wip
This commit is contained in:
Dobromir Popov
parent
2ec75e66cb
commit
c8043a9dcd
@ -7,6 +7,9 @@ if sys.platform == 'win32':
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import os
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import time
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import json
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import argparse
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import threading
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import random
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import numpy as np
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import torch
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import torch.nn as nn
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@ -15,20 +18,22 @@ from collections import deque
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from datetime import datetime
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import matplotlib.pyplot as plt
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import ccxt.async_support as ccxt
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import argparse
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from torch.nn import TransformerEncoder, TransformerEncoderLayer
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import math
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from dotenv import load_dotenv
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load_dotenv()
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# --- Directories ---
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LAST_DIR = os.path.join("models", "last")
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BEST_DIR = os.path.join("models", "best")
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os.makedirs(LAST_DIR, exist_ok=True)
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os.makedirs(BEST_DIR, exist_ok=True)
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CACHE_FILE = "candles_cache.json"
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# --- New Constants ---
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NUM_TIMEFRAMES = 5 # Example: ["1m", "5m", "15m", "1h", "1d"]
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NUM_INDICATORS = 20 # Example: 20 technical indicators
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FEATURES_PER_CHANNEL = 7 # HLOC + SMA_close + SMA_volume
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# --- Constants ---
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NUM_TIMEFRAMES = 5 # Example: ["1m", "5m", "15m", "1h", "1d"]
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NUM_INDICATORS = 20 # Example: 20 technical indicators
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FEATURES_PER_CHANNEL = 7 # e.g. HLOC, SMA_close, SMA_volume
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# --- Positional Encoding Module ---
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class PositionalEncoding(nn.Module):
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@ -58,107 +63,207 @@ class TradingModel(nn.Module):
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nn.Dropout(0.1)
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) for _ in range(num_channels)
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])
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self.timeframe_embed = nn.Embedding(num_timeframes, hidden_dim)
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self.pos_encoder = PositionalEncoding(hidden_dim)
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# Transformer
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encoder_layers = TransformerEncoderLayer(
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d_model=hidden_dim, nhead=4, dim_feedforward=512,
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dropout=0.1, activation='gelu', batch_first=False
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)
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self.transformer = TransformerEncoder(encoder_layers, num_layers=2)
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# Attention Pooling
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self.attn_pool = nn.Linear(hidden_dim, 1)
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# Prediction Heads
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self.high_pred = nn.Sequential(
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nn.Linear(hidden_dim, hidden_dim//2),
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nn.Linear(hidden_dim, hidden_dim // 2),
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nn.GELU(),
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nn.Linear(hidden_dim//2, 1)
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nn.Linear(hidden_dim // 2, 1)
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)
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self.low_pred = nn.Sequential(
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nn.Linear(hidden_dim, hidden_dim//2),
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nn.Linear(hidden_dim, hidden_dim // 2),
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nn.GELU(),
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nn.Linear(hidden_dim//2, 1)
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nn.Linear(hidden_dim // 2, 1)
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)
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def forward(self, x, timeframe_ids):
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# x shape: [batch_size, num_channels, FEATURES_PER_CHANNEL]
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batch_size, num_channels, _ = x.shape
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# Process each channel through its branch
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channel_outs = []
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for i in range(num_channels):
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channel_out = self.channel_branches[i](x[:, i, :])
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channel_outs.append(channel_out)
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# Stack and add timeframe embeddings
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channel_outs = [self.channel_branches[i](x[:, i, :]) for i in range(num_channels)]
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stacked = torch.stack(channel_outs, dim=1) # [batch, channels, hidden]
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stacked = stacked.permute(1, 0, 2) # [channels, batch, hidden]
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# Add timeframe embeddings to each channel
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tf_embeds = self.timeframe_embed(timeframe_ids).unsqueeze(1)
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stacked = stacked + tf_embeds
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# Apply Transformer
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src_mask = torch.triu(torch.ones(stacked.size(0), stacked.size(0)), diagonal=1).bool().to(x.device)
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transformer_out = self.transformer(stacked, src_mask=src_mask)
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# Attention Pooling over channels
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attn_weights = torch.softmax(self.attn_pool(transformer_out), dim=0)
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aggregated = (transformer_out * attn_weights).sum(dim=0)
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return self.high_pred(aggregated).squeeze(), self.low_pred(aggregated).squeeze()
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# --- Enhanced Data Processing ---
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# Here you need to have the helper functions get_aligned_candle_with_index and get_features_for_tf
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# They must be defined elsewhere in your code.
