popov/gogo2
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

wip

Browse Source
This commit is contained in:
Dobromir Popov
2025-02-04 17:58:00 +02:00
parent c6b4830daf
commit f5b1692a82
2 changed files with 703 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

488
crypto/brian/index.py Normal file
View File

@ -0,0 +1,488 @@
#!/usr/bin/env python3
import sys
import asyncio
if sys.platform == 'win32':
asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
from dotenv import load_dotenv
import os
import time
import json
import ccxt.async_support as ccxt
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from collections import deque
from datetime import datetime
import matplotlib.pyplot as plt
# --- Directories for saving models ---
LAST_DIR = os.path.join("models", "last")
BEST_DIR = os.path.join("models", "best")
os.makedirs(LAST_DIR, exist_ok=True)
os.makedirs(BEST_DIR, exist_ok=True)
CACHE_FILE = "candles_cache.json"
# -------------------------------------
# Utility functions for caching candles to file
# -------------------------------------
def load_candles_cache(filename):
if os.path.exists(filename):
try:
with open(filename, "r") as f:
data = json.load(f)
print(f"Loaded cached data from {filename}.")
return data
except Exception as e:
print("Error reading cache file:", e)
return {}
def save_candles_cache(filename, candles_dict):
try:
with open(filename, "w") as f:
json.dump(candles_dict, f)
except Exception as e:
print("Error saving cache file:", e)
# -------------------------------------
# Checkpoint Functions (same as before)
# -------------------------------------
def maintain_checkpoint_directory(directory, max_files=10):
files = os.listdir(directory)
if len(files) > max_files:
full_paths = [os.path.join(directory, f) for f in files]
full_paths.sort(key=lambda x: os.path.getmtime(x))
for f in full_paths[: len(files) - max_files]:
os.remove(f)
def get_best_models(directory):
best_files = []
for file in os.listdir(directory):
parts = file.split("_")
try:
r = float(parts[1])
best_files.append((r, file))
except Exception:
continue
return best_files
def save_checkpoint(model, epoch, reward, last_dir=LAST_DIR, best_dir=BEST_DIR):
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
last_filename = f"model_last_epoch_{epoch}_{timestamp}.pt"
last_path = os.path.join(last_dir, last_filename)
torch.save({
"epoch": epoch,
"reward": reward,
"model_state_dict": model.state_dict()
}, last_path)
maintain_checkpoint_directory(last_dir, max_files=10)
best_models = get_best_models(best_dir)
add_to_best = False
if len(best_models) < 10:
add_to_best = True
else:
min_reward, min_file = min(best_models, key=lambda x: x[0])
if reward > min_reward:
add_to_best = True
os.remove(os.path.join(best_dir, min_file))
if add_to_best:
best_filename = f"best_{reward:.4f}_epoch_{epoch}_{timestamp}.pt"
best_path = os.path.join(best_dir, best_filename)
torch.save({
"epoch": epoch,
"reward": reward,
"model_state_dict": model.state_dict()
}, best_path)
maintain_checkpoint_directory(best_dir, max_files=10)
print(f"Saved checkpoint for epoch {epoch} with reward {reward:.4f}")
def load_best_checkpoint(model, best_dir=BEST_DIR):
best_models = get_best_models(best_dir)
if not best_models:
return None
best_reward, best_file = max(best_models, key=lambda x: x[0])
path = os.path.join(best_dir, best_file)
print(f"Loading best model from checkpoint: {best_file} with reward {best_reward:.4f}")
checkpoint = torch.load(path)
model.load_state_dict(checkpoint["model_state_dict"])
return checkpoint
# -------------------------------------
# Technical Indicator Helper Functions
# -------------------------------------
def compute_sma(candles_list, index, period=10):
start = max(0, index - period + 1)
values = [candle["close"] for candle in candles_list[start:index+1]]
return sum(values) / len(values) if values else 0.0
def compute_sma_volume(candles_list, index, period=10):
start = max(0, index - period + 1)
values = [candle["volume"] for candle in candles_list[start:index+1]]
return sum(values) / len(values) if values else 0.0
def get_aligned_candle_with_index(candles_list, target_ts):
"""Find the candle in the list whose timestamp is the largest that is <= target_ts."""
