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