740 lines
30 KiB
Python
740 lines
30 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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# --- Fetch Real 1s Data (if main_tf=="1s") ---
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def fetch_real_1s_data():
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"""
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Fetch real 1-second candle data from your API.
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Replace the URL and parameters with those required by your data provider.
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Expected data format: a list of dictionaries with keys: "timestamp", "open", "high", "low", "close", "volume"
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"""
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import requests
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url = "https://api.example.com/1s-data" # <-- Replace with your actual endpoint.
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try:
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response = requests.get(url)
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response.raise_for_status()
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data = response.json()
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print("Fetched real 1s data successfully.")
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return data
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except Exception as e:
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print("Failed to fetch real 1s data:", e)
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return []
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# --- Helper Function for Timestamp Conversion ---
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def convert_timestamp(ts):
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"""
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Safely converts a timestamp to a datetime object.
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If the timestamp is abnormally high (e.g. in milliseconds),
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it is divided by 1000.
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"""
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ts = float(ts)
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if ts > 1e10: # Likely in milliseconds
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ts /= 1000.0
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return datetime.fromtimestamp(ts)
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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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TRAINING_CACHE_FILE = "training_cache.json"
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# --- Constants ---
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NUM_TIMEFRAMES = 6 # e.g., ["1s", "1m", "5m", "15m", "1h", "1d"]
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NUM_INDICATORS = 20 # e.g., 20 technical indicators
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FEATURES_PER_CHANNEL = 7 # Each channel has 7 features.
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ORDER_CHANNELS = 1 # One extra channel for order information.
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# --- Training Cache Helpers ---
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def load_training_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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cache = json.load(f)
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print(f"Loaded training cache from {filename}.")
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return cache
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except Exception as e:
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print("Error loading training cache:", e)
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return {"total_pnl": 0.0}
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def save_training_cache(filename, cache):
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try:
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with open(filename, "w") as f:
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json.dump(cache, f)
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except Exception as e:
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print("Error saving training cache:", e)
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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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# 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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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=True # avoid nested tensor warning
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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) # [batch, channels, hidden]
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tf_embeds = self.timeframe_embed(timeframe_ids) # [num_channels, hidden]
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stacked = stacked + tf_embeds.unsqueeze(0) # add embeddings (broadcast along batch)
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transformer_out = self.transformer(stacked)
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attn_weights = torch.softmax(self.attn_pool(transformer_out), dim=1)
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aggregated = (transformer_out * attn_weights).sum(dim=1)
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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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old_state = checkpoint["model_state_dict"]
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new_state = model.state_dict()
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if "timeframe_embed.weight" in old_state:
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old_embed = old_state["timeframe_embed.weight"]
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new_embed = new_state["timeframe_embed.weight"]
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if old_embed.shape[0] < new_embed.shape[0]:
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new_embed[:old_embed.shape[0]] = old_embed
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old_state["timeframe_embed.weight"] = new_embed
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model.load_state_dict(old_state, strict=False)
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return checkpoint
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# --- Function for Manual Trade Override ---
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def manual_trade(env):
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"""
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When no sufficient action is taken by the model, use a fallback:
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Scan the remaining window for the global maximum and minimum.
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If the maximum occurs before the minimum, simulate a short trade;
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otherwise simulate a long trade.
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Use the candle "close" prices to compute trade reward.
