AI Trading Bot with Python: Machine Learning & Backtest 2026

Learn to preprocess bitcoin price data by splitting into training and testing sets, build features from open and close prices, and convert data into PyTorch tensors for model training.

Learn neural network fundamentals through a trading example, where today's price becomes input, weights and biases shape hidden and output predictions, and forward propagation yields tomorrow's price.

Compare the predicted price with the true price to compute loss. Minimize the loss by adjusting weights and biases through gradient descent in a neural network.

Learn how a neural network trains with forward and backward propagation, gradient descent, and PyTorch handling the math; focus on forward pass and choosing layers and neurons for trading predictions.

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.

Explore how activation functions introduce non-linearity to neural networks, turning weighted inputs into complex patterns and shaping the final prediction with ReLU on hidden nodes.

Build the neural network model in PyTorch by stacking linear layers with ReLU, reshaping time series input, and understanding batch size to forecast Bitcoin prices for trading.

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.

Initialize the model with random weights and biases so neurons start differently. Then place the model and training data on the same device and apply mean squared error loss.

Learn how the learning rate governs the optimizer's step size during training toward the minimum loss. Adam with 1e-4 updates model weights on Bitcoin data.

Set a batch size that balances speed and stability, typically 32, to fit GPU memory; compare stochastic gradient descent and batch gradient descent.

Track training and test losses to monitor learning and generalization, storing them during training. If training loss falls while test loss rises, the model overfits, unlike a genuine understanding.

Set the epoch count to 200 to let the model see the full training dataset multiple times, improving weights toward the best solution.

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.

Follow a training loop across epochs, creating shuffled mini-batches, performing forward propagation, computing mean squared error loss, backpropagation, and optimizer updates to gradually improve model weights.

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.

Backtest part 2 initializes equity and accuracy epochs, tracks trade equity with a 1,000 position size, and simulates buy or sell actions from open to close prices to assess profitability.

Add seven features derived from high, low, open, and close prices, plus volume, to capture returns and volatility for trading model, then scale and trim outliers to prevent volume domination.

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.

Explore how reproducibility controls randomness in model training by setting a fixed seed across NumPy, Python's built-in random, and PyTorch, and updating the seed per epoch to keep shuffling fair.

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.

Explore how long short-term memory networks address long-term dependency through forget, input, and output gates, updating cell and hidden states across time steps to predict Bitcoin prices.

Implement a PyTorch LSTM model by adding an LSTM layer with configurable input and hidden sizes, stacking layers, and using the last hidden state for a fully connected prediction.

Learn how to prevent CUDA out-of-memory errors during LSTM forward propagation on large test datasets by chunking data into smaller batches and retrying with smaller chunks.

learn how ensemble methods combine predictions from multiple models via majority vote to drive trading decisions and assess backtest drawdown and Sharpe ratio.

Combine three architectures—Conv1D, LSTM, and a hybrid model—to boost equity from 1,000 to 4,100 while keeping drawdown below 10% and a Sharpe ratio above 0.3.

Explore live trading realities on Binance, including latency, maker and taker orders, partially filled trades, and how slippage and price offsets affect execution.

Learn how Binance futures and margin trading fees operate, including maker and taker rates for USDT and USDC pairs, and how promotions and VIP levels influence backtesting a trading bot.

Discover how a machine learning trading bot uses trained models to generate buy and sell signals, place Binance orders, and operate 24/7 with a candle-bound cleanup loop.

Build a live trading bot that executes trades from model predictions on your machine with VS Code, using a Python project with Bot, Load_Scalers, Load_Models, and Binance integration 24/7 operation.

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.

Binance futures trading bot by implementing the bot class with CCXT, a logger, and live trading parameters such as sol/usdc, slippage, position size, and timing for reliable order execution.

Build a Binance futures trading bot in Python, implementing write_to_log to log messages and print to terminal, using async and ccxt connections and fetch_ohlcv candles for a machine learning model.

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.

Load loadScalars and loadModels, then run an ensemble futures trading bot for Binance. The workflow uses transScalars, numFeatures, and sequenceLength to generate actions, place orders, and track equity.

Test bot methods by creating the exchange connection, checking current position, closing the network, and exiting; then run the bot to trade live, place a sale order, and plot graph.

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.

Define price and size notional to format orders for Binance, keep model-trained symbol BTC-USDT while trading BTC-FDUSD, track position and fees, and implement last-second candle checks to avoid API spamming.

Log bot actions and establish a robust binance connection with api keys, enabling margin trading; compare asynchronous and synchronous candles retrieval, ensuring up-to-date data and fault-tolerant trading.

Create a data update loop that uses only fully closed candles via getCandles. Place limit orders with slippage control and double-size flips, then track equity and plot performance.

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.

Understand stop loss concepts by analyzing a bitcoin trade: set a predefined $150 loss, automatically exit if price moves against you to limit loss, and improve backtest performance.

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.