#!/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())