README for Bittensor Time-Series Prediction Subnet (TSPS)

🛑 Under Development – @BeeChains on Replit ⚠

Introduction

The Bittensor Time-Series Prediction Subnet (TSPS) is a state-of-the-art forecasting tool designed to predict the future trends of Bittensor ($TAO) and other financial markets, starting with Bittensor ($TAO) price movements. TSPS utilizes advanced machine learning techniques, specifically LSTM (Long Short-Term Memory) networks, for accurate time-series prediction.

Features

• Real-time Bittensor ($TAO) price prediction.
• Utilizes LSTM networks for high accuracy.
• Incorporates latest market data for predictions.
• User-friendly predictions interface.

Installation Guide

Prerequisites:

• Python 3.8 or later.
• Pip (Python package manager).

Environment Setup:

1. Clone the TSPS repository:

bash

git clone https://github.com/your-repo/TSPS.git

cd TSPS

2. Create and activate a virtual environment (optional but recommended):

python -m venv tsp-env

source tsp-env/bin/activate  # On Windows use `tsp-env\Scripts\activate`

3. pip install -r requirements.txt

pip install -r requirements.txt

Usage

Running the Prediction Model:

1. Open a terminal in the TSPS directory.
2. Run the main script:

python tsp_model.py

Fetching Real-time Data:

• The system automatically fetches the latest Bittensor ($TAO) price data for prediction.

Viewing Predictions:

• The predicted prices are displayed in the console.
• For a more detailed view, access the predictions dashboard (if available).

Advanced Usage:

• For advanced users, the model parameters can be tweaked in the tsp_model.py script.
• Additional data sources and features can be integrated for more complex predictions.

Support and Contributions

For support, please open an issue in the GitHub repository. Contributions to the TSPS project are welcome. Please submit a pull request with your proposed changes.

License

TSPS is released under the MIT License. See the LICENSE file in the repository for more details.

Step 1: Understanding the Original Time-Series Prediction Subnet

To tailor the existing Bitcoin prediction subnet from the provided GitHub example to predict Bittensor ($TAO), we first need to understand its structure and functionality.

1. Data Collection: The current subnet likely collects and processes Bitcoin price data.
2. Model Architecture: It uses a machine learning model, possibly an LSTM or another time-series model.
3. Training and Evaluation: The model is trained on historical data and evaluated on its prediction accuracy.
4. API Integration: It might use APIs to fetch real-time data for predictions.

Step 2: Adapting to Bittensor ($TAO) Time-Series Prediction

We will follow a similar structure but focus on Bittensor ($TAO) data.

1. Data Source: Identify a reliable data source for Bittensor ($TAO) historical prices.
2. Model Retuning: Modify the model’s input dimensions to accommodate the new data.
3. API Changes: Update the API calls to fetch Bittensor ($TAO) data instead of Bitcoin.
4. Evaluation Metrics: Ensure that the evaluation metrics are relevant for Bittensor ($TAO) predictions.

Step 3: Writing the Code

3.1 Data Collection

First, we need a Python script to collect Bittensor ($TAO) price data. We can use a financial data API like Alpha Vantage or a cryptocurrency API.

python:

import requests

import pandas as pd



def fetch_tao_data(api_key):

    url = f"https://api.example.com/query?function=TIME_SERIES&symbol=TAO&apikey={api_key}"

    response = requests.get(url)

    data = response.json()

    df = pd.DataFrame(data['Time Series (Digital Currency Daily)']).T

    df = df['4a. close (USD)'].astype(float)

    return df



api_key = 'YOUR_API_KEY'

tao_data = fetch_tao_data(api_key)

print(tao_data.head())

3.2 Model Architecture

We’ll use an LSTM model, which is suitable for time-series predictions. PyTorch will be used for building the model.

import torch

import torch.nn as nn



class TSPSModel(nn.Module):

    def __init__(self, input_size, hidden_layer_size, output_size):

        super(TSPSModel, self).__init__()

        self.hidden_layer_size = hidden_layer_size

        self.lstm = nn.LSTM(input_size, hidden_layer_size)

        self.linear = nn.Linear(hidden_layer_size, output_size)

        self.hidden_cell = (torch.zeros(1,1,self.hidden_layer_size),

                            torch.zeros(1,1,self.hidden_layer_size))



    def forward(self, input_seq):

        lstm_out, self.hidden_cell = self.lstm(input_seq.view(len(input_seq) ,1, -1), self.hidden_cell)

        predictions = self.linear(lstm_out.view(len(input_seq), -1))

        return predictions[-1]

3.3 Training the Model

Training involves feeding the historical data of Bittensor ($TAO) into the model and optimizing it.

# Assuming tao_data is a pandas DataFrame with the price data



# Convert data to PyTorch tensors and reshape for LSTM

train_data = torch.FloatTensor(tao_data.values).view(-1)



# Define the model

model = TSPSModel(input_size=1, hidden_layer_size=100, output_size=1)

loss_function = nn.MSELoss()

optimizer = torch.optim.Adam(model.parameters(), lr=0.001)



# Training loop

epochs = 150

for i in range(epochs):

    for seq, labels in train_data:

        optimizer.zero_grad()

        model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),

                        torch.zeros(1, 1, model.hidden_layer_size))



        y_pred = model(seq)



        single_loss = loss_function(y_pred, labels)

        single_loss.backward()

        optimizer.step()



    if i%25 == 1:

        print(f'epoch: {i:3} loss: {single_loss.item():10.8f}')

3.4 Prediction and Evaluation

Use the trained model to predict future trends and evaluate its performance.

