AAPL Reddit Mentions | ChartExchange

Made a bot that took a little bit. But basically this is what it does
I asked gpt to look over my code and explain to me like im slow what the entire bot does basically.
Its trainings been going very well so far! Will have to see in a couple weeks really.
What Does SHODAN Actually Do?
1. Data Collection (Scanners & Backtesters)
Collects Recent Market Data:
Pulls daily and minute-by-minute price/volume/action data for your target stocks (e.g., NVDA, AMD, AAPL, MSFT, TSLA).
Stores this as CSV files for later training and research.
Backtesting:
Analyzes historical trades and strategies using your backtest log files to see what worked and what failed.
Overnight Data:
After the market closes, SHODAN pulls the newest data (daily candles, news, earnings, etc.) for use in “overnight research mode.”
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2. Model Training (Machine Learning & AI Brains)
Reads all collected data (from scanner_data and backtest_results folders).
Cleans and fills missing values: Makes sure each data sample has everything the models expect (price, volume, technical indicators, etc.).
Trains Multiple AI Models on the Data:
LSTM Models (Long Short-Term Memory):
What they do: Learn to spot trends, patterns, and momentum in time-series data. Used for short-term and longer-term price prediction.
How SHODAN uses them: Three versions—one for 20-bar (short), 50-bar (medium), and 100-bar (long) “lookback windows.”
GRU Models (Gated Recurrent Unit):
What they do: Similar to LSTM, but simpler and faster, used for sequence prediction.
How SHODAN uses them: Trained on the biggest window (usually 100 bars) to predict future prices.
CNN Models (Convolutional Neural Networks):
What they do: Pick up “shapes” or patterns in sequences, just like how image AI sees features in a picture.
How SHODAN uses them: Looks for technical chart patterns and price movement signatures.
Bi-CLSTM Models (Bidirectional LSTM):
What they do: Look at data “forward and backward” in time for richer pattern detection.
How SHODAN uses them: Used for advanced trend/pattern recognition on longer sequences.
XGBoost Models:
What they do: A powerful non-neural machine learning method—great for ranking, regression, and “tabular” prediction.
How SHODAN uses them: Adds another voice to the prediction “committee,” especially good at using lots of mixed features.
Each model type gets its own saved file (e.g., lstm_20.h5, gru_100.h5, cnn_50.h5, etc.).
Models are re-trained on command (at startup, if you say “y” to retraining prompt).
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3. Ensemble Prediction (“Brain Committee Voting”)
When making a trade prediction, SHODAN runs the latest data through all trained models.
Each model gives its “opinion” (prediction) about future price.
SHODAN averages the predictions to get a final answer—this is called an “ensemble” method and is more robust than trusting just one brain.
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4. Live Trading Engine
During market hours, SHODAN continuously:
Grabs the latest price data for each stock.
Runs predictions and pattern scans using all models.
Looks for technical signals (moving averages, crossovers, RSI, volume spikes, etc.).
Checks news and sentiment (every 30 minutes, not every second, to avoid API limits).
Calculates “confidence” for each possible trade.
Risk Controls:
Never trades if there’s a big earnings report due, if you’re over your max open trades, or if you already hold that stock.
Uses dynamic position sizing (based on cash, risk, and volatility).
Places bracket orders (automatic stop-loss and take-profit) for every trade.
All trade decisions, results, and confidence values are logged for later review.
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5. Pattern Detection & Strategy Memory
Scans for common market patterns: (bullish, bearish, neutral, crossovers, etc.).
Remembers which patterns worked best.
Overnight, analyzes all logged patterns to optimize future strategies.
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6. Overnight Research Mode (“AI Night Desk”)
After market closes, SHODAN:
Reads and syncs any new PDFs or research documents for knowledge updates.
Pulls the latest market/news/fundamental data for all your symbols using your FMP (Financial Modeling Prep) premium API.
Runs the overnight_strategy_investigator:
Pulls news, sentiment, analyst ratings, insider trades, earnings, dividends, SEC filings, ETF holdings, sector performance, macro events, and price history for each symbol.
Auto-generates and backtests simple strategies (like moving average crossovers, insider cluster buys, etc.) using this new data.
Ranks and logs the most promising strategies for you to review the next day.
Results are saved to shodan_strategy_investigator_YYYY-MM-DD.json for you to review or auto-integrate.
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7. RL (Reinforcement Learning) Training
Keeps a simulated “game” of trading running in the background.
AI tries out different trading behaviors, learning to maximize rewards or minimize drawdowns, then saves its progress.
This is continuous and helps SHODAN adapt over time.
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8. Dashboard and Monitoring
Dash web dashboard: Live status, RL status, hot-patch status, market sentiment, system resource usage, and more.
Writes system diagnostics (CPU, memory, threads) to file for health checks.
Can be expanded to display live trading stats, overnight research results, etc.
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9. Hot Patch & Status Update Loops
Periodically checks for updates, system health, and other diagnostics.
Keeps everything running smoothly and ready to reload/fix on the fly.
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10. Modular, Self-Healing Architecture
Every error, exception, or missing data is safely logged and handled—
Never crashes from a single failed API call or empty file.
You can add/remove new strategies, data sources, or models easily (just copy the pattern).
Threaded background tasks—trading, research, analysis, RL, and more all run in parallel, so nothing blocks your core

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