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# --- Technical Indicator Helper Functions ---
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def compute_sma(candles_list, index, period=10):
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start = max(0, index - period + 1)
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values = [candle["close"] for candle in candles_list[start:index+1]]
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return sum(values) / len(values) if values else 0.0
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def compute_sma_volume(candles_list, index, period=10):
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start = max(0, index - period + 1)
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values = [candle["volume"] for candle in candles_list[start:index+1]]
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return sum(values) / len(values) if values else 0.0
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def get_aligned_candle_with_index(candles_list, target_ts):
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best_idx = 0
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for i, candle in enumerate(candles_list):
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if candle["timestamp"] <= target_ts:
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best_idx = i
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else:
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break
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return best_idx, candles_list[best_idx]
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def get_features_for_tf(candles_list, index, period=10):
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candle = candles_list[index]
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f_open = candle["open"]
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f_high = candle["high"]
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f_low = candle["low"]
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f_close = candle["close"]
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f_volume = candle["volume"]
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sma_close = compute_sma(candles_list, index, period)
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sma_volume = compute_sma_volume(candles_list, index, period)
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return [f_open, f_high, f_low, f_close, f_volume, sma_close, sma_volume]
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# --- Caching and Checkpoint Functions ---
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def load_candles_cache(filename):
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if os.path.exists(filename):
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try:
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with open(filename, "r") as f:
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data = json.load(f)
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print(f"Loaded cached data from {filename}.")
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return data
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except Exception as e:
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print("Error reading cache file:", e)
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return {}
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def save_candles_cache(filename, candles_dict):
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try:
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with open(filename, "w") as f:
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json.dump(candles_dict, f)
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except Exception as e:
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print("Error saving cache file:", e)
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def maintain_checkpoint_directory(directory, max_files=10):
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files = os.listdir(directory)
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if len(files) > max_files:
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full_paths = [os.path.join(directory, f) for f in files]
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full_paths.sort(key=lambda x: os.path.getmtime(x))
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for f in full_paths[: len(files) - max_files]:
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os.remove(f)
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def get_best_models(directory):
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best_files = []
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for file in os.listdir(directory):
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parts = file.split("_")
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try:
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r = float(parts[1])
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best_files.append((r, file))
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except Exception:
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continue
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return best_files
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def save_checkpoint(model, epoch, reward, last_dir=LAST_DIR, best_dir=BEST_DIR):
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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last_filename = f"model_last_epoch_{epoch}_{timestamp}.pt"
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last_path = os.path.join(last_dir, last_filename)
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torch.save({
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"epoch": epoch,
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"reward": reward,
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"model_state_dict": model.state_dict()
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}, last_path)
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maintain_checkpoint_directory(last_dir, max_files=10)
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best_models = get_best_models(best_dir)
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add_to_best = False
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if len(best_models) < 10:
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add_to_best = True
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else:
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min_reward, min_file = min(best_models, key=lambda x: x[0])
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if reward > min_reward:
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add_to_best = True
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os.remove(os.path.join(best_dir, min_file))
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if add_to_best:
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best_filename = f"best_{reward:.4f}_epoch_{epoch}_{timestamp}.pt"
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best_path = os.path.join(best_dir, best_filename)
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torch.save({
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"epoch": epoch,
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"reward": reward,
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"model_state_dict": model.state_dict()
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}, best_path)
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maintain_checkpoint_directory(best_dir, max_files=10)
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print(f"Saved checkpoint for epoch {epoch} with reward {reward:.4f}")
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def load_best_checkpoint(model, best_dir=BEST_DIR):
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best_models = get_best_models(best_dir)
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if not best_models:
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return None
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best_reward, best_file = max(best_models, key=lambda x: x[0])
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path = os.path.join(best_dir, best_file)
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print(f"Loading best model from checkpoint: {best_file} with reward {best_reward:.4f}")
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checkpoint = torch.load(path)
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model.load_state_dict(checkpoint["model_state_dict"])
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return checkpoint
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# --- Backtest Environment ---
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class BacktestEnvironment:
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def __init__(self, candles_dict, base_tf, timeframes):
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self.candles_dict = candles_dict
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self.candles_dict = candles_dict # dict of timeframe: candles_list
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self.base_tf = base_tf
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self.timeframes = timeframes
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self.current_index = 0 # Initialize step pointer
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self.current_index = 0
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self.trade_history = []
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self.position = None
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def reset(self):
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self.current_index = 0
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self.position = None
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self.trade_history = []
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return self.get_state(self.current_index)
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def get_state(self, index):
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"""Returns state as an array of shape [num_channels, FEATURES_PER_CHANNEL]."""