best_idx = 0
for i, candle in enumerate(candles_list):
if candle["timestamp"] <= target_ts:
best_idx = i
else:
break
return best_idx, candles_list[best_idx]
def get_features_for_tf(candles_list, index, period=10):
"""Return a vector of 7 features: open, high, low, close, volume, sma_close, sma_volume."""
candle = candles_list[index]
f_open = candle["open"]
f_high = candle["high"]
f_low = candle["low"]
f_close = candle["close"]
f_volume = candle["volume"]
sma_close = compute_sma(candles_list, index, period)
sma_volume = compute_sma_volume(candles_list, index, period)
return [f_open, f_high, f_low, f_close, f_volume, sma_close, sma_volume]
# -------------------------------------
# Neural Network Architecture Definition
# -------------------------------------
class TradingModel(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(TradingModel, self).__init__()
self.net = nn.Sequential(
nn.Linear(input_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, output_dim)
)
def forward(self, x):
return self.net(x)
# -------------------------------------
# Replay Buffer for Experience Storage
# -------------------------------------
class ReplayBuffer:
def __init__(self, capacity=10000):
self.buffer = deque(maxlen=capacity)
def add(self, experience):
self.buffer.append(experience)
def sample(self, batch_size):
indices = np.random.choice(len(self.buffer), size=batch_size, replace=False)
return [self.buffer[i] for i in indices]
def __len__(self):
return len(self.buffer)
# -------------------------------------
# RL Agent with Q-Learning and Epsilon-Greedy Exploration
# -------------------------------------
class ContinuousRLAgent:
def __init__(self, model, optimizer, replay_buffer, batch_size=32, gamma=0.99):
self.model = model
self.optimizer = optimizer
self.replay_buffer = replay_buffer
self.batch_size = batch_size
self.loss_fn = nn.MSELoss()
self.gamma = gamma
def act(self, state, epsilon=0.1):
if np.random.rand() < epsilon:
return np.random.randint(0, 3)
state_tensor = torch.from_numpy(np.array(state, dtype=np.float32)).unsqueeze(0)
with torch.no_grad():
output = self.model(state_tensor)
return torch.argmax(output, dim=1).item()
def train_step(self):
if len(self.replay_buffer) < self.batch_size:
return
batch = self.replay_buffer.sample(self.batch_size)
states, actions, rewards, next_states, dones = zip(*batch)
states_tensor = torch.from_numpy(np.array(states, dtype=np.float32))
actions_tensor = torch.tensor(actions, dtype=torch.int64)
rewards_tensor = torch.from_numpy(np.array(rewards, dtype=np.float32)).unsqueeze(1)
next_states_tensor = torch.from_numpy(np.array(next_states, dtype=np.float32))
dones_tensor = torch.tensor(dones, dtype=torch.float32).unsqueeze(1)
Q_values = self.model(states_tensor)
current_Q = Q_values.gather(1, actions_tensor.unsqueeze(1))
with torch.no_grad():
next_Q_values = self.model(next_states_tensor)
max_next_Q = next_Q_values.max(1)[0].unsqueeze(1)
target = rewards_tensor + self.gamma * max_next_Q * (1.0 - dones_tensor)
loss = self.loss_fn(current_Q, target)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# -------------------------------------
# Historical Data Fetching Function (for a given timeframe)
# -------------------------------------
async def fetch_historical_data(exchange, symbol, timeframe, since, end_time, batch_size=500):
candles = []
since_ms = since
while True:
try:
batch = await exchange.fetch_ohlcv(symbol, timeframe=timeframe, since=since_ms, limit=batch_size)
except Exception as e:
print(f"Error fetching historical data for {timeframe}:", e)
break
if not batch:
break
for c in batch:
candle_dict = {
'timestamp': c[0],
'open': c[1],
'high': c[2],
'low': c[3],
'close': c[4],
'volume': c[5]
}
candles.append(candle_dict)
last_timestamp = batch[-1][0]
if last_timestamp >= end_time:
break
since_ms = last_timestamp + 1
print(f"Fetched {len(candles)} candles for timeframe {timeframe}.")
