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This commit is contained in:
Dobromir Popov 2025-03-29 04:09:03 +02:00
parent 43803caaf1
commit 8b3db10a85
3 changed files with 307 additions and 267 deletions
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177
NN/models/cnn_model_pytorch.py
View File

@ -11,6 +11,7 @@ import logging
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
import math
import torch
import torch.nn as nn
@ -24,79 +25,84 @@ logger = logging.getLogger(__name__)
class CNNPyTorch(nn.Module):
"""PyTorch CNN model for time series analysis"""
def __init__(self, input_shape, output_size=5):
def __init__(self, input_shape, output_size=3):
"""
Initialize the enhanced CNN model.
Initialize the CNN model.
Args:
input_shape (tuple): Shape of input data (window_size, features)
output_size (int): Always 5 for our trading signals
output_size (int): Size of the output (3 for BUY/HOLD/SELL)
"""
super(CNNPyTorch, self).__init__()
window_size, num_features = input_shape
kernel_size = 5
kernel_size = min(5, window_size) # Ensure kernel size doesn't exceed window size
dropout_rate = 0.3
# Enhanced CNN Architecture
# Calculate initial channel size based on number of features
initial_channels = max(32, num_features * 2) # Scale channels with features
# CNN Architecture
self.conv_layers = nn.Sequential(
# Block 1
nn.Conv1d(num_features, 64, kernel_size, padding='same'),
nn.BatchNorm1d(64),
nn.Conv1d(num_features, initial_channels, kernel_size, padding='same'),
nn.BatchNorm1d(initial_channels),
nn.ReLU(),
nn.Dropout(dropout_rate),
# Block 2
nn.Conv1d(64, 128, kernel_size, padding='same'),
nn.BatchNorm1d(128),
nn.Conv1d(initial_channels, initial_channels * 2, kernel_size, padding='same'),
nn.BatchNorm1d(initial_channels * 2),
nn.ReLU(),
nn.MaxPool1d(2),
nn.Dropout(dropout_rate),
# Block 3
nn.Conv1d(128, 256, kernel_size, padding='same'),
nn.BatchNorm1d(256),
nn.Conv1d(initial_channels * 2, initial_channels * 4, kernel_size, padding='same'),
nn.BatchNorm1d(initial_channels * 4),
nn.ReLU(),
nn.Dropout(dropout_rate),
# Block 4
nn.Conv1d(256, 512, kernel_size, padding='same'),
nn.BatchNorm1d(512),
nn.Conv1d(initial_channels * 4, initial_channels * 8, kernel_size, padding='same'),
nn.BatchNorm1d(initial_channels * 8),
nn.ReLU(),
nn.MaxPool1d(2)
nn.MaxPool1d(2),
nn.Dropout(dropout_rate)
)
# Calculate flattened size after conv and pooling
conv_output_size = 512 * (window_size // 4)
conv_output_size = (initial_channels * 8) * (window_size // 4)
# Dense layers with scaled sizes
dense_size = min(2048, conv_output_size) # Cap dense layer size
# Enhanced dense layers
self.dense_block = nn.Sequential(
nn.Flatten(),
nn.Linear(conv_output_size, 512),
nn.BatchNorm1d(512),
nn.Linear(conv_output_size, dense_size),
nn.BatchNorm1d(dense_size),
nn.ReLU(),
nn.Dropout(dropout_rate),
nn.Linear(512, 256),
nn.BatchNorm1d(256),
nn.Linear(dense_size, dense_size // 2),
nn.BatchNorm1d(dense_size // 2),
nn.ReLU(),
nn.Dropout(dropout_rate),
nn.Linear(256, 128),
nn.BatchNorm1d(128),
nn.Linear(dense_size // 2, dense_size // 4),
nn.BatchNorm1d(dense_size // 4),
nn.ReLU(),
nn.Dropout(dropout_rate),
nn.Linear(128, output_size)
nn.Linear(dense_size // 4, output_size)
)
# Activation based on output size
if output_size == 1:
self.activation = nn.Sigmoid() # Binary classification or regression
elif output_size > 1:
self.activation = nn.Softmax(dim=1) # Multi-class classification
else:
self.activation = nn.Identity() # No activation
# Activation for output
self.activation = nn.Softmax(dim=1)
def forward(self, x):
"""
Forward pass through enhanced network.