# Predict future prices

future_days = 5

for i in range(future_days):

    seq = torch.FloatTensor(tao_data[-window_size:])

    with torch.no_grad():

        model.hidden = (torch.zeros(1, 1, model.hidden_layer_size),

                        torch.zeros(1, 1, model.hidden_layer_size))

        tao_data.append(model(seq).item())



# Evaluate the model's predictions

# This could involve comparing the predicted values with actual future values

Step 4: Deployment

After testing and ensuring the model’s accuracy, deploy it as part of the subnet. This involves integrating the model into the existing subnet infrastructure.

This guide provides a high-level overview. The actual implementation would require fine-tuning and consideration of the specific requirements of the Bittensor ($TAO) subnet infrastructure.

Step 5: Fine-Tuning the Model

To optimize the LSTM model for Bittensor ($TAO) predictions, consider experimenting with:

1. Hyperparameters: Adjust the number of layers, hidden layer size, learning rate, etc.
2. Feature Engineering: Besides the price, consider other features that might influence Bittensor’s price, like trading volume, market sentiment, etc.
3. Data Normalization: Normalize the data to improve the training process.

# Example of data normalization

from sklearn.preprocessing import MinMaxScaler



scaler = MinMaxScaler(feature_range=(-1, 1))

tao_data_normalized = scaler.fit_transform(tao_data.values.reshape(-1, 1))

Step 6: Improving Data Collection

For accurate predictions, the quality and quantity of data are crucial. Ensure you have:

• A reliable and up-to-date source of Bittensor ($TAO) data.
• Sufficient historical data to capture various market conditions.

Step 7: Implementing Real-time Data Fetching

Integrate real-time data fetching in the subnet to continually update the model with new data. This ensures the predictions are based on the latest market trends.

# Example of a function to fetch real-time data

def fetch_real_time_tao_data(api_key):

    # Implement API call for real-time data

    pass

Step 8: Backtesting

Before deploying, backtest the model with historical data to evaluate its predictive accuracy.

# Backtesting logic

# Compare the model's predictions with actual historical data

Step 9: Deployment and Integration

Deploy the trained model within the subnet infrastructure. This may involve:

• Setting up a server or cloud environment for the model.
• Integrating the model with the existing Bittensor infrastructure.

Step 10: Monitoring and Maintenance

After deployment:

• Continuously monitor the model’s performance.
• Periodically retrain the model with new data.
• Adjust the model as needed to maintain accuracy.

Conclusion

This guide sets the foundation for building a TSPS for Bittensor ($TAO). The next steps involve detailed implementation, testing, and deployment.

Detailed Coding Implementation

1. Data Collection and Processing

We’ll begin by setting up a python script to collect and preprocess Bittensor ($TAO) data.

python

import requests

import pandas as pd

from sklearn.preprocessing import MinMaxScaler



def fetch_tao_data(api_key, symbol="TAO"):

    url = f"https://www.alphavantage.co/query?function=DIGITAL_CURRENCY_DAILY&symbol={symbol}&market=USD&apikey={api_key}"

    response = requests.get(url)

    data = response.json()

    df = pd.DataFrame(data['Time Series (Digital Currency Daily)']).T

    df = df[['4a. close (USD)']].astype(float)

    return df



def preprocess_data(data):

    scaler = MinMaxScaler(feature_range=(-1, 1))

    scaled_data = scaler.fit_transform(data.values.reshape(-1, 1))

    return scaled_data, scaler



api_key = 'YOUR_API_KEY'

tao_data = fetch_tao_data(api_key)

tao_data_normalized, scaler = preprocess_data(tao_data)

2. LSTM Model for Time-Series Prediction

Next, we’ll define the LSTM model for time-series prediction.

python

import torch

import torch.nn as nn



class TSPSModel(nn.Module):

    def __init__(self, input_size=1, hidden_layer_size=100, output_size=1):

        super(TSPSModel, self).__init__()

        self.hidden_layer_size = hidden_layer_size

        self.lstm = nn.LSTM(input_size, hidden_layer_size)

        self.linear = nn.Linear(hidden_layer_size, output_size)

        self.hidden = (torch.zeros(1,1,self.hidden_layer_size),

                       torch.zeros(1,1,self.hidden_layer_size))



    def forward(self, input_seq):

        lstm_out, self.hidden = self.lstm(input_seq.view(len(input_seq), 1, -1), self.hidden)

        predictions = self.linear(lstm_out.view(len(input_seq), -1))

        return predictions[-1]

3. Training the Model

Preparing Data for Training

First prepare the data for training, then define the training loop.

python

import numpy as np



def create_inout_sequences(input_data, tw):

    inout_seq = []

    L = len(input_data)

    for i in range(L-tw):

        train_seq = input_data[i:i+tw]

        train_label = input_data[i+tw:i+tw+1]

        inout_seq.append((train_seq ,train_label))

    return inout_seq



train_window = 12  # You can tune this window size

train_inout_seq = create_inout_sequences(tao_data_normalized, train_window)

We will train the model using the normalized Bittensor ($TAO) data.