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state_features = []
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base_ts = self.candles_dict[self.base_tf][index]["timestamp"]
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# Timeframe channels
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for tf in self.timeframes:
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aligned_idx, _ = get_aligned_candle_with_index(self.candles_dict[tf], base_ts)
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features = get_features_for_tf(self.candles_dict[tf], aligned_idx)
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state_features.append(features)
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# Indicator channels (placeholder - implement your indicators)
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for _ in range(NUM_INDICATORS):
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state_features.append([0.0] * FEATURES_PER_CHANNEL)
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return np.array(state_features, dtype=np.float32)
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def step(self, action):
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"""
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Advance the environment by one step.
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Since this is for backtesting, action isn't used here.
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Returns: current candle info, reward, next state, done, actual high, actual low.
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"""
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# Dummy implementation: you would generate targets based on your backtest logic.
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done = (self.current_index >= len(self.candles_dict[self.base_tf]) - 2)
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current_candle = self.candles_dict[self.base_tf][self.current_index]
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# For example, take the next candle's high/low as targets
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next_candle = self.candles_dict[self.base_tf][self.current_index + 1]
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actual_high = next_candle["high"]
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actual_low = next_candle["low"]
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self.current_index += 1
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next_state = self.get_state(self.current_index)
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return current_candle, 0.0, next_state, done, actual_high, actual_low
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base_candles = self.candles_dict[self.base_tf]
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if self.current_index >= len(base_candles) - 1:
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return self.get_state(self.current_index), 0.0, None, True
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current_state = self.get_state(self.current_index)
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next_index = self.current_index + 1
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next_state = self.get_state(next_index)
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current_candle = base_candles[self.current_index]
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next_candle = base_candles[next_index]
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reward = 0.0
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# Action mapping: 0 -> SELL, 1 -> HOLD, 2 -> BUY.
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if self.position is None:
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if action == 2: # BUY signal
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entry_price = next_candle["open"]
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self.position = {"entry_price": entry_price, "entry_index": self.current_index}
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else:
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if action == 0: # SELL signal
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exit_price = next_candle["open"]
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reward = exit_price - self.position["entry_price"]
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trade = {
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"entry_index": self.position["entry_index"],
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"entry_price": self.position["entry_price"],
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"exit_index": next_index,
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"exit_price": exit_price,
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"pnl": reward
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}
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self.trade_history.append(trade)
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self.position = None
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self.current_index = next_index
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done = (self.current_index >= len(base_candles) - 1)
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return current_state, reward, next_state, done
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def __len__(self):
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return len(self.candles_dict[self.base_tf])
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@ -167,99 +272,126 @@ class BacktestEnvironment:
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def train_on_historical_data(env, model, device, args):
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optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-5)
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scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs)
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for epoch in range(args.epochs):
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state = env.reset()
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total_loss = 0
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model.train()
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while True:
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# Prepare batch (here batch size is 1 for simplicity)
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state_tensor = torch.FloatTensor(state).unsqueeze(0).to(device)
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timeframe_ids = torch.arange(state.shape[0]).to(device)
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# Forward pass
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pred_high, pred_low = model(state_tensor, timeframe_ids)
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# Get target values from next candle (dummy targets from environment)
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_, _, next_state, done, actual_high, actual_low = env.step(None) # Dummy action
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# Here we use dummy targets extracted from the next candle's high/low
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_, _, next_state, done, actual_high, actual_low = env.step(None)
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target_high = torch.FloatTensor([actual_high]).to(device)
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target_low = torch.FloatTensor([actual_low]).to(device)
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# Custom loss: use absolute error scaled by 2
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high_loss = torch.abs(pred_high - target_high) * 2
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low_loss = torch.abs(pred_low - target_low) * 2
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loss = (high_loss + low_loss).mean()
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# Backpropagation
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optimizer.zero_grad()
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loss.backward()
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torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
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optimizer.step()
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total_loss += loss.item()
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if done:
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break
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state = next_state
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scheduler.step()
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print(f"Epoch {epoch+1} Loss: {total_loss/len(env):.4f}")
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save_checkpoint(model, epoch, total_loss)
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# --- Mode Handling and Argument Parsing ---
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# --- Live Plotting Functions ---
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def update_live_chart(ax, candles, trade_history):
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ax.clear()
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close_prices = [candle["close"] for candle in candles]
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x = list(range(len(close_prices)))
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ax.plot(x, close_prices, label="Close Price", color="black", linewidth=1)
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buy_label_added = False
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sell_label_added = False
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for trade in trade_history:
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in_idx = trade["entry_index"]
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out_idx = trade["exit_index"]
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in_price = trade["entry_price"]
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out_price = trade["exit_price"]
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if not buy_label_added:
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ax.plot(in_idx, in_price, marker="^", color="green", markersize=10, label="BUY")
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buy_label_added = True
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else:
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ax.plot(in_idx, in_price, marker="^", color="green", markersize=10)
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if not sell_label_added:
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ax.plot(out_idx, out_price, marker="v", color="red", markersize=10, label="SELL")
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sell_label_added = True
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else:
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ax.plot(out_idx, out_price, marker="v", color="red", markersize=10)
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ax.plot([in_idx, out_idx], [in_price, out_price], linestyle="dotted", color="blue")
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ax.set_title("Live Trading Chart")
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ax.set_xlabel("Candle Index")
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ax.set_ylabel("Price")
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ax.legend()
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ax.grid(True)
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def live_preview_loop(candles, env):
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plt.ion()
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fig, ax = plt.subplots(figsize=(12, 6))
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while True:
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update_live_chart(ax, candles, env.trade_history)
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plt.draw()
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plt.pause(1) # Update every second
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# --- Argument Parsing ---
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--mode', choices=['train', 'live', 'inference'], default='train')
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parser.add_argument('--mode', choices=['train','live','inference'], default='train')
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parser.add_argument('--epochs', type=int, default=100)
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parser.add_argument('--lr', type=float, default=3e-4)
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parser.add_argument('--threshold', type=float, default=0.005)
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return parser.parse_args()
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def load_best_checkpoint(model, best_dir="models/best"):
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# Dummy implementation for loading the best checkpoint.
