Allora: A Comprehensive Guide to Mastering Cryptocurrency Price Prediction with BiRNN

"Mastering Cryptocurrency Price Prediction with BiRNN: A Comprehensive Guide from Model Creation to Real-Time Deployment"

Introduction Predicting cryptocurrency prices is notoriously difficult due to the market's volatile and dynamic nature. However, advances in deep learning, particularly in Recurrent Neural Networks (RNNs) and their variants like Bidirectional RNNs (BiRNNs), provide us with the tools to model these complex patterns. This guide will walk you through the principles and practical steps for building, training, and deploying a BiRNN model for cryptocurrency price prediction.

Section 1: Understanding the BiRNN Model

What is a BiRNN? A Bidirectional Recurrent Neural Network (BiRNN) is an extension of a standard Recurrent Neural Network (RNN) that processes data in both forward and backward directions. This allows the model to capture dependencies from both past and future states, making it particularly useful for time-series predictions like cryptocurrency prices.

Understanding Time Series Data Time series data consists of sequential data points collected or recorded at specific time intervals. For cryptocurrencies, this typically includes prices at regular intervals (e.g., every minute, hour, or day). Understanding key characteristics like trend, seasonality, and noise is crucial when building a predictive model.
Introduction to Recurrent Neural Networks (RNNs) RNNs are a type of neural network designed specifically to handle sequential data. They are particularly useful for time series data because they can maintain a "memory" of previous inputs, allowing them to capture temporal dependencies.

Key Features of RNNs:

Hidden State: Stores information from previous time steps.
Sequential Processing: Processes data in order, considering the context provided by previous steps. However, standard RNNs can struggle with long-term dependencies due to issues like vanishing gradients, making them less effective for long sequences.

Benefits of BiRNNs:

Enhanced Context Understanding: Captures more comprehensive information by considering data in both directions.
Improved Accuracy: Often outperforms unidirectional RNNs in tasks requiring detailed context analysis

Building and Training a BiRNN Model

Implementation Example:

import torch
import torch.nn as nn

class BiRNNModel(nn.Module):
    def __init__(self, input_size, hidden_layer_size, output_size, num_layers, dropout):
        super(BiRNNModel, self).__init__()
        self.rnn = nn.RNN(input_size, hidden_layer_size, num_layers=num_layers, 
                          dropout=dropout, batch_first=True, bidirectional=True)
        self.linear = nn.Linear(hidden_layer_size * 2, output_size)

    def forward(self, input_seq):
        h0 = torch.zeros(self.num_layers * 2, input_seq.size(0), self.hidden_layer_size)
        rnn_out, _ = self.rnn(input_seq, h0)
        predictions = self.linear(rnn_out[:, -1])
        return predictions

Data Collection and Preprocessing Data is collected using the Binance API, providing historical price data for various cryptocurrencies. The data is then preprocessed, typically involving:

Scaling: Normalizing data using techniques like Min-Max Scaling.
Feature Engineering: Creating features like moving averages or price changes that might help the model learn better.

Fetching Data:

def get_binance_data(symbols, interval="1m", limit=1000):
    # Function to fetch data from Binance
    ...

Training the Model We train the BiRNN model using historical data. The training process involves:

Loss Function: Using Mean Squared Error (MSE) to quantify the prediction error.
Optimizer: Utilizing Adam or similar optimizers to minimize the loss function.
Training Loop: Iterating over the data, updating the model's parameters based on the gradients computed from the loss.

Training Example:

model = BiRNNModel(input_size=1, hidden_layer_size=128, output_size=1, num_layers=2, dropout=0.3)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=1e-5)

for epoch in range(num_epochs):
    model.train()
    optimizer.zero_grad()
    output = model(train_seq)
    loss = criterion(output, train_labels)
    loss.backward()
    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)  # Gradient clipping
    optimizer.step()

Fine-Tuning and Optimizing the Model To improve model performance, consider fine-tuning the following aspects:

Hidden Layer Size: Adjust to capture more complex patterns.
Number of Layers: More layers can model deeper patterns but may require careful tuning to avoid overfitting.
Learning Rate: Fine-tuning this can improve training stability and convergence.
Dropout Rate: Helps prevent overfitting by randomly dropping neurons during training.
Gradient Clipping: Prevents exploding gradients by capping the gradients during backpropagation.

