Machine Learning Models

Learn how to leverage machine learning for trading on Neura AI.

ML in Trading

Use Cases

• Price prediction
• Pattern recognition
• Sentiment analysis
• Risk assessment
• Portfolio optimization

Challenges

• Non-stationary data
• Low signal-to-noise ratio
• Overfitting risk
• Regime changes

Common Models

Linear Models

Simple but interpretable.

from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X_train, y_train)
predictions = model.predict(X_test)

Random Forest

Handles non-linear relationships.

from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
predictions = model.predict(X_test)

Gradient Boosting (XGBoost)

High performance for tabular data.

import xgboost as xgb

model = xgb.XGBClassifier(
    max_depth=5,
    learning_rate=0.1,
    n_estimators=100
)
model.fit(X_train, y_train)

LSTM Neural Networks

For sequential data and time series.

Feature Engineering

Price Features

• Returns (various timeframes)
• Momentum
• Volatility
• Moving averages

Technical Indicators

• RSI
• MACD
• Bollinger Bands
• ATR

Alternative Data

• Sentiment scores
• Social media metrics
• News sentiment
• On-chain data (crypto)

Model Evaluation

Metrics for Classification

• Accuracy
• Precision/Recall
• F1 Score
• AUC-ROC

Metrics for Regression

• MAE
• RMSE
• R-squared

Trading-Specific Metrics

• Sharpe Ratio
• Maximum Drawdown
• Win Rate
• Profit Factor

Neura AI ML Tools

Pre-built Models

Access trained models for:

• Price direction prediction
• Volatility forecasting
• Pattern recognition

Custom Model Training

Upload and train your own models with our infrastructure.