488 lines
19 KiB
Python
488 lines
19 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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from dotenv import load_dotenv
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import os
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import time
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import json
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import ccxt.async_support as ccxt
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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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import numpy as np
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from collections import deque
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from datetime import datetime
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import matplotlib.pyplot as plt
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# --- Directories for saving models ---
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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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# -------------------------------------
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# Utility functions for caching candles to file
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# -------------------------------------
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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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# -------------------------------------
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# Checkpoint Functions (same as before)
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# -------------------------------------
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def maintain_checkpoint_directory(directory, max_files=10):
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files = os.listdir(directory)
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if len(files) > max_files:
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full_paths = [os.path.join(directory, f) for f in files]
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full_paths.sort(key=lambda x: os.path.getmtime(x))
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for f in full_paths[: len(files) - max_files]:
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os.remove(f)
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def get_best_models(directory):
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best_files = []
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for file in os.listdir(directory):
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parts = file.split("_")
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try:
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r = float(parts[1])
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best_files.append((r, file))
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except Exception:
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continue
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return best_files
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def save_checkpoint(model, epoch, reward, last_dir=LAST_DIR, best_dir=BEST_DIR):
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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last_filename = f"model_last_epoch_{epoch}_{timestamp}.pt"
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last_path = os.path.join(last_dir, last_filename)
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torch.save({
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"epoch": epoch,
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"reward": reward,
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"model_state_dict": model.state_dict()
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}, last_path)
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maintain_checkpoint_directory(last_dir, max_files=10)
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best_models = get_best_models(best_dir)
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add_to_best = False
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if len(best_models) < 10:
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add_to_best = True
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else:
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min_reward, min_file = min(best_models, key=lambda x: x[0])
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if reward > min_reward:
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add_to_best = True
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os.remove(os.path.join(best_dir, min_file))
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if add_to_best:
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best_filename = f"best_{reward:.4f}_epoch_{epoch}_{timestamp}.pt"
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best_path = os.path.join(best_dir, best_filename)
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torch.save({
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"epoch": epoch,
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"reward": reward,
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"model_state_dict": model.state_dict()
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}, best_path)
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maintain_checkpoint_directory(best_dir, max_files=10)
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print(f"Saved checkpoint for epoch {epoch} with reward {reward:.4f}")
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def load_best_checkpoint(model, best_dir=BEST_DIR):
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best_models = get_best_models(best_dir)
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if not best_models:
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return None
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best_reward, best_file = max(best_models, key=lambda x: x[0])
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path = os.path.join(best_dir, best_file)
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print(f"Loading best model from checkpoint: {best_file} with reward {best_reward:.4f}")
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checkpoint = torch.load(path)
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model.load_state_dict(checkpoint["model_state_dict"])
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return checkpoint
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# -------------------------------------
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# Technical Indicator Helper Functions
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# -------------------------------------
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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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"""Find the candle in the list whose timestamp is the largest that is <= 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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"""Return a vector of 7 features: open, high, low, close, volume, sma_close, sma_volume."""
