Algorithmic Trading A-z With Python- Machine Le...

For price sequences, LSTMs capture long-term dependencies. Using TensorFlow/Keras:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout
def create_lstm_dataset(data, lookback=60):
X, y = [], []
for i in range(lookback, len(data)):
X.append(data[i-lookback:i])
y.append(data[i])
return np.array(X), np.array(y)
split_idx = int(len(X) * 0.8)
X_train, X_test = X[:split_idx], X[split_idx:]
y_train, y_test = y[:split_idx], y[split_idx:]
Instead of predicting price, teach an agent to maximize equity curve. Using Stable-Baselines3: Algorithmic Trading A-Z with Python- Machine Le...
import gym
from stable_baselines3 import PPO
class TradingEnv(gym.Env):
# Define state (portfolio, prices), actions (buy/sell/hold), rewards (PnL)
pass
env = TradingEnv(data)
model = PPO('MlpPolicy', env, verbose=1)
model.learn(total_timesteps=10000)

A complete guide binds the code and models into a rigorous workflow.
Before diving into AI, one must understand the rule-based strategies that have governed markets for decades.
Let's write the Python code to fetch and prepare data. For price sequences, LSTMs capture long-term dependencies
import pandas as pd
import yfinance as yf
import numpy as np
import yfinance as yf
import pandas as pd
data['ML_Signal'] = 0
data.loc[X_test.index, 'ML_Signal'] = y_pred  # Only trade on predictions