.
Every bot has flaws. Here are ours — stated plainly, before you ask. And here's what we're doing about each one.
The honest pitch: this is an experiment in autonomous AI trading — running in the open, with its flaws visible, and improving every cycle. If you want a money printer, this isn't it. If you want to watch a genuinely novel system learn to trade, this is the most interesting one you'll find.
asyncio, dependency injection, modular architecture
REST API, WebSocket streaming, middleware pipeline
Chart reading, technical reasoning, trade debates
50+ home-grown indicators, 0 external TA dependencies
Vector embeddings, semantic trade memory, full-text search
Decision graphs, candlestick charts, live KPI cards
Tunnel, CDN, Page Rules, zero-downtime deploys
1,270+ automated tests, zero lint errors, CI-ready
Live dashboard with real-time charts, decision graphs, and full trading transparency. No signup. Just watch the system that's being scaled to real trades.
Self-taught backend developer with deep AI/LLM expertise. Currently working in warehouse logistics while building autonomous AI systems after hours. My strength: I go deep rather than wide — every line of this codebase I wrote, tested, and understand.
Previously worked with enterprise SAP environments at BSH Sprzęt Gospodarstwa Domowego, where I gained enterprise software experience and learned to navigate complex business processes. Now I build AI agents that trade crypto autonomously — and right now I'm building the trade executor: the dedicated service that carries this system from paper trading to live exchange orders.
Interested in: Backend Python/FastAPI, AI engineering, LLM infrastructure — ideally a role where I can apply both my AI knowledge and backend skills. No university degree, but I bring something better: a project with 1,270+ automated tests, zero lint errors, and a live dashboard you can inspect right now.