new NN sub-proj

crypto/brian/index.py

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+#!/usr/bin/env python3
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import asyncio
+from collections import deque
+import numpy as np
+
+# ------------------------------
+# Neural Network Architecture
+# ------------------------------
+class TradingModel(nn.Module):
+    def __init__(self, input_dim, hidden_dim, output_dim):
+        super(TradingModel, self).__init__()
+        # This is a simplified network template.
+        # A production-grade 8B model would involve model parallelism and a deep transformer or other architecture.
+        self.net = nn.Sequential(
+            nn.Linear(input_dim, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, output_dim)
+        )
+    
+    def forward(self, x):
+        return self.net(x)
+
+# ------------------------------
+# Replay Buffer for Continuous Learning
+# ------------------------------
+class ReplayBuffer:
+    def __init__(self, capacity=10000):
+        self.buffer = deque(maxlen=capacity)
+    
+    def add(self, experience):
+        self.buffer.append(experience)
+    
+    def sample(self, batch_size):
+        indices = np.random.choice(len(self.buffer), size=batch_size, replace=False)
+        return [self.buffer[i] for i in indices]
+    
+    def __len__(self):
+        return len(self.buffer)
+
+# ------------------------------
+# Feature Engineering & Indicator Calculation
+# ------------------------------
+def compute_indicators(candle, additional_data):
+    """
+    Combine candle data (H, L, O, C, V) with additional indicators.
+    In production, use proper TA libraries (e.g., TA-Lib) to compute RSI, stochastic oscillator, etc.
+    """
+    features = []
+    # Base candlestick features:
+    features.extend([
+        candle.get('open', 0.0),
+        candle.get('high', 0.0),
+        candle.get('low', 0.0),
+        candle.get('close', 0.0),
+        candle.get('volume', 0.0)
+    ])
+    
+    # Append additional indicator values (e.g., sentiment score, news volume, etc.)
+    for key, value in additional_data.items():
+        features.append(value)
+    
+    return np.array(features, dtype=np.float32)
+
+# ------------------------------
+# Simulated Live Data Streams
+# ------------------------------
+async def get_live_candle_data():
+    """
+    This function should connect to your live data feed.
+    For demonstration purposes, we simulate new candlestick data.
+    """
+    await asyncio.sleep(1)  # simulate network/data latency
+    return {
+        'open': np.random.rand(),
+        'high': np.random.rand(),
+        'low': np.random.rand(),
+        'close': np.random.rand(),
+        'volume': np.random.rand()
+    }
+
+async def get_sentiment_data():
+    """
+    Simulate fetching live sentiment data from external sources.
+    Replace this with integration to actual X feeds or news APIs.
+    """
+    await asyncio.sleep(1)
+    return {
+        'sentiment_score': np.random.rand(),  # e.g., normalized sentiment between 0 and 1
+        'news_volume': np.random.rand(),
+        'social_engagement': np.random.rand()
+    }
+
+# ------------------------------
+# RL Agent with Continuous Training
+# ------------------------------
+class ContinuousRLAgent:
+    def __init__(self, model, optimizer, replay_buffer, batch_size=32):
+        self.model = model
+        self.optimizer = optimizer
+        self.replay_buffer = replay_buffer
+        self.batch_size = batch_size
+        # Placeholder loss function; a real-world RL agent often has a more complex loss (e.g., Q-learning loss)
+        self.loss_fn = nn.MSELoss()  
+    
+    def act(self, state):
+        """
+        Compute the action given the latest state.
+        In production, the network output should map to a confidence or Q-values for actions.
+        Action mapping (for example): 0: SELL, 1: HOLD, 2: BUY.
+        """
+        state_tensor = torch.tensor(state, dtype=torch.float32).unsqueeze(0)
+        with torch.no_grad():
+            output = self.model(state_tensor)
+        action = torch.argmax(output, dim=1).item()  
+        return action
+
+    def train_step(self):
+        """
+        Perform one training step using a batch sampled from the replay buffer.
+        In RL, targets are computed from rewards and estimated future returns.
