diff --git a/crypto/brian/index.py b/crypto/brian/index.py index 1fa3e53..4c33712 100644 --- a/crypto/brian/index.py +++ b/crypto/brian/index.py @@ -1,10 +1,12 @@ #!/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 asyncio -from collections import deque import numpy as np +from collections import deque # ------------------------------ # Neural Network Architecture @@ -12,8 +14,8 @@ import numpy as np 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. + # 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(), @@ -47,11 +49,12 @@ class ReplayBuffer: # ------------------------------ 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. + 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 candlestick features: + # Base candle features: open, high, low, close, volume features.extend([ candle.get('open', 0.0), candle.get('high', 0.0), @@ -60,43 +63,57 @@ def compute_indicators(candle, additional_data): candle.get('volume', 0.0) ]) - # Append additional indicator values (e.g., sentiment score, news volume, etc.) + # 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) # ------------------------------ -# Simulated Live Data Streams +# Data Ingestion from MEXC: Live Candle Data # ------------------------------ -async def get_live_candle_data(): +async def get_live_candle_data(exchange, symbol, timeframe='1m'): """ - This function should connect to your live data feed. - For demonstration purposes, we simulate new candlestick data. + 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. """ - 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() - } + 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 from external sources. - Replace this with integration to actual X feeds or news APIs. + Simulate fetching live sentiment data. + In production, integrate sentiment analysis from social media, news APIs, etc. """ - await asyncio.sleep(1) + await asyncio.sleep(0.1) # Simulated latency return { - 'sentiment_score': np.random.rand(), # e.g., normalized sentiment between 0 and 1 + '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 Training +# RL Agent with Continuous Learning # ------------------------------ class ContinuousRLAgent: def __init__(self, model, optimizer, replay_buffer, batch_size=32): @@ -104,14 +121,13 @@ class ContinuousRLAgent: 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) + # For demonstration, we use a basic MSE 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. + 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(): @@ -121,15 +137,14 @@ class ContinuousRLAgent: 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. + 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 + 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) @@ -141,7 +156,7 @@ class ContinuousRLAgent: 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. + # For demonstration, we use the first output as our estimation. predictions = outputs[:, 0].unsqueeze(1) loss = self.loss_fn(predictions, targets_tensor) @@ -150,82 +165,104 @@ class ContinuousRLAgent: self.optimizer.step() # ------------------------------ -# Trading Bot: Integration with Jupiter API (Solana) +# Trading Bot: Execution with MEXC API # ------------------------------ class TradingBot: - def __init__(self, rl_agent): + def __init__(self, rl_agent, exchange, symbol='BTC/USDT', trade_amount=0.001): 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') + self.exchange = exchange + self.symbol = symbol + self.trade_amount = trade_amount # Amount to trade (in base currency) 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. + 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") - # self.jupiter_client.sell(...actual trade parameters...) + await self.place_market_order('sell') elif action == 2: print("Executing BUY order") - # self.jupiter_client.buy(...actual trade parameters...) + await self.place_market_order('buy') else: - print("Holding position") + print("Holding position - no order executed") - async def trading_loop(self): + async def place_market_order(self, side): """ - Main trading loop: - • Ingest live data. - • Compute features. - • Let the agent decide on an action. - • Execute trades. - • Store experience and train continuously. + 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: - # 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. + candle = await get_live_candle_data(self.exchange, self.symbol, timeframe) + if candle is None: + await asyncio.sleep(1) + continue - # Compute state features - state = compute_indicators(candle, indicators) - # Get an action from the RL agent (0: Sell, 1: Hold, 2: Buy) + 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 reality, your reward function should be based on trading performance) + # Simulate reward computation. In production, use trading performance to compute reward. 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 + 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)) - # 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) + # Wait to match the candle timeframe (for a 1m candle, sleep 60 seconds) + await asyncio.sleep(60) # ------------------------------ -# Main Orchestration Loop +# Main Execution 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 + # 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) - # Start the continuous trading loop - await trading_bot.trading_loop() + 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()) \ No newline at end of file diff --git a/crypto/brian/readme.md b/crypto/brian/readme.md new file mode 100644 index 0000000..e761aab --- /dev/null +++ b/crypto/brian/readme.md @@ -0,0 +1,2 @@ +pip install ccxt torch numpy +