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"""
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current_index = env.current_index
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if current_index >= len(env.candle_window) - 1:
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env.current_index = len(env.candle_window) - 1
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return
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max_val = -float('inf')
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min_val = float('inf')
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i_max = current_index
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i_min = current_index
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for j in range(current_index + 1, len(env.candle_window)):
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high_j = env.candle_window[j]["high"]
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low_j = env.candle_window[j]["low"]
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if high_j > max_val:
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max_val = high_j
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i_max = j
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if low_j < min_val:
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min_val = low_j
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i_min = j
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if i_max < i_min:
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entry_price = env.candle_window[current_index]["close"]
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exit_price = env.candle_window[i_min]["close"]
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reward = entry_price - exit_price
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trade = {
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"entry_index": current_index,
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"entry_price": entry_price,
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"exit_index": i_min,
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"exit_price": exit_price,
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"pnl": reward
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}
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else:
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entry_price = env.candle_window[current_index]["close"]
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exit_price = env.candle_window[i_max]["close"]
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reward = exit_price - entry_price
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trade = {
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"entry_index": current_index,
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"entry_price": entry_price,
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"exit_index": i_max,
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"exit_price": exit_price,
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"pnl": reward
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}
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env.trade_history.append(trade)
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env.current_index = trade["exit_index"]
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# --- Simulation for 1s Data Using Local Extrema ---
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def simulate_trades_1s(env):
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"""
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When the main timeframe is 1s, scan the entire remaining window to detect local extrema.
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If at least two extrema are found, pair consecutive extrema as trades.
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Use the candle "close" prices for trade reward calculation.
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If too few extrema are found, fallback to manual_trade.
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"""
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n = len(env.candle_window)
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extrema = []
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for i in range(env.current_index, n):
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if i == env.current_index or i == n-1:
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extrema.append(i)
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else:
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prev = env.candle_window[i-1]["close"]
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curr = env.candle_window[i]["close"]
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nex = env.candle_window[i+1]["close"]
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if curr < prev and curr < nex:
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extrema.append(i)
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elif curr > prev and curr > nex:
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extrema.append(i)
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if len(extrema) < 2:
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manual_trade(env)
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return
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for j in range(len(extrema)-1):
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entry_idx = extrema[j]
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exit_idx = extrema[j+1]
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entry_price = env.candle_window[entry_idx]["close"]
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exit_price = env.candle_window[exit_idx]["close"]
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if env.candle_window[entry_idx]["close"] < env.candle_window[exit_idx]["close"]:
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reward = exit_price - entry_price
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else:
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reward = entry_price - exit_price
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trade = {
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"entry_index": entry_idx,
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"entry_price": entry_price,
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"exit_index": exit_idx,
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"exit_price": exit_price,
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"pnl": reward
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}
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env.trade_history.append(trade)
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env.current_index = n - 1
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# --- General Simulation of Trades ---
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def simulate_trades(model, env, device, args):
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"""
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Simulate trades over the current sliding window.
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If the main timeframe is 1s, use local extrema detection.
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Otherwise, check if the model's predicted potentials exceed the threshold.
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Use manual_trade if the model's signal is too weak.
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"""
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if args.main_tf == "1s":
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simulate_trades_1s(env)
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return
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env.reset()
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while True:
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i = env.current_index
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if i >= len(env.candle_window) - 1:
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break
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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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if (pred_high - current_open) > args.threshold or (current_open - pred_low) > args.threshold:
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if (pred_high - current_open) >= (current_open - pred_low):
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action = 2 # BUY
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else:
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action = 0 # SELL
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_, _, _, done, _, _ = env.step(action)
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else:
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manual_trade(env)
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if env.current_index >= len(env.candle_window) - 1:
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break
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# --- Live HTML Chart Update (with Volume and Loss) ---
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def update_live_html(candles, trade_history, epoch, loss, total_pnl):
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"""
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Generate an HTML page with a live chart.
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The chart displays price (line) and volume (bars on a secondary y-axis),
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and includes trade markers with dotted connecting lines.
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The title shows the epoch, loss, and total PnL.
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The page auto-refreshes every 1 second.
