580 lines
23 KiB
Python
580 lines
23 KiB
Python
#!/usr/bin/env python3
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import sys
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import asyncio
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if sys.platform == 'win32':
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asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
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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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import torch.optim as optim
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from datetime import datetime
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import matplotlib.pyplot as plt
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import math
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from torch.nn import TransformerEncoder, TransformerEncoderLayer
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import matplotlib.dates as mdates
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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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# --- Constants ---
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NUM_TIMEFRAMES = 5 # e.g., ["1m", "5m", "15m", "1h", "1d"]
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NUM_INDICATORS = 20 # e.g., 20 technical indicators
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# Each channel input will have 7 features.
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FEATURES_PER_CHANNEL = 7
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# We add one extra channel for order information.
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ORDER_CHANNELS = 1
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# --- Positional Encoding Module ---
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class PositionalEncoding(nn.Module):
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def __init__(self, d_model, dropout=0.1, max_len=5000):
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super().__init__()
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self.dropout = nn.Dropout(p=dropout)
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position = torch.arange(max_len).unsqueeze(1)
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div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
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pe = torch.zeros(max_len, 1, d_model)
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pe[:, 0, 0::2] = torch.sin(position * div_term)
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pe[:, 0, 1::2] = torch.cos(position * div_term)
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self.register_buffer('pe', pe)
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def forward(self, x):
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x = x + self.pe[:x.size(0)]
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return self.dropout(x)
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# --- Enhanced Transformer Model ---
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class TradingModel(nn.Module):
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def __init__(self, num_channels, num_timeframes, hidden_dim=128):
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super().__init__()
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# Create one branch per channel.
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self.channel_branches = nn.ModuleList([
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nn.Sequential(
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nn.Linear(FEATURES_PER_CHANNEL, hidden_dim),
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nn.LayerNorm(hidden_dim),
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nn.GELU(),
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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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# Embedding for channels 0..num_channels-1.
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self.timeframe_embed = nn.Embedding(num_channels, hidden_dim)
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self.pos_encoder = PositionalEncoding(hidden_dim)
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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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self.attn_pool = nn.Linear(hidden_dim, 1)
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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.GELU(),
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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.GELU(),
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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: [batch_size, num_channels, FEATURES_PER_CHANNEL]
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batch_size, num_channels, _ = x.shape
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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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stacked = torch.stack(channel_outs, dim=1) # shape: [batch, channels, hidden]
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stacked = stacked.permute(1, 0, 2) # shape: [channels, batch, hidden]
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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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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, mask=src_mask)
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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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# --- Technical Indicator Helpers ---
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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 & 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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loss = float(parts[1])
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best_files.append((loss, 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, optimizer, epoch, loss, 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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"loss": loss,
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"model_state_dict": model.state_dict(),
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"optimizer_state_dict": optimizer.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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worst_loss, worst_file = max(best_models, key=lambda x: x[0])
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if loss < worst_loss:
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add_to_best = True
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os.remove(os.path.join(best_dir, worst_file))
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if add_to_best:
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best_filename = f"best_{loss:.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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"loss": loss,
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"model_state_dict": model.state_dict(),
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"optimizer_state_dict": optimizer.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 loss {loss:.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_loss, best_file = min(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 loss {best_loss:.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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# --- Live HTML Chart Update ---
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def update_live_html(candles, trade_history, epoch):
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"""
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Generate a chart image that uses actual timestamps on the x-axis and shows a cumulative epoch PnL.
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The chart (with buy/sell markers and dotted lines) is embedded in an HTML page that auto-refreshes.
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"""
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from io import BytesIO
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import base64
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fig, ax = plt.subplots(figsize=(12, 6))
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update_live_chart(ax, candles, trade_history)
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# Compute cumulative epoch PnL.