Forward pass through the network.
Args:
x: Input tensor of shape [batch_size, window_size, features]
@ -107,14 +113,16 @@ class CNNPyTorch(nn.Module):
# Transpose for conv1d: [batch, features, window]
x_t = x.transpose(1, 2)
# Process through all CNN layers
# Process through CNN layers
conv_out = self.conv_layers(x_t)
# Process through dense layers
output = self.dense_block(conv_out)
dense_out = self.dense_block(conv_out)
return self.activation(output)
# Apply activation
output = self.activation(dense_out)
return output
class CNNModelPyTorch:
"""
@ -124,14 +132,14 @@ class CNNModelPyTorch:
predictions with the CNN model.
"""
def __init__(self, window_size, num_features, output_size=5, timeframes=None):
def __init__(self, window_size, num_features, output_size=3, timeframes=None):
"""
Initialize the CNN model.
Args:
window_size (int): Size of the input window
num_features (int): Number of features in the input data
output_size (int): Size of the output (1 for regression, 3 for classification)
output_size (int): Size of the output (default: 3 for BUY/HOLD/SELL)
timeframes (list): List of timeframes used (for logging)
"""
# Action tracking
@ -171,26 +179,22 @@ class CNNModelPyTorch:
output_size=self.output_size
).to(self.device)
# Initialize optimizer
# Initialize optimizer with learning rate schedule
self.optimizer = optim.Adam(self.model.parameters(), lr=0.001)
self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(
self.optimizer, mode='max', factor=0.5, patience=10, verbose=True
)
# Initialize loss function based on output size
if self.output_size == 1:
self.criterion = nn.BCELoss() # Binary classification
elif self.output_size > 1:
self.criterion = nn.CrossEntropyLoss() # Multi-class classification
else:
self.criterion = nn.MSELoss() # Regression
# Initialize loss function with class weights
class_weights = torch.tensor([1.0, 0.5, 1.0]).to(self.device) # Lower weight for HOLD
self.criterion = nn.CrossEntropyLoss(weight=class_weights)
logger.info(f"Model built successfully with {sum(p.numel() for p in self.model.parameters())} parameters")
def train_epoch(self, X_train, y_train, batch_size=32):
def train_epoch(self, X_train, y_train, future_prices=None, batch_size=32):
"""Train for one epoch and return loss and accuracy"""
# Convert to PyTorch tensors
X_train_tensor = torch.tensor(X_train, dtype=torch.float32).to(self.device)
if self.output_size == 1:
y_train_tensor = torch.tensor(y_train, dtype=torch.float32).to(self.device)
else:
y_train_tensor = torch.tensor(y_train, dtype=torch.long).to(self.device)
# Create DataLoader
@ -210,18 +214,18 @@ class CNNModelPyTorch:
outputs = self.model(inputs)
# Calculate loss
if self.output_size == 1:
loss = self.criterion(outputs, targets.unsqueeze(1))
else:
loss = self.criterion(outputs, targets)
# Backward pass and optimize
loss.backward()
# Clip gradients to prevent exploding gradients
torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0)
self.optimizer.step()
# Statistics
running_loss += loss.item()
if self.output_size > 1:
_, predicted = torch.max(outputs, 1)
total += targets.size(0)
correct += (predicted == targets).sum().item()
@ -229,21 +233,25 @@ class CNNModelPyTorch:
epoch_loss = running_loss / len(train_loader)
epoch_acc = correct / total if total > 0 else 0
return epoch_loss, epoch_acc
# Update learning rate scheduler
self.scheduler.step(epoch_acc)
def evaluate(self, X_val, y_val):