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# In real usage, check your saved checkpoints.
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print("Loading best checkpoint (dummy implementation)")
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# torch.load(...) can be invoked here.
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return
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def save_checkpoint(model, epoch, reward, last_dir="models/last", best_dir="models/best"):
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# Dummy implementation for saving checkpoints.
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print(f"Saving checkpoint for epoch {epoch}, reward: {reward:.4f}")
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def random_action():
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return random.randint(0, 2)
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# --- Main Function ---
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async def main():
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args = parse_args()
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# Initialize model
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model = TradingModel(
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num_channels=NUM_TIMEFRAMES + NUM_INDICATORS,
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num_timeframes=NUM_TIMEFRAMES
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).to(device)
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# Define timeframes; these must match your data and expected state dimensions.
|
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timeframes = ["1m", "5m", "15m", "1h", "1d"]
|
||||
input_dim = len(timeframes) * 7 # 7 features per timeframe.
|
||||
hidden_dim = 128
|
||||
output_dim = 3 # Actions: SELL, HOLD, BUY.
|
||||
# For the Transformer model, we set number of channels = NUM_TIMEFRAMES + NUM_INDICATORS.
|
||||
model = TradingModel(NUM_TIMEFRAMES + NUM_INDICATORS, NUM_TIMEFRAMES).to(device)
|
||||
|
||||
if args.mode == 'train':
|
||||
# Load historical candle data for backtesting
|
||||
candles_dict = load_candles_cache("candles_cache.json")
|
||||
candles_dict = load_candles_cache(CACHE_FILE)
|
||||
if not candles_dict:
|
||||
print("No historical candle data available for backtesting.")
|
||||
return
|
||||
base_tf = "1m" # Base timeframe
|
||||
timeframes = ["1m", "5m", "15m", "1h", "1d"]
|
||||
base_tf = "1m"
|
||||
env = BacktestEnvironment(candles_dict, base_tf, timeframes)
|
||||
train_on_historical_data(env, model, device, args)
|
||||
elif args.mode == 'live':
|
||||
# Load model and connect to live data
|
||||
load_best_checkpoint(model)
|
||||
candles_dict = load_candles_cache(CACHE_FILE)
|
||||
if not candles_dict:
|
||||
print("No cached candles available for live preview.")
|
||||
return
|
||||
env = BacktestEnvironment(candles_dict, base_tf="1m", timeframes=timeframes)
|
||||
# Start the live preview in a separate daemon thread.
|
||||
preview_thread = threading.Thread(target=live_preview_loop, args=(candles_dict["1m"], env), daemon=True)
|
||||
preview_thread.start()
|
||||
print("Starting live trading loop. (Using random actions for simulation.)")
|
||||
while True:
|
||||
# Process live data: fetch live candles, make predictions, execute trades
|
||||
print("Processing live data...")
|
||||
await asyncio.sleep(1)
|
||||
state, reward, next_state, done = env.step(random_action())
|
||||
if done:
|
||||
print("Reached end of simulated data, resetting environment.")
|
||||
state = env.reset(clear_trade_history=False)
|
||||
await asyncio.sleep(1) # Simulate one candle per second.
|
||||
elif args.mode == 'inference':
|
||||
# Load model and run inference
|
||||
load_best_checkpoint(model)
|
||||
print("Running inference...")
|
||||
# Add your inference logic here
|
||||
# Implement your inference loop here.
|
||||
else:
|
||||
print("Invalid mode specified.")
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
Loading…
x
Reference in New Issue
Block a user