Code

```python
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor

# Load historical forex data
data = pd.read_csv('forex_data.csv')

# Feature engineering
data['momentum'] = data['close'].diff()
data['rsi'] = talib.RSI(data['close'], timeperiod=14)
data['macd'], data['signal'], data['hist'] = talib.MACD(data['close'], fastperiod=12, slowperiod=26, signalperiod=9)

# Split data into training and testing sets
X_train = data[:-100][['momentum', 'rsi', 'macd', 'signal', 'hist']]
y_train = data[:-100]['close']
X_test = data[-100:][['momentum', 'rsi', 'macd', 'signal', 'hist']]
y_test = data[-100:]['close']

# Train the random forest model
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = model.predict(X_test)

# Evaluate the model
print(f'Mean Squared Error: {mean_squared_error(y_test, y_pred)}')
print(f'R-squared: {r2_score(y_test, y_pred)}')
```

This code snippet demonstrates the use of a Random Forest Regressor model to predict forex prices based on technical indicators like momentum, RSI, and MACD. The model is trained on historical forex data and then evaluated on a test set. This type of approach can be a starting point for building a more sophisticated forex trading bot.