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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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epoch_pnl = sum(trade["pnl"] for trade in trade_history)
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ax.set_title(f"Epoch {epoch} | Loss: {loss:.4f} | PnL: {epoch_pnl:.2f}| Total PnL: {total_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="1">
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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>Epoch {epoch} | Loss: {loss:.4f} | PnL: {epoch_pnl:.2f}| Total PnL: {total_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 (with Volume and Date+Time) ---
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def update_live_chart(ax, candles, trade_history):
|
|
"""
|
|
Plot the price chart with actual timestamps (date and time in short format)
|
|
and volume on a secondary y-axis.
|
|
Mark trade entry (green) and exit (red) points, with dotted lines connecting them.
|
|
"""
|
|
ax.clear()
|
|
times = [convert_timestamp(candle["timestamp"]) for candle in candles]
|
|
close_prices = [candle["close"] for candle in candles]
|
|
ax.plot(times, close_prices, label="Close Price", color="black", linewidth=1)
|
|
ax.set_xlabel("Time")
|
|
ax.set_ylabel("Price")
|
|
ax.xaxis.set_major_formatter(mdates.DateFormatter('%m-%d %H:%M'))
|
|
ax2 = ax.twinx()
|
|
volumes = [candle["volume"] for candle in candles]
|
|
if len(times) > 1:
|
|
times_num = mdates.date2num(times)
|
|
bar_width = (times_num[-1] - times_num[0]) / len(times) * 0.8
|
|
else:
|
|
bar_width = 0.01
|
|
ax2.bar(times, volumes, width=bar_width, alpha=0.3, color="grey", label="Volume")
|
|
ax2.set_ylabel("Volume")
|
|
for trade in trade_history:
|
|
entry_time = convert_timestamp(candles[trade["entry_index"]]["timestamp"])
|
|
exit_time = convert_timestamp(candles[trade["exit_index"]]["timestamp"])
|
|
in_price = trade["entry_price"]
|
|
out_price = trade["exit_price"]
|
|
ax.plot(entry_time, in_price, marker="^", color="green", markersize=10, label="BUY")
|
|
ax.plot(exit_time, out_price, marker="v", color="red", markersize=10, label="SELL")
|
|
ax.plot([entry_time, exit_time], [in_price, out_price], linestyle="dotted", color="blue")
|
|
lines, labels = ax.get_legend_handles_labels()
|
|
lines2, labels2 = ax2.get_legend_handles_labels()
|
|
ax.legend(lines + lines2, labels + labels2)
|
|
ax.grid(True)
|
|
fig = ax.get_figure()
|
|
fig.autofmt_xdate()
|
|
|
|
# --- Backtest Environment with Sliding Window and Order Info ---
|
|
class BacktestEnvironment:
|
|
def __init__(self, candles_dict, base_tf, timeframes, window_size=None):
|
|
self.candles_dict = candles_dict # full candles dict across timeframes
|
|
self.base_tf = base_tf
|
|
self.timeframes = timeframes
|
|
self.full_candles = candles_dict[base_tf]
|
|
if window_size is None:
|
|
window_size = 100 if len(self.full_candles) >= 100 else len(self.full_candles)
|
|
self.window_size = window_size
|
|
self.reset()
|
|
def reset(self):
|
|
self.start_index = random.randint(0, len(self.full_candles) - self.window_size)
|
|
self.candle_window = self.full_candles[self.start_index: self.start_index + self.window_size]
|
|
self.current_index = 0
|
|
self.trade_history = []
|
|
self.position = None
|
|
return self.get_state(self.current_index)
|
|
def __len__(self):
|
|
return self.window_size
|
|
def get_order_features(self, index):
|
|
candle = self.candle_window[index]
|
|
if self.position is None:
|
|
return [0.0] * FEATURES_PER_CHANNEL
|
|
else:
|
|
flag = 1.0
|
|
diff = (candle["open"] - self.position["entry_price"]) / candle["open"]
|
|
return [flag, diff] + [0.0] * (FEATURES_PER_CHANNEL - 2)
|
|
def get_state(self, index):
|
|
state_features = []
|
|
base_ts = self.candle_window[index]["timestamp"]
|
|
for tf in self.timeframes:
|
|
if tf == self.base_tf:
|
|
candle = self.candle_window[index]
|
|
features = get_features_for_tf([candle], 0)
|
|
else:
|
|
aligned_idx, _ = get_aligned_candle_with_index(self.candles_dict[tf], base_ts)
|
|
features = get_features_for_tf(self.candles_dict[tf], aligned_idx)
|
|
state_features.append(features)
|
|
order_features = self.get_order_features(index)
|
|
state_features.append(order_features)
|
|
for _ in range(NUM_INDICATORS):
|
|
state_features.append([0.0] * FEATURES_PER_CHANNEL)
|
|
return np.array(state_features, dtype=np.float32)
|
|
def step(self, action):
|
|
base = self.candle_window
|
|
if self.current_index >= len(base) - 1:
|
|
current_state = self.get_state(self.current_index)
|
|
return current_state, 0.0, None, True, 0.0, 0.0
|
|
current_state = self.get_state(self.current_index)
|
|
next_index = self.current_index + 1
|
|
next_state = self.get_state(next_index)
|
|
next_candle = base[next_index]
|
|
reward = 0.0
|
|
if self.position is None:
|
|
if action == 2: # BUY (open long)
|
|
self.position = {"entry_price": next_candle["open"], "entry_index": self.current_index}
|
|
else:
|
|
if action == 0: # SELL (close trade)
|
|
exit_price = next_candle["close"] # use close price
|
|
reward = exit_price - self.position["entry_price"]
|
|
trade = {
|
|
"entry_index": self.position["entry_index"],
|
|
"entry_price": self.position["entry_price"],
|
|
"exit_index": next_index,
|
|
"exit_price": exit_price,
|
|
"pnl": reward
|
|
}
|
|
self.trade_history.append(trade)
|
|
self.position = None
|
|
self.current_index = next_index
|
|
done = (self.current_index >= len(base) - 1)
|
|
actual_high = next_candle["high"]
|
|
actual_low = next_candle["low"]
|
|
return current_state, reward, next_state, done, actual_high, actual_low
|
|
|
|
# --- Enhanced Training Loop ---
|
|
def train_on_historical_data(env, model, device, args, start_epoch, optimizer, scheduler):
|
|
lambda_trade = args.lambda_trade
|
|
# Load any saved total PnL from training cache:
|
|
training_cache = load_training_cache(TRAINING_CACHE_FILE)
|
|
total_pnl = training_cache.get("total_pnl", 0.0)
|
|
for epoch in range(start_epoch, args.epochs):
|
|
env.reset()
|
|
loss_accum = 0.0
|
|
steps = len(env) - 1
|
|
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"]
|
|
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)
|
|
L_pred = torch.abs(pred_high - torch.tensor(actual_high, device=device)) + \
|
|
torch.abs(pred_low - torch.tensor(actual_low, device=device))
|
|
profit_buy = pred_high - current_open
|
|
profit_sell = current_open - pred_low
|
|
L_trade = - torch.max(profit_buy, profit_sell)
|
|
current_open_tensor = torch.tensor(current_open, device=device)
|
|
signal_strength = torch.max(pred_high - current_open_tensor, current_open_tensor - pred_low)
|
|
penalty_term = args.penalty_noaction * torch.clamp(args.threshold - signal_strength, min=0)
|
|
loss = L_pred + lambda_trade * L_trade + penalty_term
|
|
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
|
|
# If no trades occurred during the epoch, multiply the loss by 3.
|
|
if len(env.trade_history) == 0:
|
|
epoch_loss *= 3
|
|
epoch_pnl = sum(trade["pnl"] for trade in env.trade_history)
|
|
total_pnl += epoch_pnl
|
|
print(f"Epoch {epoch+1} Loss: {epoch_loss:.4f} | Epoch PnL: {epoch_pnl:.2f} | Total PnL: {total_pnl:.2f}")
|
|
save_checkpoint(model, optimizer, epoch, loss_accum)
|
|
simulate_trades(model, env, device, args)
|
|
update_live_html(env.candle_window, env.trade_history, epoch+1, epoch_loss, total_pnl)
|
|
# Update training cache with the new total PnL:
|
|
training_cache["total_pnl"] = total_pnl
|
|
save_training_cache(TRAINING_CACHE_FILE, training_cache)
|
|
|
|
# --- Live Plotting (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=1000)
|
|
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 (used in loss; model may override manual trades).")