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epoch_pnl = sum(trade["pnl"] for trade in trade_history)
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ax.set_title(f"Live Trading Chart - Epoch {epoch} | PnL: {epoch_pnl:.2f}")
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buf = BytesIO()
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fig.savefig(buf, format='png')
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plt.close(fig)
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buf.seek(0)
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image_base64 = base64.b64encode(buf.getvalue()).decode('utf-8')
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html_content = f"""
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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8">
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<meta http-equiv="refresh" content="10">
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<title>Live Trading Chart - Epoch {epoch}</title>
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<style>
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body {{
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margin: 0;
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padding: 0;
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display: flex;
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justify-content: center;
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align-items: center;
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background-color: #f4f4f4;
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}}
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.chart-container {{
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text-align: center;
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}}
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img {{
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max-width: 100%;
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height: auto;
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}}
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</style>
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</head>
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<body>
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<div class="chart-container">
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<h2>Live Trading Chart - Epoch {epoch} | PnL: {epoch_pnl:.2f}</h2>
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<img src="data:image/png;base64,{image_base64}" alt="Live Chart"/>
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</div>
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</body>
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</html>
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"""
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with open("live_chart.html", "w") as f:
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f.write(html_content)
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print("Updated live_chart.html.")
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# --- Chart Drawing Helpers ---
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def update_live_chart(ax, candles, trade_history):
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"""
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Plot the price chart using actual timestamps on the x-axis.
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Mark BUY (green) and SELL (red) actions, and draw dotted lines between entry and exit.
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"""
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ax.clear()
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# Convert timestamps to datetime objects.
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times = [datetime.fromtimestamp(candle["timestamp"]) for candle in candles]
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close_prices = [candle["close"] for candle in candles]
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ax.plot(times, close_prices, label="Close Price", color="black", linewidth=1)
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# Format x-axis date labels.
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))
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# Calculate epoch PnL.
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epoch_pnl = sum(trade["pnl"] for trade in trade_history)
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# Plot each trade.
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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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entry_time = datetime.fromtimestamp(candles[trade["entry_index"]]["timestamp"])
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exit_time = datetime.fromtimestamp(candles[trade["exit_index"]]["timestamp"])
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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(entry_time, 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(entry_time, in_price, marker="^", color="green", markersize=10)
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if not sell_label_added:
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ax.plot(exit_time, 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(exit_time, out_price, marker="v", color="red", markersize=10)
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ax.plot([entry_time, exit_time], [in_price, out_price], linestyle="dotted", color="blue")
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ax.set_xlabel("Time")
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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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fig = ax.get_figure()
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fig.autofmt_xdate()
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# --- Simulation of Trades for Visualization ---
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def simulate_trades(model, env, device, args):
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"""
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Run a simulation on the current sliding window using the model's outputs and a decision rule.
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This simulation updates env.trade_history and is used for visualization only.
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"""
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env.reset() # resets the window and index
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while True:
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i = env.current_index
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state = env.get_state(i)
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current_open = env.candle_window[i]["open"]
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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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pred_high, pred_low = model(state_tensor, timeframe_ids)
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pred_high = pred_high.item()
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pred_low = pred_low.item()
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# Simple decision rule based on predicted move.
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if (pred_high - current_open) >= (current_open - pred_low) and (pred_high - current_open) > args.threshold:
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action = 2 # BUY
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elif (current_open - pred_low) > (pred_high - current_open) and (current_open - pred_low) > args.threshold:
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action = 0 # SELL
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else:
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action = 1 # HOLD
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_, _, _, done, _, _ = env.step(action)
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if done:
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break
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# --- Backtest Environment with Sliding Window and Order Info ---
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class BacktestEnvironment:
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def __init__(self, candles_dict, base_tf, timeframes, window_size=None):
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self.candles_dict = candles_dict # full candles dict across timeframes
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self.base_tf = base_tf
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self.timeframes = timeframes
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self.full_candles = candles_dict[base_tf]
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if window_size is None:
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window_size = 100 if len(self.full_candles) >= 100 else len(self.full_candles)
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self.window_size = window_size
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self.reset()
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def reset(self):
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# Randomly select a sliding window from the full dataset.
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self.start_index = random.randint(0, len(self.full_candles) - self.window_size)
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self.candle_window = self.full_candles[self.start_index: self.start_index + self.window_size]
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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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return self.get_state(self.current_index)
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def __len__(self):
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return self.window_size
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def get_order_features(self, index):
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"""
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Returns a list of 7 features for the order channel.
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If an order is open, the first element is 1.0 and the second is the normalized difference:
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(current open - entry_price) / current open.