# To maintain compatibility with the updated training code, we'll return 3 values
# But the price_loss will be zero since we're not using that in this model
return epoch_loss, 0.0, epoch_acc
def evaluate(self, X_val, y_val, future_prices=None):
"""Evaluate on validation data and return loss and accuracy"""
# Convert to PyTorch tensors
X_val_tensor = torch.tensor(X_val, dtype=torch.float32).to(self.device)
if self.output_size == 1:
y_val_tensor = torch.tensor(y_val, dtype=torch.float32).to(self.device)
else:
y_val_tensor = torch.tensor(y_val, dtype=torch.long).to(self.device)
# Create DataLoader
val_dataset = TensorDataset(X_val_tensor, y_val_tensor)
val_loader = DataLoader(val_dataset, batch_size=32)
self.model.eval()
val_loss = 0.0
running_loss = 0.0
correct = 0
total = 0
@ -253,20 +261,20 @@ class CNNModelPyTorch:
outputs = self.model(inputs)
# Calculate loss
if self.output_size == 1:
loss = self.criterion(outputs, targets.unsqueeze(1))
else:
loss = self.criterion(outputs, targets)
val_loss += loss.item()
running_loss += loss.item()
# Calculate accuracy
if self.output_size > 1:
_, predicted = torch.max(outputs, 1)
total += targets.size(0)
correct += (predicted == targets).sum().item()
return val_loss / len(val_loader), correct / total if total > 0 else 0
val_loss = running_loss / len(val_loader)
val_acc = correct / total if total > 0 else 0
# To maintain compatibility with the updated training code, we'll return 3 values
# But the price_loss will be zero since we're not using that in this model
return val_loss, 0.0, val_acc
def predict(self, X):
"""Make predictions on input data"""
@ -275,15 +283,13 @@ class CNNModelPyTorch:
with torch.no_grad():
outputs = self.model(X_tensor)
if self.output_size > 1:
_, predicted = torch.max(outputs, 1)
return predicted.cpu().numpy()
else:
return outputs.cpu().numpy()
# To maintain compatibility with the transformer model, return the action probs
# And a dummy price prediction of zeros
return outputs.cpu().numpy(), np.zeros((len(X), 1))
def predict_next_candles(self, X, n_candles=3):
"""
Predict the next n candles for each timeframe.
Predict the next n candles.
Args:
X: Input data of shape [batch_size, window_size, features]
@ -296,33 +302,14 @@ class CNNModelPyTorch:
X_tensor = torch.tensor(X, dtype=torch.float32).to(self.device)
with torch.no_grad():
# Get the last window of data
last_window = X_tensor[-1:] # [1, window_size, features]
# Get predictions for the input window
action_probs = self.model(X_tensor)
# Initialize predictions
# For compatibility, we'll return a dictionary with the timeframes
predictions = {}
# For each timeframe, predict next n candles
for i, tf in enumerate(self.timeframes):
# Extract features for this timeframe
tf_features = last_window[:, :, i*5:(i+1)*5] # [1, window_size, 5]
# Predict next n candles
tf_predictions = []
current_window = tf_features
for _ in range(n_candles):
# Get prediction for next candle
output = self.model(current_window)
tf_predictions.append(output.cpu().numpy())
# Update window for next prediction
current_window = torch.cat([
current_window[:, 1:, :],
output.unsqueeze(1)
], dim=1)
predictions[tf] = np.concatenate(tf_predictions, axis=0)
# Simple prediction: just repeat the current prediction for next n candles
predictions[tf] = np.tile(action_probs.cpu().numpy(), (n_candles, 1))
return predictions

123
NN/realtime_main.py
View File

@ -148,19 +148,29 @@ def train(data_interface, model, args):
best_val_acc = 0
best_val_pnl = float('-inf')
best_win_rate = 0
best_price_mae = float('inf')
logger.info("Verifying data interface...")
