#!/usr/bin/env python3 import asyncio import os import ccxt.async_support as ccxt import torch import torch.nn as nn import torch.optim as optim import numpy as np from collections import deque # ------------------------------ # Neural Network Architecture # ------------------------------ class TradingModel(nn.Module): def __init__(self, input_dim, hidden_dim, output_dim): super(TradingModel, self).__init__() # This is a simplified feed-forward model. # A production-grade 8B parameter model would need a distributed strategy. 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 basic OHLCV candle data with additional sentiment/indicator data. In production, you might use dedicated libraries (like TA‑Lib) to calculate RSI, stochastic, and support up to 100 channels. """ features = [] # Base candle features: open, high, low, close, volume 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 indicators (e.g., sentiment, news volume, etc.) for key, value in additional_data.items(): features.append(value) return np.array(features, dtype=np.float32) # ------------------------------ # Data Ingestion from MEXC: Live Candle Data # ------------------------------ async def get_live_candle_data(exchange, symbol, timeframe='1m'): """ Fetch the latest OHLCV candle for the given symbol and timeframe. MEXC (or other exchanges via ccxt) returns a list of candles: [ timestamp, open, high, low, close, volume ] We use limit=1 to get the last candle. """ try: ohlcv = await exchange.fetch_ohlcv(symbol, timeframe=timeframe, limit=1) if ohlcv and len(ohlcv) > 0: ts, open_, high, low, close, volume = ohlcv[-1] candle = { 'timestamp': ts, 'open': open_, 'high': high, 'low': low, 'close': close, 'volume': volume } return candle return None except Exception as e: print("Error fetching candle data:", e) return None # ------------------------------ # Simulated Sentiment Data # ------------------------------ async def get_sentiment_data(): """ Simulate fetching live sentiment data. In production, integrate sentiment analysis from social media, news APIs, etc. """ await asyncio.sleep(0.1) # Simulated latency return { 'sentiment_score': np.random.rand(), # Example: normalized between 0 and 1 'news_volume': np.random.rand(), 'social_engagement': np.random.rand() } # ------------------------------ # RL Agent with Continuous Learning # ------------------------------ 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 # For demonstration, we use a basic MSE loss. self.loss_fn = nn.MSELoss() def act(self, state): """ Given the latest state, decide on an action. Mapping: 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 sample from the replay buffer. Note: A true RL implementation would incorporate rewards and discounted returns. """ if len(self.replay_buffer) < self.batch_size: return # Not enough samples yet 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 demonstration, we use the first output as our estimation. predictions = outputs[:, 0].unsqueeze(1) loss = self.loss_fn(predictions, targets_tensor) self.optimizer.zero_grad() loss.backward() self.optimizer.step() # ------------------------------ # Trading Bot: Execution with MEXC API # ------------------------------ class TradingBot: def __init__(self, rl_agent, exchange, symbol='BTC/USDT', trade_amount=0.001): self.rl_agent = rl_agent self.exchange = exchange self.symbol = symbol self.trade_amount = trade_amount # Amount to trade (in base currency) async def execute_trade(self, action): """ Convert the RL agent's decision into a market order. Action mapping: 0 => SELL, 1 => HOLD, 2 => BUY. """ if action == 0: print("Executing SELL order") await self.place_market_order('sell') elif action == 2: print("Executing BUY order") await self.place_market_order('buy') else: print("Holding position - no order executed") async def place_market_order(self, side): """ Place a market order on MEXC using ccxt. """ try: order = await self.exchange.create_order(self.symbol, 'market', side, self.trade_amount, None) print(f"Order executed: {order}") except Exception as e: print(f"Error executing {side} order:", e) async def trading_loop(self, timeframe='1m'): """ Main loop: - Fetch live candlestick and sentiment data. - Compute state features. - Let the RL agent decide an action. - Execute market orders. - Store experience and perform continuous training. """ while True: candle = await get_live_candle_data(self.exchange, self.symbol, timeframe) if candle is None: await asyncio.sleep(1) continue sentiment = await get_sentiment_data() state = compute_indicators(candle, sentiment) action = self.rl_agent.act(state) await self.execute_trade(action) # Simulate reward computation. In production, use trading performance to compute reward. reward = np.random.rand() next_state = state # In a real system, this would be updated after the trade. done = False self.rl_agent.replay_buffer.add((state, reward, next_state, done)) self.rl_agent.train_step() # Wait to match the candle timeframe (for a 1m candle, sleep 60 seconds) await asyncio.sleep(60) # ------------------------------ # Main Execution Loop # ------------------------------ async def main_loop(): # Retrieve MEXC API credentials from environment variables or replace with your keys. mexc_api_key = os.environ.get('MEXC_API_KEY', 'YOUR_API_KEY') mexc_api_secret = os.environ.get('MEXC_API_SECRET', 'YOUR_SECRET_KEY') # Create the MEXC exchange instance (ccxt asynchronous) exchange = ccxt.mexc({ 'apiKey': mexc_api_key, 'secret': mexc_api_secret, 'enableRateLimit': True, }) # Define the model input dimensions. # Base candle features (5: open, high, low, close, volume) + simulated 3 sentiment features input_dim = 8 hidden_dim = 128 output_dim = 3 # 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, exchange, symbol='BTC/USDT', trade_amount=0.001) try: # Begin the continuous trading loop (using 1-minute candles) await trading_bot.trading_loop(timeframe='1m') except Exception as e: print("Error in trading loop:", e) finally: await exchange.close() if __name__ == "__main__": asyncio.run(main_loop())