Example of Gradient Clipping:

torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)

Section 2: Building the BiRNN Model

Setting Up the Environment First, ensure you have all the necessary libraries installed. You'll need torch, pandas, sklearn, and requests for this project. Install them using pip:

pip install torch pandas scikit-learn requests flask

Defining the BiRNN Model (model.py) Here's how you can define the BiRNN model:

import torch
import torch.nn as nn
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
import joblib

class BiRNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size, num_layers, dropout):
        super(BiRNN, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.rnn = nn.RNN(input_size, hidden_size, num_layers, dropout=dropout, batch_first=True, bidirectional=True)
        self.fc = nn.Linear(hidden_size * 2, output_size)  # Multiply by 2 for bidirectional

    def forward(self, x):
        h0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size)  # Multiply by 2 for bidirectional
        out, _ = self.rnn(x, h0)
        out = self.fc(out[:, -1, :])
        return out

Section 3: Training the BiRNN Model

Fetching Data from Binance API Use the following function to fetch historical data for the tokens:

import requests

def get_binance_data(symbols=["ETHUSDT", "BTCUSDT", "BNBUSDT", "SOLUSDT", "ARBUSDT"], interval="1m", limit=1000):
    data_frames = {}
    for symbol in symbols:
        url = f"https://api.binance.com/api/v3/klines?symbol={symbol}&interval={interval}&limit={limit}"
        response = requests.get(url)
        data = response.json()
        df = pd.DataFrame(data, columns=[
            "open_time", "open", "high", "low", "close", "volume",
            "close_time", "quote_asset_volume", "number_of_trades",
            "taker_buy_base_asset_volume", "taker_buy_quote_asset_volume", "ignore"
        ])
        df["close"] = df["close"].astype(float)
        data_frames[symbol] = df[["close"]]
    return data_frames

Preparing Data for Training Prepare the data by scaling it:

data_frames = get_binance_data()
scaler = MinMaxScaler(feature_range=(-1, 1))
scaled_data = {symbol: scaler.fit_transform(df.values) for symbol, df in data_frames.items()}

# Save the scaler for later use
joblib.dump(scaler, "scaler.save")

Training the Model Train the BiRNN model using the following code:

model = BiRNN(input_size=1, hidden_size=115, output_size=1, num_layers=2, dropout=0.3)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
num_epochs = 100

for epoch in range(num_epochs):
    for symbol, data in scaled_data.items():
        seq = torch.FloatTensor(data).view(1, -1, 1)
        labels = torch.FloatTensor(data[-1]).view(1, -1)
        
        optimizer.zero_grad()
        y_pred = model(seq)
        loss = criterion(y_pred, labels)
        loss.backward()
        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
        optimizer.step()

# Save the model and scaler
torch.save(model.state_dict(), "birnn_model.pth")

Section 4: Integrating the Model into the Flask Application

Loading the Model and Scaler in app.py Modify your app.py to load the trained BiRNN model:

from flask import Flask, Response
import torch
import joblib
import requests
import pandas as pd

app = Flask(__name__)

class BiRNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size, num_layers, dropout):
        super(BiRNN, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.rnn = nn.RNN(input_size, hidden_size, num_layers, dropout=dropout, batch_first=True, bidirectional=True)
        self.fc = nn.Linear(hidden_size * 2, output_size)

    def forward(self, x):
        h0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size)
        out, _ = self.rnn(x, h0)
        out = self.fc(out[:, -1, :])
        return out

model = BiRNN(input_size=1, hidden_size=115, output_size=1, num_layers=2, dropout=0.3)
model.load_state_dict(torch.load("birnn_model.pth"))
model.eval()

scaler = joblib.load("scaler.save")

Making Predictions

def get_binance_url(symbol="BTCUSDT", interval="1m", limit=1000):
    return f"https://api.binance.com/api/v3/klines?symbol={symbol}&interval={interval}&limit={limit}"

@app.route("/inference/<string:token>")
def get_inference(token):
    token = token.upper()
    url = get_binance_url(symbol=token + "USDT")
    response = requests.get(url)
    
    if response.status_code == 200:
        data = response.json()
        df = pd.DataFrame(data, columns=[
            "open_time", "open", "high", "low", "close", "volume",
            "close_time", "quote_asset_volume", "number_of_trades",
            "taker_buy_base_asset_volume", "taker_buy_quote_asset_volume", "ignore"
        ])
        df["close"] = df["close"].astype(float)
        scaled_data = scaler.transform(df["close"].values.reshape(-1, 1))
        seq = torch.FloatTensor(scaled_data).view(1, -1, 1)
        
        with torch.no_grad():
            y_pred = model(seq)
        predicted_price = scaler.inverse_transform(y_pred.numpy())
        return Response(str(round(predicted_price.item(), 2)), status=200, mimetype='application/json')
    else:
        return Response("Failed to retrieve data", status=500, mimetype='application/json')

if __name__ == "__main__":
    app.run(host='0.0.0.0', port=8000)

Build and Run Your Worker Node:

docker compose up --build -d

Your Allora Worker Node is now ready to handle requests for real-time cryptocurrency price predictions.

Key Steps:

Model Loading: The pre-trained model is loaded into memory when the Flask app starts.
Data Fetching: Historical data is retrieved for the requested cryptocurrency.
Inference: The BiRNN model processes the data and outputs a predicted price.
Response: The prediction is returned as a JSON response.

Full Code Sample:

Full Code Sample Model.py: https://github.com/0xtnpxsgt/Allora-Comprehensive-Guide/blob/main/birnn_model.py
Full Code Sample App.py: https://github.com/0xtnpxsgt/Allora-Comprehensive-Guide/blob/main/app.py

Conclusion