X_sample, y_sample, _, _, _, _ = data_interface.prepare_training_data(refresh=True)
logger.info(f"Data validation - X shape: {X_sample.shape}, y shape: {y_sample.shape}")
# Calculate refresh intervals based on timeframes
min_timeframe = min(args.timeframes)
refresh_interval = {
'1s': 1,
'1m': 60,
'5m': 300,
'15m': 900,
'1h': 3600,
'4h': 14400,
'1d': 86400
}.get(min_timeframe, 60)
logger.info(f"Using refresh interval of {refresh_interval} seconds based on {min_timeframe} timeframe")
for epoch in range(args.epochs):
# More frequent refresh for shorter timeframes
if '1s' in args.timeframes:
refresh = True # Always refresh for tick data
refresh_interval = 30 # 30 seconds for tick data
else:
refresh = epoch % 1 == 0 # Refresh every epoch
refresh_interval = 120 # 2 minutes for other timeframes
# Always refresh for tick data or when using multiple timeframes
refresh = '1s' in args.timeframes or len(args.timeframes) > 1
logger.info(f"\nStarting epoch {epoch+1}/{args.epochs}")
X_train, y_train, X_val, y_val, train_prices, val_prices = data_interface.prepare_training_data(
@ -170,86 +180,106 @@ def train(data_interface, model, args):
logger.info(f"Training data - X shape: {X_train.shape}, y shape: {y_train.shape}")
logger.info(f"Validation data - X shape: {X_val.shape}, y shape: {y_val.shape}")
# Get future prices for retrospective training
train_future_prices = data_interface.get_future_prices(train_prices, n_candles=3)
val_future_prices = data_interface.get_future_prices(val_prices, n_candles=3)
# Train and validate
try:
train_loss, train_acc = model.train_epoch(X_train, y_train, args.batch_size)
val_loss, val_acc = model.evaluate(X_val, y_val)
train_action_loss, train_price_loss, train_acc = model.train_epoch(
X_train, y_train, train_future_prices, args.batch_size
)
val_action_loss, val_price_loss, val_acc = model.evaluate(
X_val, y_val, val_future_prices
)
# Get predictions for PnL calculation
train_preds = model.predict(X_train)
val_preds = model.predict(X_val)
train_action_probs, train_price_preds = model.predict(X_train)
val_action_probs, val_price_preds = model.predict(X_val)
# Calculate PnL and win rates
train_pnl, train_win_rate, train_trades = data_interface.calculate_pnl(
train_preds, train_prices, position_size=1.0
train_action_probs, train_prices, position_size=1.0
)
val_pnl, val_win_rate, val_trades = data_interface.calculate_pnl(
val_preds, val_prices, position_size=1.0
val_action_probs, val_prices, position_size=1.0
)
# Calculate price prediction error
train_price_mae = np.mean(np.abs(train_price_preds - train_future_prices))
val_price_mae = np.mean(np.abs(val_price_preds - val_future_prices))
# Monitor action distribution
train_actions = np.bincount(train_preds, minlength=3)
val_actions = np.bincount(val_preds, minlength=3)
train_actions = np.bincount(np.argmax(train_action_probs, axis=1), minlength=3)
val_actions = np.bincount(np.argmax(val_action_probs, axis=1), minlength=3)
# Log metrics
writer.add_scalar('Loss/train', train_loss, epoch)
writer.add_scalar('Loss/action_train', train_action_loss, epoch)
writer.add_scalar('Loss/price_train', train_price_loss, epoch)
writer.add_scalar('Loss/action_val', val_action_loss, epoch)
writer.add_scalar('Loss/price_val', val_price_loss, epoch)
writer.add_scalar('Accuracy/train', train_acc, epoch)
writer.add_scalar('Loss/val', val_loss, epoch)
writer.add_scalar('Accuracy/val', val_acc, epoch)
writer.add_scalar('PnL/train', train_pnl, epoch)
writer.add_scalar('PnL/val', val_pnl, epoch)
writer.add_scalar('WinRate/train', train_win_rate, epoch)
writer.add_scalar('WinRate/val', val_win_rate, epoch)
writer.add_scalar('PriceMAE/train', train_price_mae, epoch)
writer.add_scalar('PriceMAE/val', val_price_mae, epoch)
# Log action distribution
for i, action in enumerate(['SELL', 'HOLD', 'BUY']):
writer.add_scalar(f'Actions/train_{action}', train_actions[i], epoch)
writer.add_scalar(f'Actions/val_{action}', val_actions[i], epoch)