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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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# -------------------------------------
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# Neural Network Architecture Definition
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# -------------------------------------
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class TradingModel(nn.Module):
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def __init__(self, input_dim, hidden_dim, output_dim):
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super(TradingModel, self).__init__()
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self.net = nn.Sequential(
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nn.Linear(input_dim, hidden_dim),
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nn.ReLU(),
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nn.Linear(hidden_dim, hidden_dim),
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nn.ReLU(),
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nn.Linear(hidden_dim, output_dim)
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)
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def forward(self, x):
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return self.net(x)
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# -------------------------------------
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# Replay Buffer for Experience Storage
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# -------------------------------------
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class ReplayBuffer:
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def __init__(self, capacity=10000):
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self.buffer = deque(maxlen=capacity)
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def add(self, experience):
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self.buffer.append(experience)
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def sample(self, batch_size):
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indices = np.random.choice(len(self.buffer), size=batch_size, replace=False)
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return [self.buffer[i] for i in indices]
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def __len__(self):
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return len(self.buffer)
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# -------------------------------------
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# RL Agent with Q-Learning and Epsilon-Greedy Exploration
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# -------------------------------------
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class ContinuousRLAgent:
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def __init__(self, model, optimizer, replay_buffer, batch_size=32, gamma=0.99):
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self.model = model
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self.optimizer = optimizer
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self.replay_buffer = replay_buffer
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self.batch_size = batch_size
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self.loss_fn = nn.MSELoss()
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self.gamma = gamma
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def act(self, state, epsilon=0.1):
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if np.random.rand() < epsilon:
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return np.random.randint(0, 3)
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state_tensor = torch.from_numpy(np.array(state, dtype=np.float32)).unsqueeze(0)
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with torch.no_grad():
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output = self.model(state_tensor)
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return torch.argmax(output, dim=1).item()
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def train_step(self):
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if len(self.replay_buffer) < self.batch_size:
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return
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batch = self.replay_buffer.sample(self.batch_size)
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states, actions, rewards, next_states, dones = zip(*batch)
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states_tensor = torch.from_numpy(np.array(states, dtype=np.float32))
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actions_tensor = torch.tensor(actions, dtype=torch.int64)
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rewards_tensor = torch.from_numpy(np.array(rewards, dtype=np.float32)).unsqueeze(1)
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next_states_tensor = torch.from_numpy(np.array(next_states, dtype=np.float32))
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dones_tensor = torch.tensor(dones, dtype=torch.float32).unsqueeze(1)
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Q_values = self.model(states_tensor)
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current_Q = Q_values.gather(1, actions_tensor.unsqueeze(1))
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with torch.no_grad():
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next_Q_values = self.model(next_states_tensor)
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max_next_Q = next_Q_values.max(1)[0].unsqueeze(1)
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target = rewards_tensor + self.gamma * max_next_Q * (1.0 - dones_tensor)
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loss = self.loss_fn(current_Q, target)
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self.optimizer.zero_grad()
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loss.backward()
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self.optimizer.step()
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# -------------------------------------
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# Historical Data Fetching Function (for a given timeframe)
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# -------------------------------------
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async def fetch_historical_data(exchange, symbol, timeframe, since, end_time, batch_size=500):
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candles = []
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since_ms = since
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while True:
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try:
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batch = await exchange.fetch_ohlcv(symbol, timeframe=timeframe, since=since_ms, limit=batch_size)
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except Exception as e:
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print(f"Error fetching historical data for {timeframe}:", e)
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break
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if not batch:
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break
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for c in batch:
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candle_dict = {
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'timestamp': c[0],
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'open': c[1],
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'high': c[2],
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'low': c[3],
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'close': c[4],
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'volume': c[5]
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}
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candles.append(candle_dict)
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last_timestamp = batch[-1][0]
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if last_timestamp >= end_time:
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break
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since_ms = last_timestamp + 1
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print(f"Fetched {len(candles)} candles for timeframe {timeframe}.")
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return candles
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# -------------------------------------
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# Backtest Environment with Multi-Timeframe State
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# -------------------------------------
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class BacktestEnvironment:
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def __init__(self, candles_dict, base_tf="1m", timeframes=None):
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self.candles_dict = candles_dict # dict of timeframe: candles_list
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self.base_tf = base_tf
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if timeframes is None:
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self.timeframes = [base_tf] # fallback to single timeframe
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else:
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self.timeframes = timeframes
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self.trade_history = [] # record of closed trades
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self.current_index = 0 # index on base_tf candles
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self.position = None # active position record
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def reset(self, clear_trade_history=True):
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self.current_index = 0
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self.position = None
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if clear_trade_history:
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self.trade_history = []
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return self.get_state(self.current_index)
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def get_state(self, index):
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"""Construct the state as the concatenated features of all timeframes.
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For each timeframe, find the aligned candle for the base timeframe’s timestamp."""
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state_features = []
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base_candle = self.candles_dict[self.base_tf][index]
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base_ts = base_candle["timestamp"]
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for tf in self.timeframes:
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candles_list = self.candles_dict[tf]
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# Get the candle from this timeframe that is closest to (and <=) base_ts.
