From 773b1b6b761bd3b82206b344ec65651f8b56b290 Mon Sep 17 00:00:00 2001 From: Dobromir Popov Date: Sat, 1 Feb 2025 23:19:08 +0200 Subject: [PATCH] new NN sub-proj --- crypto/brian/index.py | 231 ++++++++++++++++++++++++++++++++++++++++++ web/prisma/dev.db | 0 2 files changed, 231 insertions(+) create mode 100644 crypto/brian/index.py create mode 100644 web/prisma/dev.db diff --git a/crypto/brian/index.py b/crypto/brian/index.py new file mode 100644 index 0000000..1fa3e53 --- /dev/null +++ b/crypto/brian/index.py @@ -0,0 +1,231 @@ +#!/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()) \ No newline at end of file diff --git a/web/prisma/dev.db b/web/prisma/dev.db new file mode 100644 index 0000000..e69de29