# Save best model based on validation PnL
if val_pnl > best_val_pnl:
# Save best model based on validation metrics
if val_pnl > best_val_pnl or (val_pnl == best_val_pnl and val_acc > best_val_acc):
best_val_pnl = val_pnl
best_val_acc = val_acc
best_win_rate = val_win_rate
best_price_mae = val_price_mae
model.save(f"models/{args.model_type}_best.pt")
logger.info("Saved new best model based on validation metrics")
# Log detailed metrics
logger.info(f"Epoch {epoch+1}/{args.epochs} - "
f"Train Loss: {train_loss:.4f}, Acc: {train_acc:.2f}, "
f"PnL: {train_pnl:.2%}, Win Rate: {train_win_rate:.2%} - "
f"Val Loss: {val_loss:.4f}, Acc: {val_acc:.2f}, "
f"PnL: {val_pnl:.2%}, Win Rate: {val_win_rate:.2%}")
logger.info(f"Epoch {epoch+1}/{args.epochs}")
logger.info("Training Metrics:")
logger.info(f" Action Loss: {train_action_loss:.4f}")
logger.info(f" Price Loss: {train_price_loss:.4f}")
logger.info(f" Accuracy: {train_acc:.2f}")
logger.info(f" PnL: {train_pnl:.2%}")
logger.info(f" Win Rate: {train_win_rate:.2%}")
logger.info(f" Price MAE: {train_price_mae:.2f}")
logger.info("Validation Metrics:")
logger.info(f" Action Loss: {val_action_loss:.4f}")
logger.info(f" Price Loss: {val_price_loss:.4f}")
logger.info(f" Accuracy: {val_acc:.2f}")
logger.info(f" PnL: {val_pnl:.2%}")
logger.info(f" Win Rate: {val_win_rate:.2%}")
logger.info(f" Price MAE: {val_price_mae:.2f}")
# Log action distribution
logger.info("Action Distribution:")
for i, action in enumerate(['SELL', 'HOLD', 'BUY']):
logger.info(f"{action}: Train={train_actions[i]}, Val={val_actions[i]}")
logger.info(f" {action}: Train={train_actions[i]}, Val={val_actions[i]}")
# Log trade statistics
if train_trades:
logger.info(f"Training trades: {len(train_trades)}")
logger.info(f"Validation trades: {len(val_trades)}")
logger.info("Trade Statistics:")
logger.info(f" Training trades: {len(train_trades)}")
logger.info(f" Validation trades: {len(val_trades)}")
# Retrospective fine-tuning
if epoch > 0 and val_pnl > 0: # Only fine-tune if we're making profit
logger.info("Performing retrospective fine-tuning...")
# Get predictions for next few candles
# Log next candle predictions
if epoch % 10 == 0: # Every 10 epochs
logger.info("\nNext Candle Predictions:")
next_candles = model.predict_next_candles(X_val[-1:], n_candles=3)
# Log predictions for each timeframe
for tf, preds in next_candles.items():
logger.info(f"Next 3 candles for {tf}:")
for i, pred in enumerate(preds):
for tf in args.timeframes:
if tf in next_candles:
logger.info(f"\n{tf} timeframe predictions:")
for i, pred in enumerate(next_candles[tf]):
action = ['SELL', 'HOLD', 'BUY'][np.argmax(pred)]
confidence = np.max(pred)
logger.info(f"Candle {i+1}: {action} (confidence: {confidence:.2f})")
# Fine-tune on recent successful trades
successful_trades = [t for t in train_trades if t['pnl'] > 0]
if successful_trades:
logger.info(f"Fine-tuning on {len(successful_trades)} successful trades")
# TODO: Implement fine-tuning logic here
logger.info(f" Candle {i+1}: {action} (confidence: {confidence:.2f})")
except Exception as e:
logger.error(f"Error during epoch {epoch+1}: {str(e)}")
@ -257,10 +287,11 @@ def train(data_interface, model, args):
# Save final model
model.save(f"models/{args.model_type}_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pt")
logger.info(f"Training complete. Best validation metrics:")
logger.info(f"\nTraining complete. Best validation metrics:")
logger.info(f"Accuracy: {best_val_acc:.2f}")
logger.info(f"PnL: {best_val_pnl:.2%}")
logger.info(f"Win Rate: {best_win_rate:.2%}")
logger.info(f"Price MAE: {best_price_mae:.2f}")
except Exception as e:
logger.error(f"Error in training: {str(e)}")

244
NN/utils/data_interface.py
View File

@ -61,6 +61,10 @@ class DataInterface:
"""
cache_file = os.path.join(self.data_dir, f"{self.symbol.replace('/', '_')}_{timeframe}.csv")
# For 1s timeframe, always fetch fresh data
if timeframe == '1s':