Training Loop

python

def train_model(model, train_data, epochs=150, lr=0.001):

    loss_function = nn.MSELoss()

    optimizer = torch.optim.Adam(model.parameters(), lr=lr)



    for i in range(epochs):

        for seq, labels in train_data:

            optimizer.zero_grad()

            model.hidden = (torch.zeros(1, 1, model.hidden_layer_size),

                            torch.zeros(1, 1, model.hidden_layer_size))



            y_pred = model(seq)



            single_loss = loss_function(y_pred, torch.FloatTensor(labels))

            single_loss.backward()

            optimizer.step()



        if i % 25 == 1:

            print(f'epoch {i} loss: {single_loss.item()}')



model = TSPSModel()

train_model(model, train_inout_seq)

This completes the training part of the model. You might need to adjust the number of epochs, learning rate, and training window based on the performance and accuracy of the model.

Now, proceed by integrating the trained model into a practical application for forecasting Bittensor ($TAO) prices. This involves setting up a pipeline that fetches the latest data, preprocesses it, feeds it into the model, and then outputs the prediction.

Step 1: Fetching Latest Data

You’ll need a function that fetches the most recent data for Bittensor ($TAO). This should be similar to the fetch_tao_data function but might only retrieve the latest available data point or a small window of recent data.

Step 2: Preprocessing

The new data must be preprocessed in the same way as the training data. This means scaling it with the same MinMaxScaler instance used during training.

Step 3: Making Predictions

With the latest data preprocessed, you can then feed it into the model to make a prediction.

Step 4: Outputting Predictions

Finally, the model’s output (the predicted price) should be presented in a user-friendly format, possibly reverted from the scaled value to the actual price prediction.

python

def predict_next_day_price(model, latest_data, scaler):

    model.eval()  # Set the model to evaluation mode



    # Preprocess latest_data with the same scaler used in training

    scaled_data = scaler.transform(latest_data.reshape(-1, 1))



    # Convert to tensor

    data_tensor = torch.FloatTensor(scaled_data).view(-1)



    # Make prediction

    with torch.no_grad():

        model.hidden = (torch.zeros(1, 1, model.hidden_layer_size),

                        torch.zeros(1, 1, model.hidden_layer_size))

        prediction = model(data_tensor)



    # Revert scaling to get actual price prediction

    actual_prediction = scaler.inverse_transform(prediction.numpy().reshape(-1, 1))

    return actual_prediction[0][0]



# Example usage

latest_data = np.array([/* latest Bittensor ($TAO) prices here */])

predicted_price = predict_next_day_price(model, latest_data, scaler)

print(f"Predicted Bittensor ($TAO) price for next day: {predicted_price}")

This code provides a complete end-to-end example of using your trained model to make a prediction. Remember to replace the placeholder in ‘latest_data‘ with actual real-time data.

Integrating the model into a continuous service that periodically fetches the latest Bittensor ($TAO) data, processes it, and updates the predictions. This can be done using a scheduled job or a live-streaming data pipeline, depending on the requirements and infrastructure.

Setting Up a Scheduled Job

1. Cron Job: Set up a cron job or a scheduled task that runs the prediction script at regular intervals (e.g., daily).
2. Automated Data Fetching: Modify the data fetching script to automatically retrieve the latest data at each run.
3. Logging Predictions: Store or log the predictions for further analysis or real-time dashboard display.

Example Cron Job Setup

bash

# Add this line to your crontab to run the prediction script every day at a specific time

0 0 * * * /path/to/python /path/to/predict_next_day_price.py

Setting Up a Live-Streaming Data Pipeline

1. Real-Time Data Stream: Use a service or API that provides real-time streaming of Bittensor ($TAO) price data.
2. Stream Processing: Process the streaming data and feed it into the model as it arrives.
3. Continuous Predictions: The model makes predictions based on the latest data and updates the output in real-time.

Monitoring and Maintenance

• Performance Monitoring: Regularly check the accuracy of the predictions and the health of the system.
• Model Retraining: Periodically retrain the model with new data to maintain its accuracy over time.
• Alerts and Notifications: Implement alerts for system failures or significant prediction changes.

Deployment Considerations

• Cloud or On-Premise: Decide whether to deploy on the cloud for scalability or on-premise for control.
• Security: Ensure secure data handling and model access.
• Scalability: Design the system to handle increased data volume or frequency.

Conclusion

This setup completes the implementation of a Bittensor ($TAO) Time-Series Prediction Subnet. It’s a robust system that can adapt to new data, providing up-to-date forecasts.

Sources: Grimoire custom GPT

This work was inspired while viewing Taoshi.io and Subnet 8 at Taoshidev

🛑 Under Development – @BeeChains on Replit ⚠

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