AI Trading Bot with Python: Machine Learning & Backtest 2026

Explore how the dot product computes each layer's outputs from inputs, weights, and biases, using a single input X and a two node hidden layer with 96 input prices.

Reshape the input X into a two-dimensional array, feed to FC1 with 20 specialists, apply activation function, pass to a second layer with 40 specialists, and obtain the final prediction.

Create mini batches to split your dataset into smaller x and y chunks, producing batches as pairs and enabling shuffled, faster training with more frequent weight updates and reduced overfitting.

Evaluate a PyTorch model on the test set with torch.no_grad and eval mode, track train_losses and test_losses, plot losses, note overfitting in Bitcoin price prediction, and preview backtesting for profitability.

Backtest a trading strategy using model predictions by aligning opens and closes with the prediction index and skipping the first 96 elements, using backtest_opens and backtest_closes in the trading simulation.

Scale financial time series with per-feature max-abs scalers, using fit on training data and transform on test data to prevent leakage, then prepare features for model training.

Save the training state with torch.save to resume later. Create a models folder and save best weights when equity exceeds 3,000; load training data to resume with a reproducible seed.

Explains how Conv1D in PyTorch detects spikes in time series, demonstrates kernel sliding, channels, and backpropagation, and explains why Conv1D captures temporal context over linear layers.

Implement conv1d in PyTorch to build a two-layer cnn with 32 kernels and kernel size 3. Compare short-term bitcoin price predictions to a linear model, and explore padding and activation.

Load the training scalers from the bitcoin dataset, reuse buildFeatures and preprocessData, and prepare the scalers to normalize live data before trading bot predictions.

Load trained models with load_models.py, preprocess live Binance data using original training scalers, then predict, combine outputs, and decide buy or sell actions for the trading bot.

Build a Binance futures trading bot that updates current futures prices, prepares model input from data, runs async limit orders, and tracks equity, position, and fees.

Build a margin trading bot on Binance with Python, covering library setup, logging, and the Bot class, while explaining maker versus taker, slippage, latency, and FDUSD pairing.

Assemble and run a 24/7 margin trading bot on Binance using trained models, scalers, and a Bot class, with live market data, model signals, order management, and state persistence.

Add high and low prices to preprocess_data to enable stop-loss checks within each candle and implement risk-first backtest logic to improve equity.

Implement a stop-loss feature by adding a 0.02 threshold and a close_position method using market orders to exit long or short positions, with a stop loss flag to prevent duplicates.