use_cache = False
# Check if cached data exists and is recent
if use_cache and os.path.exists(cache_file):
try:
@ -74,18 +78,18 @@ class DataInterface:
logger.error(f"Error reading cached data: {str(e)}")
# If we get here, we need to fetch data
# For now, we'll use a placeholder for fetching data from an exchange
try:
# In a real implementation, we would fetch data from an exchange or API here
# For this example, we'll create dummy data if we can't load from cache
logger.info(f"Fetching historical data for {self.symbol} {timeframe}")
# For 1s timeframe, we need more data points
if timeframe == '1s':
n_candles = min(n_candles * 60, 10000) # Up to 10k ticks
# Placeholder for real data fetching
# In a real implementation, this would be replaced with API calls
self._fetch_data_from_exchange(timeframe, n_candles)
# Save to cache
if self.dataframes[timeframe] is not None:
# Save to cache (except for 1s timeframe)
if self.dataframes[timeframe] is not None and timeframe != '1s':
self.dataframes[timeframe].to_csv(cache_file, index=False)
return self.dataframes[timeframe]
else:
@ -122,6 +126,7 @@ class DataInterface:
"""
# Map timeframe to seconds
tf_seconds = {
'1s': 1, # Added 1s timeframe
'1m': 60,
'5m': 300,
'15m': 900,
@ -207,59 +212,62 @@ class DataInterface:
# Get data for all requested timeframes
dfs = {}
min_length = float('inf')
for tf in timeframes:
df = self.get_historical_data(timeframe=tf, n_candles=n_candles)
# For 1s timeframe, we need more data points
tf_candles = n_candles * 60 if tf == '1s' else n_candles
df = self.get_historical_data(timeframe=tf, n_candles=tf_candles)
if df is not None and not df.empty:
dfs[tf] = df
# Keep track of minimum length across all timeframes
min_length = min(min_length, len(df))
if not dfs:
logger.error("No data available for feature creation")
return None, None, None
# Align all dataframes to the same length
for tf in dfs:
dfs[tf] = dfs[tf].tail(min_length)
# Create features for each timeframe
features = []
targets = []
timestamps = []
targets = None
timestamps = None
for tf in timeframes:
if tf in dfs:
X, y, ts = self._create_features(dfs[tf], window_size)
if X is not None and y is not None:
features.append(X)
if len(targets) == 0: # Only need targets from one timeframe
if targets is None: # Only need targets from one timeframe
targets = y
timestamps = ts
if not features:
if not features or targets is None:
logger.error("Failed to create features for any timeframe")
return None, None, None
# Stack features from all timeframes along the time dimension
# Reshape each timeframe's features to [samples, window, 1, features]
reshaped_features = [f.reshape(f.shape[0], f.shape[1], 1, f.shape[2])
for f in features]
# Concatenate along the channel dimension
X = np.concatenate(reshaped_features, axis=2)
# Reshape to [samples, window, features*timeframes]
X = X.reshape(X.shape[0], X.shape[1], -1)
# Ensure all feature arrays have the same length
min_samples = min(f.shape[0] for f in features)
features = [f[-min_samples:] for f in features]
targets = targets[-min_samples:]
timestamps = timestamps[-min_samples:]
# Stack features from all timeframes
X = np.concatenate([f.reshape(min_samples, window_size, -1) for f in features], axis=2)
# Validate data
if np.any(np.isnan(X)) or np.any(np.isinf(X)):
logger.error("Generated features contain NaN or infinite values")
return None, None, None
# Ensure all values are finite and normalized
X = np.nan_to_num(X, nan=0.0, posinf=1.0, neginf=-1.0)
X = np.clip(X, -1e6, 1e6) # Clip extreme values
# Log data shapes for debugging
logger.info(f"Prepared input data - X shape: {X.shape}, y shape: {np.array(targets).shape}")
logger.info(f"Prepared input data - X shape: {X.shape}, y shape: {targets.shape}")
return X, targets, timestamps
def _create_features(self, df, window_size):
"""
Create features from OHLCV data using a sliding window approach.
Create features from OHLCV data using a sliding window.