return candles
# -------------------------------------
# Backtest Environment with Multi-Timeframe State
# -------------------------------------
class BacktestEnvironment:
def __init__(self, candles_dict, base_tf="1m", timeframes=None):
self.candles_dict = candles_dict # dict of timeframe: candles_list
self.base_tf = base_tf
if timeframes is None:
self.timeframes = [base_tf] # fallback to single timeframe
else:
self.timeframes = timeframes
self.trade_history = [] # record of closed trades
self.current_index = 0 # index on base_tf candles
self.position = None # active position record
def reset(self, clear_trade_history=True):
self.current_index = 0
self.position = None
if clear_trade_history:
self.trade_history = []
return self.get_state(self.current_index)
def get_state(self, index):
"""Construct the state as the concatenated features of all timeframes.
For each timeframe, find the aligned candle for the base timeframe’s timestamp."""
state_features = []
base_candle = self.candles_dict[self.base_tf][index]
base_ts = base_candle["timestamp"]
for tf in self.timeframes:
candles_list = self.candles_dict[tf]
# Get the candle from this timeframe that is closest to (and <=) base_ts.
aligned_index, _ = get_aligned_candle_with_index(candles_list, base_ts)
features = get_features_for_tf(candles_list, aligned_index, period=10)
state_features.extend(features)
return np.array(state_features, dtype=np.float32)
def step(self, action):
"""
Simulate a trading step based on the base timeframe.
- If not in a position and action is BUY (2), record entry at next candle's open.
- If in a position and action is SELL (0), record exit at next candle's open, computing PnL.
Returns: (current_state, reward, next_state, done)
"""
base_candles = self.candles_dict[self.base_tf]
if self.current_index >= len(base_candles) - 1:
return self.get_state(self.current_index), 0.0, None, True
current_state = self.get_state(self.current_index)
next_index = self.current_index + 1
next_state = self.get_state(next_index)
current_candle = base_candles[self.current_index]
next_candle = base_candles[next_index]
reward = 0.0
# Action mapping: 0 -> SELL, 1 -> HOLD, 2 -> BUY.
if self.position is None:
if action == 2: # BUY signal: enter position at next candle's open.
entry_price = next_candle["open"]
self.position = {"entry_price": entry_price, "entry_index": self.current_index}
else:
if action == 0: # SELL signal: close position at next candle's open.
exit_price = next_candle["open"]
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_candles) - 1)
return current_state, reward, next_state, done
# -------------------------------------
# Chart Plotting: Trade History & PnL
# -------------------------------------
def plot_trade_history(candles, trade_history):
close_prices = [candle["close"] for candle in candles]
x = list(range(len(close_prices)))
plt.figure(figsize=(12, 6))
plt.plot(x, close_prices, label="Close Price", color="black", linewidth=1)
# Use these flags so that the label "BUY" or "SELL" is only shown once in the legend.
buy_label_added = False
sell_label_added = False
for trade in trade_history:
in_idx = trade["entry_index"]
out_idx = trade["exit_index"]
in_price = trade["entry_price"]
out_price = trade["exit_price"]
pnl = trade["pnl"]
# Plot BUY marker ("IN")
if not buy_label_added:
plt.plot(in_idx, in_price, marker="^", color="green", markersize=10, label="BUY (IN)")
buy_label_added = True
else:
plt.plot(in_idx, in_price, marker="^", color="green", markersize=10)
plt.text(in_idx, in_price, " IN", color="green", fontsize=8, verticalalignment="bottom")
# Plot SELL marker ("OUT")
if not sell_label_added:
plt.plot(out_idx, out_price, marker="v", color="red", markersize=10, label="SELL (OUT)")
sell_label_added = True
else:
plt.plot(out_idx, out_price, marker="v", color="red", markersize=10)
plt.text(out_idx, out_price, " OUT", color="red", fontsize=8, verticalalignment="top")
