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

Strategies based on the assumption that "the trend is your friend."

Traditional algos relied on rule-based logic (if RSI < 30: buy). Machine Learning replaces fixed rules with probabilistic models trained on historical patterns. The typical ML pipeline in this course includes:

from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
scaler = MinMaxScaler()
scaled = scaler.fit_transform(data[features]) Algorithmic Trading A-Z with Python- Machine Le...
Using libraries like Stable-Baselines3, a trader defines a state (current portfolio, price trends), action (buy/sell/hold), and reward (PnL change). The agent learns an optimal policy through trial-and-error in a simulated environment.
Monitor your bot live:
import streamlit as st
st.title("Live Algo Trader")
st.line_chart(df['Portfolio_Value'])
st.metric("Current PnL", f"$pnl", delta=f"pnl_pct%")

ML models are only as good as the data fed into them. In finance, raw price is rarely enough.
X = df[['rsi']]
y = (df['target'] > 0).astype(int)
split = int(0.8*len(X))
model = RandomForestClassifier().fit(X[:split], y[:split]) Strategies based on the assumption that "the trend
sharpe = test_data['strategy_returns'].mean() / test_data['strategy_returns'].std() * (252**0.5)
cumulative = (1 + test_data['strategy_returns']).cumprod()
print(f"Sharpe Ratio: sharpe:.2f")