|
|
parser.add_argument('--lambda_trade', type=float, default=1.0,
|
|
help="Weight for the trade surrogate loss.")
|
|
parser.add_argument('--penalty_noaction', type=float, default=10.0,
|
|
help="Penalty if no action is taken (used in loss).")
|
|
parser.add_argument('--start_fresh', action='store_true', help="Start training from scratch.")
|
|
parser.add_argument('--main_tf', type=str, default='1m',
|
|
help="Desired main timeframe to focus on (e.g., '1s' or '1m').")
|
|
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)
|
|
# Load cached candles.
|
|
candles_dict = load_candles_cache(CACHE_FILE)
|
|
# If the desired main timeframe is 1s, attempt to fetch real 1s data.
|
|
if args.main_tf == "1s":
|
|
real_1s_data = fetch_real_1s_data()
|
|
if real_1s_data:
|
|
candles_dict["1s"] = real_1s_data
|
|
if not candles_dict:
|
|
print("No historical candle data available for backtesting.")
|
|
return
|
|
# Define desired timeframes list; if available, include "1s".
|
|
default_timeframes = ["1s", "1m", "5m", "15m", "1h", "1d"]
|
|
timeframes = [tf for tf in default_timeframes if tf in candles_dict]
|
|
if args.main_tf not in timeframes:
|
|
print(f"Desired main timeframe {args.main_tf} is not available. Available: {timeframes}")
|
|
return
|
|
base_tf = args.main_tf # Set the main timeframe as the base for the environment.
|
|
hidden_dim = 128
|
|
# Total channels: number of timeframes + 1 order channel + NUM_INDICATORS.
|
|
total_channels = len(timeframes) + ORDER_CHANNELS + NUM_INDICATORS
|
|
model = TradingModel(total_channels, len(timeframes)).to(device)
|
|
|
|
if args.mode == 'train':
|
|
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:
|
|
try:
|
|
optimizer.load_state_dict(optim_state)
|
|
print("Loaded optimizer state from checkpoint.")
|
|
except Exception as e:
|
|
print("Failed to load optimizer state due to:", e)
|
|
print("Deleting all checkpoints and starting fresh.")
|
|
for chk_dir in [LAST_DIR, BEST_DIR]:
|
|
for f in os.listdir(chk_dir):
|
|
os.remove(os.path.join(chk_dir, f))
|
|
else:
|
|
print("No valid optimizer state found; using fresh optimizer state.")
|
|
train_on_historical_data(env, model, device, args, start_epoch, optimizer, scheduler)
|
|
|
|
elif args.mode == 'live':
|
|
load_best_checkpoint(model)
|
|
env = BacktestEnvironment(candles_dict, base_tf, 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. (For main_tf={} using manual override if model signal is weak.)".format(args.main_tf))
|
|
while True:
|
|
if args.main_tf == "1s":
|
|
simulate_trades_1s(env)
|
|
else:
|
|
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) > args.threshold or (current_open - pred_low) > args.threshold:
|
|
if (pred_high - current_open) >= (current_open - pred_low):
|
|
action = 2
|
|
else:
|
|
action = 0
|
|
_, _, _, done, _, _ = env.step(action)
|
|
else:
|
|
manual_trade(env)
|
|
if env.current_index >= len(env.candle_window)-1:
|
|
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...")
|
|
# Inference logic goes here.
|
|
else:
|
|
print("Invalid mode specified.")
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main()) |