# Annotate the PnL near the SELL marker.
plt.text(out_idx, out_price, f" {pnl:+.2f}", color="blue", fontsize=8, verticalalignment="bottom")
# Choose line color based on profitability.
if pnl > 0:
line_color = "green"
elif pnl < 0:
line_color = "red"
else:
line_color = "gray"
# Draw a dotted line between the buy and sell points.
plt.plot([in_idx, out_idx], [in_price, out_price], linestyle="dotted", color=line_color)
plt.title("Trade History with PnL")
plt.xlabel("Base Candle Index (1m)")
plt.ylabel("Price")
plt.legend()
plt.grid(True)
plt.show()
# -------------------------------------
# Training Loop: Backtesting Trading Episodes
# -------------------------------------
def train_on_historical_data(env, rl_agent, num_epochs=10, epsilon=0.1):
for epoch in range(1, num_epochs + 1):
state = env.reset() # clear trade history each epoch
done = False
total_reward = 0.0
steps = 0
while not done:
action = rl_agent.act(state, epsilon=epsilon)
prev_state = state
state, reward, next_state, done = env.step(action)
if next_state is None:
next_state = np.zeros_like(prev_state)
rl_agent.replay_buffer.add((prev_state, action, reward, next_state, done))
rl_agent.train_step()
total_reward += reward
steps += 1
print(f"Epoch {epoch}/{num_epochs} completed, total reward: {total_reward:.4f} over {steps} steps.")
save_checkpoint(rl_agent.model, epoch, total_reward, LAST_DIR, BEST_DIR)
# -------------------------------------
# Main Asynchronous Function for Training & Charting
# -------------------------------------
async def main_backtest():
symbol = 'BTC/USDT'
# Define timeframes: we'll use 5 different ones.
timeframes = ["1m", "5m", "15m", "1h", "1d"]
now = int(time.time() * 1000)
# Use the base timeframe period of 1500 candles. For 1m, that is 1500 minutes.
period_ms = 1500 * 60 * 1000
since = now - period_ms
end_time = now
# Initialize exchange using MEXC (or your preferred exchange).
mexc_api_key = os.environ.get('MEXC_API_KEY', 'YOUR_API_KEY')
mexc_api_secret = os.environ.get('MEXC_API_SECRET', 'YOUR_SECRET_KEY')
exchange = ccxt.mexc({
'apiKey': mexc_api_key,
'secret': mexc_api_secret,
'enableRateLimit': True,
})
candles_dict = {}
for tf in timeframes:
print(f"Fetching historical data for timeframe {tf}...")
candles = await fetch_historical_data(exchange, symbol, tf, since, end_time, batch_size=500)
candles_dict[tf] = candles
# Optionally, save the multi-timeframe cache.
save_candles_cache(CACHE_FILE, candles_dict)
# Create the backtest environment using multi-timeframe data.
env = BacktestEnvironment(candles_dict, base_tf="1m", timeframes=timeframes)
# Neural Network dimensions: each timeframe produces 7 features.
input_dim = len(timeframes) * 7 # 7 features * 5 timeframes = 35.
hidden_dim = 128
output_dim = 3 # Actions: SELL, HOLD, BUY.
model = TradingModel(input_dim, hidden_dim, output_dim)
optimizer = optim.Adam(model.parameters(), lr=1e-4)
replay_buffer = ReplayBuffer(capacity=10000)
rl_agent = ContinuousRLAgent(model, optimizer, replay_buffer, batch_size=32, gamma=0.99)
# 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())