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Otherwise, returns zeros.
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"""
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candle = self.candle_window[index]
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if self.position is None:
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return [0.0] * FEATURES_PER_CHANNEL
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else:
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flag = 1.0
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diff = (candle["open"] - self.position["entry_price"]) / candle["open"]
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return [flag, diff] + [0.0] * (FEATURES_PER_CHANNEL - 2)
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def get_state(self, index):
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"""
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Build state features from:
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- For each timeframe: features from the aligned candle.
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- One extra channel: current order information.
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- NUM_INDICATORS channels of zeros.
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Each channel is a vector of length FEATURES_PER_CHANNEL.
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"""
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state_features = []
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base_ts = self.candle_window[index]["timestamp"]
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for tf in self.timeframes:
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if tf == self.base_tf:
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candle = self.candle_window[index]
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features = get_features_for_tf([candle], 0)
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else:
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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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# Append order channel.
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order_features = self.get_order_features(index)
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state_features.append(order_features)
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# Append technical indicator channels.
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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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Execute one step in the environment:
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- action: 0 => SELL, 1 => HOLD, 2 => BUY.
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- Trades recorded when a BUY is followed by a SELL.
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"""
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base = self.candle_window
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if self.current_index >= len(base) - 1:
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current_state = self.get_state(self.current_index)
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return current_state, 0.0, None, True, 0.0, 0.0
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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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next_candle = base[next_index]
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reward = 0.0
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if self.position is None:
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if action == 2: # BUY signal: enter at next open.
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self.position = {"entry_price": next_candle["open"], "entry_index": self.current_index}
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else:
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if action == 0: # SELL signal: exit at next open.
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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) - 1)
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actual_high = next_candle["high"]
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actual_low = next_candle["low"]
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return current_state, reward, next_state, done, actual_high, actual_low
|
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|
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# --- Enhanced Training Loop ---
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def train_on_historical_data(env, model, device, args, start_epoch, optimizer, scheduler):
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lambda_trade = args.lambda_trade # Weight for the surrogate profit loss.
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|
for epoch in range(start_epoch, args.epochs):
|
|
env.reset() # Resets the sliding window.
|
|
loss_accum = 0.0
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|
steps = len(env) - 1 # We assume steps over consecutive candle pairs.
|
|
for i in range(steps):
|
|
state = env.get_state(i)
|
|
current_open = env.candle_window[i]["open"]
|
|
actual_high = env.candle_window[i+1]["high"]
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|
actual_low = env.candle_window[i+1]["low"]
|
|
state_tensor = torch.FloatTensor(state).unsqueeze(0).to(device)
|
|
timeframe_ids = torch.arange(state.shape[0]).to(device)
|
|
pred_high, pred_low = model(state_tensor, timeframe_ids)
|
|
# Prediction loss (L1 error).
|
|
L_pred = torch.abs(pred_high - torch.tensor(actual_high, device=device)) + \
|
|
torch.abs(pred_low - torch.tensor(actual_low, device=device))
|
|
# Surrogate profit loss:
|
|
profit_buy = pred_high - current_open # potential long gain
|
|
profit_sell = current_open - pred_low # potential short gain
|
|
L_trade = - torch.max(profit_buy, profit_sell)
|
|
loss = L_pred + lambda_trade * L_trade
|
|
optimizer.zero_grad()
|
|
loss.backward()
|
|
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
|
|
optimizer.step()
|
|
loss_accum += loss.item()
|
|
scheduler.step()
|
|
epoch_loss = loss_accum / steps
|
|
print(f"Epoch {epoch+1} Loss: {epoch_loss:.4f}")
|
|
save_checkpoint(model, optimizer, epoch, loss_accum)
|
|
simulate_trades(model, env, device, args)
|
|
update_live_html(env.candle_window, env.trade_history, epoch+1)
|
|
|
|
# --- Live Plotting Functions (For Live Mode) ---
|
|
def live_preview_loop(candles, env):
|
|
plt.ion()
|
|
fig, ax = plt.subplots(figsize=(12, 6))
|
|
while True:
|
|
update_live_chart(ax, candles, env.trade_history)
|
|
plt.draw()
|
|
plt.pause(1)
|
|
|
|
# --- Argument Parsing ---
|
|
def parse_args():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--mode', choices=['train', 'live', 'inference'], default='train')
|
|
parser.add_argument('--epochs', type=int, default=100)
|
|
parser.add_argument('--lr', type=float, default=3e-4)
|
|
parser.add_argument('--threshold', type=float, default=0.005, help="Minimum predicted move to trigger trade.")