Args:
df (pd.DataFrame): DataFrame with OHLCV data
@ -267,76 +275,34 @@ class DataInterface:
Returns:
tuple: (X, y, timestamps) where:
X is the input features array
X is the feature array
y is the target array
timestamps is an array of timestamps for each sample
timestamps is the array of timestamps
"""
# Extract OHLCV columns
ohlcv = df[['open', 'high', 'low', 'close', 'volume']].values
# Validate data before scaling
if np.any(np.isnan(ohlcv)) or np.any(np.isinf(ohlcv)):
logger.error("Input data contains NaN or infinite values")
if len(df) < window_size + 1:
logger.error(f"Not enough data for feature creation (need {window_size + 1}, got {len(df)})")
return None, None, None
# Handle potential constant columns (avoid division by zero in scaler)
ohlcv = np.nan_to_num(ohlcv, nan=0.0)
ranges = np.ptp(ohlcv, axis=0)
for i in range(len(ranges)):
if ranges[i] == 0: # Constant column
ohlcv[:, i] = 1 if i == 3 else 0 # Set close to 1, others to 0
# Scale the data with safety checks
try:
scaler = MinMaxScaler()
ohlcv_scaled = scaler.fit_transform(ohlcv)
if np.any(np.isnan(ohlcv_scaled)) or np.any(np.isinf(ohlcv_scaled)):
logger.error("Scaling produced invalid values")
return None, None, None
except Exception as e:
logger.error(f"Scaling failed: {str(e)}")
return None, None, None
# Store the scaler for later use
timeframe = next((tf for tf in self.timeframes if self.dataframes.get(tf) is not None and
self.dataframes[tf].equals(df)), 'unknown')
self.scalers[timeframe] = scaler
# Extract OHLCV data
data = df[['open', 'high', 'low', 'close', 'volume']].values
timestamps = df['timestamp'].values
# Create sliding windows
X = []
y = []
timestamps = []
X = np.array([data[i:i+window_size] for i in range(len(data)-window_size)])
for i in range(len(ohlcv_scaled) - window_size):
# Input: window_size candles of OHLCV data
window = ohlcv_scaled[i:i+window_size]
# Create targets (next candle's movement: 0=down, 1=neutral, 2=up)
next_close = data[window_size:, 3] # Close prices
curr_close = data[window_size-1:-1, 3]
price_changes = (next_close - curr_close) / curr_close
# Validate window data
if np.any(np.isnan(window)) or np.any(np.isinf(window)):
continue
# Define thresholds for price movement classification
threshold = 0.001 # 0.1% threshold
y = np.zeros(len(price_changes), dtype=int)
y[price_changes > threshold] = 2 # Up
y[(price_changes >= -threshold) & (price_changes <= threshold)] = 1 # Neutral
X.append(window)
# Target: binary classification - price goes up (1) or down (0)
# 1 if close price increases in the next candle, 0 otherwise
price_change = ohlcv[i+window_size, 3] - ohlcv[i+window_size-1, 3]
y.append(1 if price_change > 0 else 0)
# Store timestamp for reference
timestamps.append(df['timestamp'].iloc[i+window_size])
if not X:
logger.error("No valid windows created")
return None, None, None
X = np.array(X)
y = np.array(y)
timestamps = np.array(timestamps)
# Log shapes for debugging
logger.info(f"Created features - X shape: {X.shape}, y shape: {y.shape}")
return X, y, timestamps
return X, y, timestamps[window_size:]
def generate_training_dataset(self, timeframes=None, n_candles=1000, window_size=20):
"""
@ -406,21 +372,28 @@ class DataInterface:
return dataset_info
def get_feature_count(self):
"""Get the number of features per input sample"""
# OHLCV (5 features) per timeframe
return 5 * len(self.timeframes)
"""
Calculate total number of features across all timeframes.
Returns:
int: Total number of features (5 features per timeframe)
"""
return len(self.timeframes) * 5 # OHLCV features for each timeframe
def calculate_pnl(self, predictions, actual_prices, position_size=1.0):
"""
Calculate PnL based on predictions and actual price movements.
Calculate PnL and win rates based on predictions and actual price movements.
