I Built a Trading Bot That Doesn't Just Calculate — It Sees, Remembers, and Learns from Its Failures

1. The Wrocław warehouse spark

In December 2025 I was living in Wrocław, Poland, working 8-hour warehouse shifts. My back was sore, my energy depleted, and my mind was stuck on one question: why are all the "AI trading bots" out there so dumb?

I did what every YouTube tutorial recommends. Pull RSI, MACD and Bollinger Bands in Python, write a prompt template, dump the floats into an LLM API. The decisions came back confident, articulate — and wrong. "RSI is at 28, oversold. BUY." Then the market went down instead, and the paper portfolio went with it.

The model was not the problem. The architecture was. I had built a stateless calculator and called it a reasoning engine.

2. The timeline, taken from git log

Every date below is the date a file first appeared in the repository or a commit landed. You can check all of them with git log --date=short --diff-filter=A.

3. The mathematics and the engineering

The Numba JIT indicator engine

Indicators are written in NumPy and decorated with Numba's @njit(cache=True), so they compile to machine code on first call and cache the result. src/indicators/ contains 97 Numba-compiled functions (134 across the whole src/ tree) with zero external technical-analysis dependencies. Real code, not a screenshot:

@njit(cache=True)
def supertrend_numba(high: np.ndarray, low: np.ndarray, close: np.ndarray,
                     length: int = 10, multiplier: float = 3.0) -> tuple[np.ndarray, np.ndarray]:
    """Calculate Supertrend indicator."""
    n = len(close)
    atr = atr_numba(high, low, close, length)

    hl2 = (high + low) / 2
    upperband = hl2 + multiplier * atr
    lowerband = hl2 - multiplier * atr

    # ... band adjustments, then the trend/direction loop
    return trend, direction

src/indicators/trend/trend_indicators.py — trimmed; the full function is in the repo.

Chart vision

Plotly renders a 1920×1080 candlestick chart (SMA, RSI, CMF, OBV, swing annotations) to PNG and it is sent to a multimodal model alongside the numeric stack. Images arrive from an export pipeline that hangs often enough that the code carries timeouts and exponential-backoff retries around it — src/analyzer/pattern_engine/chart_generator.py.

The Surprise Ratio

A support break that won because of a random news spike should not become a rule. Every closed trade gets a surprise score:

Anything above 1.5 is tagged ⚠️ high surprise in vector memory so the model discounts it later (src/trading/brain_reflection.py). The bot learns from what it understood, not from what it got lucky on.

What constrains an entry

The project prompts the model to discuss expected value, but the repository does not currently enforce a deterministic expected-value gate. Deterministic checks concern the symbol whitelist, position caps and validated stop/target values. The optional min_rr_entry setting is currently 0.0, so this page does not present it as an active entry gate.

What is not in here

• No Kelly criterion. An earlier version of this page claimed Kelly-sized positions. There is no Kelly code in the repository. Position size is the model's proposal, clamped to a 10% of capital cap, with 1% / 2% / 3% fallbacks by conviction level.
• No magic win rate. I have no sustained profitable period to show, so I do not quote one.
• No backtest-to-live claim. Simulated fills have no slippage, no partial fills and no liquidity limits. Paper numbers are not live numbers.

4. Honest status — September 2026

• Trading: simulated capital only (a 10,000 USDC paper book). No real funds are involved anywhere in this project.
• Executor: implemented as a separate service and wired to an exchange testnet (ENABLE_TESTNET=true, sandbox endpoints). It has placed test orders and journals their verdicts; live trading with real money is not enabled.
• Profitability: unproven. Development has had losing stretches and the code comments admit it. If that ever changes, this page changes with it.
• Source: the whole thing is at github.com/qrak/LLM_trader. Read the code before you believe a word of this page.

5. Quick start

git clone https://github.com/qrak/LLM_trader.git && cd LLM_trader
python -m venv .venv && source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -r requirements.txt
cp keys.env.example keys.env    # add GOOGLE_STUDIO_API_KEY, DEEPSEEK_API_KEY or OPENROUTER_API_KEY
python start.py                 # dashboard at http://localhost:8000

Paper trading is the default. The executor only sees decisions when you enable it — and it defaults to testnet.