|
|
parser.add_argument('--lambda_trade', type=float, default=1.0, help="Weight for trade surrogate loss.")
|
|
parser.add_argument('--start_fresh', action='store_true', help="Start training from scratch.")
|
|
return parser.parse_args()
|
|
|
|
def random_action():
|
|
return random.randint(0, 2)
|
|
|
|
# --- Main Function ---
|
|
async def main():
|
|
args = parse_args()
|
|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
|
print("Using device:", device)
|
|
timeframes = ["1m", "5m", "15m", "1h", "1d"]
|
|
hidden_dim = 128
|
|
# Total channels: NUM_TIMEFRAMES + 1 (order info) + NUM_INDICATORS.
|
|
total_channels = NUM_TIMEFRAMES + 1 + NUM_INDICATORS
|
|
model = TradingModel(total_channels, NUM_TIMEFRAMES).to(device)
|
|
|
|
if args.mode == 'train':
|
|
candles_dict = load_candles_cache(CACHE_FILE)
|
|
if not candles_dict:
|
|
print("No historical candle data available for backtesting.")
|
|
return
|
|
base_tf = "1m"
|
|
env = BacktestEnvironment(candles_dict, base_tf, timeframes, window_size=100)
|
|
start_epoch = 0
|
|
checkpoint = None
|
|
if not args.start_fresh:
|
|
checkpoint = load_best_checkpoint(model)
|
|
if checkpoint is not None:
|
|
start_epoch = checkpoint.get("epoch", 0) + 1
|
|
print(f"Resuming training from epoch {start_epoch}.")
|
|
else:
|
|
print("No checkpoint found. Starting training from scratch.")
|
|
else:
|
|
print("Starting training from scratch as requested.")
|
|
optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-5)
|
|
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs - start_epoch)
|
|
if checkpoint is not None:
|
|
optim_state = checkpoint.get("optimizer_state_dict", None)
|
|
if optim_state is not None and "param_groups" in optim_state:
|
|
optimizer.load_state_dict(optim_state)
|
|
print("Loaded optimizer state from checkpoint.")
|
|
else:
|
|
print("No valid optimizer state found in checkpoint; starting fresh optimizer state.")
|
|
train_on_historical_data(env, model, device, args, start_epoch, optimizer, scheduler)
|
|
|
|
elif args.mode == 'live':
|
|
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, window_size=100)
|
|
preview_thread = threading.Thread(target=live_preview_loop, args=(env.candle_window, env), daemon=True)
|
|
preview_thread.start()
|
|
print("Starting live trading loop. (Using model-based decision rule.)")
|
|
while True:
|
|
state = env.get_state(env.current_index)
|
|
current_open = env.candle_window[env.current_index]["open"]
|
|
state_tensor = torch.FloatTensor(state).unsqueeze(0).to(device)
|
|
timeframe_ids = torch.arange(state.shape[0]).to(device)
|
|
pred_high, pred_low = model(state_tensor, timeframe_ids)
|
|
pred_high = pred_high.item()
|
|
pred_low = pred_low.item()
|
|
if (pred_high - current_open) >= (current_open - pred_low) and (pred_high - current_open) > args.threshold:
|
|
action = 2
|
|
elif (current_open - pred_low) > (pred_high - current_open) and (current_open - pred_low) > args.threshold:
|
|
action = 0
|
|
else:
|
|
action = 1
|
|
_, _, _, done, _, _ = env.step(action)
|
|
if done:
|
|
print("Reached end of simulation window; resetting environment.")
|
|
env.reset()
|
|
await asyncio.sleep(1)
|
|
elif args.mode == 'inference':
|
|
load_best_checkpoint(model)
|
|
print("Running inference...")
|
|
# Your inference logic goes here.
|
|
else:
|
|
print("Invalid mode specified.")
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main()) |