Args:
predictions (np.array): Model predictions (0: sell, 1: hold, 2: buy)
actual_prices (np.array): Actual price data
position_size (float): Size of the position to trade
predictions: Array of predicted actions (0=SELL, 1=HOLD, 2=BUY) or probabilities
actual_prices: Array of actual close prices
position_size: Position size for each trade
Returns:
tuple: (total_pnl, win_rate, trade_history)
tuple: (pnl, win_rate, trades) where:
pnl is the total profit and loss
win_rate is the ratio of winning trades
trades is a list of trade dictionaries
"""
if len(predictions) != len(actual_prices) - 1:
logger.error("Predictions and prices length mismatch")
@ -468,36 +441,85 @@ class DataInterface:
win_rate = wins / trades if trades > 0 else 0.0
return pnl, win_rate, trade_history
def prepare_training_data(self, refresh=False, refresh_interval=300):
def get_future_prices(self, prices, n_candles=3):
"""
Prepare training and validation data with optional refresh.
Extract future prices for use in retrospective training.
Args:
refresh (bool): Whether to force refresh data
refresh_interval (int): Minimum seconds between refreshes
prices: Array of close prices
n_candles: Number of future candles to predict
Returns:
tuple: (X_train, y_train, X_val, y_val, prices) numpy arrays
numpy.ndarray: Array of future prices for each sample
"""
if prices is None or len(prices) <= n_candles:
logger.warning(f"Not enough price data for future prediction: {len(prices) if prices is not None else 0} prices")
# Return zeros if not enough data
return np.zeros((len(prices) if prices is not None else 0, 1))
# For each price point i, get the price at i+n_candles
future_prices = np.zeros((len(prices), 1))
for i in range(len(prices) - n_candles):
future_prices[i, 0] = prices[i + n_candles]
# For the last n_candles positions, we don't have future data
# We'll use the last known price as a placeholder
for i in range(len(prices) - n_candles, len(prices)):
future_prices[i, 0] = prices[-1]
return future_prices
def prepare_training_data(self, refresh=False, refresh_interval=300):
"""
Prepare data for training, including splitting into train/validation sets.
Args:
refresh (bool): Whether to refresh the data cache
refresh_interval (int): Interval in seconds to refresh data
Returns:
tuple: (X_train, y_train, X_val, y_val, train_prices, val_prices)
"""
current_time = datetime.now()
if refresh or (current_time - getattr(self, 'last_refresh', datetime.min)).total_seconds() > refresh_interval:
logger.info("Refreshing training data...")
for tf in self.timeframes:
self.get_historical_data(timeframe=tf, n_candles=1000, use_cache=False)
self.last_refresh = current_time
# Get all data
# Check if we should refresh the data
if refresh or not hasattr(self, 'last_refresh_time') or \
(current_time - self.last_refresh_time).total_seconds() > refresh_interval:
logger.info("Refreshing training data...")
self.last_refresh_time = current_time
else:
# Use cached data
if hasattr(self, 'cached_train_data'):
return self.cached_train_data
# Prepare input data
X, y, _ = self.prepare_nn_input()
if X is None:
return None, None, None, None, None
return None, None, None, None, None, None
# Get price data for PnL calculation
prices = self.dataframes[self.timeframes[0]]['close'].values
raw_prices = []
for tf in self.timeframes:
if tf in self.dataframes and self.dataframes[tf] is not None:
# Get the close prices for the same period as X
prices = self.dataframes[tf]['close'].values[-len(X):]
if len(prices) == len(X):
raw_prices = prices
break
# Split into train/validation (80/20)
if len(raw_prices) != len(X):
raw_prices = np.zeros(len(X)) # Fallback if no prices available
# Split data into training and validation sets (80/20)
split_idx = int(len(X) * 0.8)
return (X[:split_idx], y[:split_idx], X[split_idx:], y[split_idx:],
prices[:split_idx], prices[split_idx:])
X_train, X_val = X[:split_idx], X[split_idx:]
y_train, y_val = y[:split_idx], y[split_idx:]
train_prices, val_prices = raw_prices[:split_idx], raw_prices[split_idx:]
# Cache the data
self.cached_train_data = (X_train, y_train, X_val, y_val, train_prices, val_prices)
return X_train, y_train, X_val, y_val, train_prices, val_prices
def prepare_realtime_input(self, timeframe='1h', n_candles=30, window_size=20):
"""