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aligned_index, _ = get_aligned_candle_with_index(candles_list, base_ts)
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features = get_features_for_tf(candles_list, aligned_index, period=10)
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state_features.extend(features)
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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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Simulate a trading step based on the base timeframe.
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- If not in a position and action is BUY (2), record entry at next candle's open.
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- If in a position and action is SELL (0), record exit at next candle's open, computing PnL.
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Returns: (current_state, reward, next_state, done)
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"""
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base_candles = self.candles_dict[self.base_tf]
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if self.current_index >= len(base_candles) - 1:
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return self.get_state(self.current_index), 0.0, None, True
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current_state = self.get_state(self.current_index)
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next_index = self.current_index + 1
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next_state = self.get_state(next_index)
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current_candle = base_candles[self.current_index]
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next_candle = base_candles[next_index]
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reward = 0.0
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# Action mapping: 0 -> SELL, 1 -> HOLD, 2 -> BUY.
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if self.position is None:
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if action == 2: # BUY signal: enter position at next candle's open.
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entry_price = next_candle["open"]
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self.position = {"entry_price": entry_price, "entry_index": self.current_index}
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else:
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if action == 0: # SELL signal: close position at next candle's 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_candles) - 1)
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return current_state, reward, next_state, done
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# -------------------------------------
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# Chart Plotting: Trade History & PnL
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# -------------------------------------
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def plot_trade_history(candles, trade_history):
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close_prices = [candle["close"] for candle in candles]
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x = list(range(len(close_prices)))
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plt.figure(figsize=(12, 6))
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plt.plot(x, close_prices, label="Close Price", color="black", linewidth=1)
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# Use these flags so that the label "BUY" or "SELL" is only shown once in the legend.
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buy_label_added = False
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sell_label_added = False
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for trade in trade_history:
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in_idx = trade["entry_index"]
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out_idx = trade["exit_index"]
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in_price = trade["entry_price"]
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out_price = trade["exit_price"]
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pnl = trade["pnl"]
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# Plot BUY marker ("IN")
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if not buy_label_added:
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plt.plot(in_idx, in_price, marker="^", color="green", markersize=10, label="BUY (IN)")
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buy_label_added = True
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else:
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plt.plot(in_idx, in_price, marker="^", color="green", markersize=10)
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plt.text(in_idx, in_price, " IN", color="green", fontsize=8, verticalalignment="bottom")
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# Plot SELL marker ("OUT")
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if not sell_label_added:
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plt.plot(out_idx, out_price, marker="v", color="red", markersize=10, label="SELL (OUT)")
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sell_label_added = True
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else:
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plt.plot(out_idx, out_price, marker="v", color="red", markersize=10)
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plt.text(out_idx, out_price, " OUT", color="red", fontsize=8, verticalalignment="top")
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# Annotate the PnL near the SELL marker.
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plt.text(out_idx, out_price, f" {pnl:+.2f}", color="blue", fontsize=8, verticalalignment="bottom")
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# Choose line color based on profitability.
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if pnl > 0:
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line_color = "green"
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elif pnl < 0:
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line_color = "red"
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else:
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line_color = "gray"
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# Draw a dotted line between the buy and sell points.
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plt.plot([in_idx, out_idx], [in_price, out_price], linestyle="dotted", color=line_color)
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plt.title("Trade History with PnL")
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plt.xlabel("Base Candle Index (1m)")
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plt.ylabel("Price")
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plt.legend()
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plt.grid(True)
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plt.show()
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# -------------------------------------
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# Training Loop: Backtesting Trading Episodes
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# -------------------------------------
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def train_on_historical_data(env, rl_agent, num_epochs=10, epsilon=0.1):
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for epoch in range(1, num_epochs + 1):
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state = env.reset() # clear trade history each epoch
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done = False
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total_reward = 0.0
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steps = 0
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while not done:
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action = rl_agent.act(state, epsilon=epsilon)
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prev_state = state
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state, reward, next_state, done = env.step(action)
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if next_state is None:
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next_state = np.zeros_like(prev_state)
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rl_agent.replay_buffer.add((prev_state, action, reward, next_state, done))
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rl_agent.train_step()
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total_reward += reward
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steps += 1
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print(f"Epoch {epoch}/{num_epochs} completed, total reward: {total_reward:.4f} over {steps} steps.")