+        """
+        if len(self.replay_buffer) < self.batch_size:
+            return
+        
+        batch = self.replay_buffer.sample(self.batch_size)
+        states, rewards, next_states, dones = [], [], [], []
+        
+        for experience in batch:
+            state, reward, next_state, done = experience
+            states.append(state)
+            rewards.append(reward)
+            next_states.append(next_state)
+            dones.append(done)
+        
+        states_tensor = torch.tensor(states, dtype=torch.float32)
+        targets_tensor = torch.tensor(rewards, dtype=torch.float32).unsqueeze(1)
+        
+        outputs = self.model(states_tensor)
+        # For simplicity, assume we use a single output value to represent the signal.
+        predictions = outputs[:, 0].unsqueeze(1)
+        loss = self.loss_fn(predictions, targets_tensor)
+        
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+
+# ------------------------------
+# Trading Bot: Integration with Jupiter API (Solana)
+# ------------------------------
+class TradingBot:
+    def __init__(self, rl_agent):
+        self.rl_agent = rl_agent
+        # Initialize Jupiter API client for Solana trading
+        # Hypothetical client initialization (substitute with an actual library/client):
+        # self.jupiter_client = JupiterClient(api_key='YOUR_API_KEY')
+    
+    async def execute_trade(self, action):
+        """
+        Translate the agent's selected action into a trade order.
+        Action mapping example: 0 => SELL, 1 => HOLD, 2 => BUY.
+        """
+        if action == 0:
+            print("Executing SELL order")
+            # self.jupiter_client.sell(...actual trade parameters...)
+        elif action == 2:
+            print("Executing BUY order")
+            # self.jupiter_client.buy(...actual trade parameters...)
+        else:
+            print("Holding position")
+    
+    async def trading_loop(self):
+        """
+        Main trading loop:
+          • Ingest live data.
+          • Compute features.
+          • Let the agent decide on an action.
+          • Execute trades.
+          • Store experience and train continuously.
+        """
+        while True:
+            # Fetch latest data (you might aggregate data for different time frames)
+            candle = await get_live_candle_data()
+            sentiment = await get_sentiment_data()
+            # In practice, merge technical indicators computed on candle data with sentiment data.
+            indicators = sentiment  # For demo, sentiment is our extra feature set.
+            
+            # Compute state features
+            state = compute_indicators(candle, indicators)
+            # Get an action from the RL agent (0: Sell, 1: Hold, 2: Buy)
+            action = self.rl_agent.act(state)
+            await self.execute_trade(action)
+            
+            # Simulate reward computation (in reality, your reward function should be based on trading performance)
+            reward = np.random.rand()
+            next_state = state  # For demonstration, we reuse the state; in practice, next_state is computed after action execution.
+            done = False     # Flag to indicate episode termination if needed
+            
+            # Store the experience in the replay buffer
+            self.rl_agent.replay_buffer.add((state, reward, next_state, done))
+            # Run a training step to update the network continuously
+            self.rl_agent.train_step()
+            
+            # Sleep to conform to the data frequency (adjust the delay as needed)
+            await asyncio.sleep(0.5)
+
+# ------------------------------
+# Main Orchestration Loop
+# ------------------------------
+async def main_loop():
+    # Define dimensions. For instance: 5 for basic candlestick data + additional channels (e.g., 3 here; expand as necessary)
+    input_dim = 5 + 3  # Adjust this to support up to 100 additional indicator channels
+    hidden_dim = 128   # Placeholder; for an 8B parameter model, this will be much larger and distributed.
+    output_dim = 3     # Action space: Sell, Hold, Buy
+    
+    model = TradingModel(input_dim, hidden_dim, output_dim)
+    optimizer = optim.Adam(model.parameters(), lr=1e-4)
+    replay_buffer = ReplayBuffer(capacity=10000)
+    
+    rl_agent = ContinuousRLAgent(model, optimizer, replay_buffer, batch_size=32)
+    trading_bot = TradingBot(rl_agent)
+    
+    # Start the continuous trading loop
+    await trading_bot.trading_loop()
+
+if __name__ == "__main__":
+    asyncio.run(main_loop())