1.1 AiTradeBot Overview: Algorithms and Prediction
AiTradeBot is a tool designed to help traders and investors make informed decisions by analyzing market data and predicting promising cryptocurrencies and tokens for investment. The AiTradeBot leverages machine learning algorithms, including time-series analysis and neural networks, to forecast trends based on historical and real-time market data.
1.2 Machine Learning Model Setup
This section outlines the setup for an ML model to power AiTradeBot’s prediction capabilities. The model in this example will use a recurrent neural network (RNN), which is suited for time-series forecasting in financial data.
1.2.1 Prerequisites
Python (version 3.8 or later)
TensorFlow (for machine learning model)
Pandas (for data manipulation)
NumPy (for numerical computations)
Install required libraries:
pip install tensorflow pandas numpy
1.2.2 Data Collection and Preparation
To train the model, we'll use historical price data. Here’s how to collect and prepare the data in a time-series format.
import pandas as pd
import numpy as np
# Load historical data (sample data for illustration)
# Data should include columns like 'timestamp', 'open', 'high', 'low', 'close', and 'volume'
data = pd.read_csv('historical_data.csv', parse_dates=['timestamp'], index_col='timestamp')
# Create features and labels for the model
data['future_price'] = data['close'].shift(-1) # Next time step’s close price as the target
data = data.dropna() # Remove NaN values
features = data[['open', 'high', 'low', 'close', 'volume']].values
targets = data['future_price'].values
1.3 Model Configuration and Training
This section provides sample code for setting up and training a simple LSTM-based model for price prediction. LSTM (Long Short-Term Memory) is effective for sequential and time-series data, making it a good choice for predicting trends.
1.3.1 Model Setup
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM, Dropout
# Model setup
model = Sequential([
LSTM(64, input_shape=(features.shape[1], 1), return_sequences=True),
Dropout(0.2),
LSTM(64, return_sequences=False),
Dropout(0.2),
Dense(1) # Output layer for the predicted future price
])
model.compile(optimizer='adam', loss='mean_squared_error')
model.summary()
1.3.2 Data Preparation for LSTM Model
LSTMs expect a 3D input of shape (samples, time steps, features). Here’s how to reshape the data:
# Reshape data to match LSTM input
X_train = np.reshape(features, (features.shape[0], features.shape[1], 1))
y_train = targets
# Split data for training and validation (80/20 split)
split_index = int(len(X_train) * 0.8)
X_train, X_val = X_train[:split_index], X_train[split_index:]
y_train, y_val = y_train[:split_index], y_train[split_index:]
1.3.3 Training the Model
# Train the model
history = model.fit(
X_train, y_train,
epochs=50,
batch_size=32,
validation_data=(X_val, y_val)
)
# Save the trained model for later use
model.save('ai_tradebot_model.h5')
1.4 Making Predictions with the Model
After training, use the model to predict the next price movement. Here’s how to load the model and make predictions with new data.
# Load the model
model = tf.keras.models.load_model('ai_tradebot_model.h5')
# Sample data for prediction (must match the shape of the training input)
sample_data = np.array([[[34000], [34150], [33900], [34050], [1000]]])
predicted_price = model.predict(sample_data)
print("Predicted price for next time step:", predicted_price[0][0])
1.5 Integrating with AiTradeBot System
AiTradeBot should perform the following steps within the trading environment:
Data Collection: Retrieve real-time data through APIs or WebSocket.
Data Preprocessing: Ensure incoming data matches the training data format.
Prediction: Use the trained model to generate predictions.
Signal Generation: Trigger trading signals based on model predictions.
1.6 Sample Code for Signal Generation
# Signal generation based on predicted movement
THRESHOLD = 1.5 # Define a threshold percentage for generating signals
def generate_signal(current_price, predicted_price):
percentage_change = ((predicted_price - current_price) / current_price) * 100
if percentage_change > THRESHOLD:
return "Buy Signal"
elif percentage_change < -THRESHOLD:
return "Sell Signal"
else:
return "Hold Signal"
# Example usage
current_price = 34000 # Current price from real-time data
predicted_price = predicted_price[0][0]
signal = generate_signal(current_price, predicted_price)
print("Trading Signal:", signal)