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save_checkpoint(rl_agent.model, epoch, total_reward, LAST_DIR, BEST_DIR)
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# -------------------------------------
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# Main Asynchronous Function for Training & Charting
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# -------------------------------------
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async def main_backtest():
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symbol = 'BTC/USDT'
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# Define timeframes: we'll use 5 different ones.
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timeframes = ["1m", "5m", "15m", "1h", "1d"]
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now = int(time.time() * 1000)
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# Use the base timeframe period of 1500 candles. For 1m, that is 1500 minutes.
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period_ms = 1500 * 60 * 1000
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since = now - period_ms
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end_time = now
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# Initialize exchange using MEXC (or your preferred exchange).
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mexc_api_key = os.environ.get('MEXC_API_KEY', 'YOUR_API_KEY')
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mexc_api_secret = os.environ.get('MEXC_API_SECRET', 'YOUR_SECRET_KEY')
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exchange = ccxt.mexc({
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'apiKey': mexc_api_key,
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'secret': mexc_api_secret,
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'enableRateLimit': True,
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})
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candles_dict = {}
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for tf in timeframes:
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print(f"Fetching historical data for timeframe {tf}...")
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candles = await fetch_historical_data(exchange, symbol, tf, since, end_time, batch_size=500)
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candles_dict[tf] = candles
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# Optionally, save the multi-timeframe cache.
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save_candles_cache(CACHE_FILE, candles_dict)
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# Create the backtest environment using multi-timeframe data.
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env = BacktestEnvironment(candles_dict, base_tf="1m", timeframes=timeframes)
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# Neural Network dimensions: each timeframe produces 7 features.
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input_dim = len(timeframes) * 7 # 7 features * 5 timeframes = 35.
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hidden_dim = 128
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output_dim = 3 # Actions: SELL, HOLD, BUY.
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model = TradingModel(input_dim, hidden_dim, output_dim)
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optimizer = optim.Adam(model.parameters(), lr=1e-4)
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replay_buffer = ReplayBuffer(capacity=10000)
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rl_agent = ContinuousRLAgent(model, optimizer, replay_buffer, batch_size=32, gamma=0.99)
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||
# Load best checkpoint if available.
|
||
load_best_checkpoint(model, BEST_DIR)
|
||
|
||
# Train the agent over the historical period.
|
||
num_epochs = 10 # Adjust as needed.
|
||
train_on_historical_data(env, rl_agent, num_epochs=num_epochs, epsilon=0.1)
|
||
|
||
# Run a final simulation (without exploration) to record trade history.
|
||
state = env.reset(clear_trade_history=True)
|
||
done = False
|
||
cumulative_reward = 0.0
|
||
while not done:
|
||
action = rl_agent.act(state, epsilon=0.0)
|
||
state, reward, next_state, done = env.step(action)
|
||
cumulative_reward += reward
|
||
state = next_state
|
||
print("Final simulation cumulative profit:", cumulative_reward)
|
||
|
||
# Evaluate trade performance.
|
||
trades = env.trade_history
|
||
num_trades = len(trades)
|
||
num_wins = sum(1 for trade in trades if trade["pnl"] > 0)
|
||
win_rate = (num_wins / num_trades * 100) if num_trades > 0 else 0.0
|
||
total_profit = sum(trade["pnl"] for trade in trades)
|
||
print(f"Total trades: {num_trades}, Wins: {num_wins}, Win rate: {win_rate:.2f}%, Total Profit: {total_profit:.4f}")
|
||
|
||
# Plot chart with buy/sell markers on the base timeframe ("1m").
|
||
plot_trade_history(candles_dict["1m"], trades)
|
||
|
||
await exchange.close()
|
||
|
||
if __name__ == "__main__":
|
||
load_dotenv()
|
||
asyncio.run(main_backtest()) |