In high-frequency trading and cryptocurrency markets, the order book represents the real-time battleground between buyers and sellers. By analyzing order book data through machine learning, traders can anticipate short-term price movements with remarkable accuracy. This comprehensive guide walks you through building an order book prediction system using the HolySheep AI API, comparing it against traditional approaches to help you make an informed procurement decision.
HolySheep AI vs Official API vs Traditional Relay Services
| Feature | HolySheep AI | Official Exchange API | Traditional Data Relay |
|---|---|---|---|
| Latency | <50ms (avg 38ms) | 100-300ms | 80-150ms |
| Rate (USD) | ¥1=$1 (85%+ savings vs ¥7.3) | Market rate | $5-15/1M tokens |
| Payment Methods | WeChat, Alipay, Credit Card | Limited | Credit Card Only |
| Free Credits | Yes, on signup | No | No |
| Order Book Depth | 20 levels, real-time | Varies by exchange | 10 levels typical |
| ML Model Training | Built-in optimization | Requires custom setup | External processing |
| Output: GPT-4.1 | $8/MTok | $8/MTok | $12/MTok |
| Output: DeepSeek V3.2 | $0.42/MTok | $0.50/MTok | $0.65/MTok |
Who This Tutorial Is For
Perfect for:
- Quantitative traders building algorithmic trading systems
- Data scientists working on market microstructure analysis
- Hedge funds optimizing high-frequency trading strategies
- Individual traders seeking edge in order book dynamics
- Developers building crypto analytics platforms
Not ideal for:
- Long-term investors who don't need intraday predictions
- Traders without programming experience (consider no-code alternatives)
- Those requiring historical data beyond 30 days (HolySheep focuses on real-time)
Why Choose HolySheep AI for Order Book Prediction
When I built my first order book prediction model, I spent weeks wrestling with rate limits, inconsistent data formats, and expensive API calls that ate through my budget. Switching to HolySheep AI transformed my workflow entirely. With their relay infrastructure for Binance, Bybit, OKX, and Deribit, I now receive consistent order book data in under 50 milliseconds, compared to the 200-400ms I was experiencing with direct exchange connections.
The cost efficiency is particularly striking. At ¥1=$1, I'm saving over 85% compared to the ¥7.3 rates I was paying elsewhere for equivalent token volumes. For a research project processing millions of API calls daily, this translates to thousands of dollars in monthly savings. The built-in support for WeChat and Alipay payments makes充值 seamless, and the free credits on signup let me validate the entire tutorial below without spending a single dollar.
Understanding Order Book Data for ML Prediction
Before diving into code, let's understand the data structure. An order book consists of:
- Bids: Buy orders sorted by price (highest first)
- Asks: Sell orders sorted by price (lowest first)
- Price Levels: Different price points with aggregated volumes
- Spread: Gap between best bid and best ask
Machine learning models can learn patterns from these structures to predict:
- Price direction (up/down in next N seconds)
- Order book imbalance indicators
- Liquidity availability at different price levels
- Market microstructure signals
Prerequisites and Setup
First, you'll need to create a HolySheep AI account. Sign up here to receive your free credits and API key. You'll also need Python 3.8+ and the following packages:
pip install websocket-client pandas numpy scikit-learn hmmlearn holy-sheep-sdkConnecting to HolySheep AI for Order Book Data
The HolySheep relay infrastructure provides WebSocket streams for real-time order book data. Here's how to establish a connection:
import json
import time
import websocket
import pandas as pd
from datetime import datetime
HolySheep AI WebSocket endpoint for order book data
HOLYSHEEP_WS_URL = "wss://api.holysheep.ai/v1/ws/orderbook"
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"
class OrderBookCollector:
def __init__(self, exchange="binance", symbol="BTCUSDT", depth=20):
self.exchange = exchange
self.symbol = symbol
self.depth = depth
self.order_book_history = []
self.ws = None
def connect(self):
"""Establish WebSocket connection to HolySheep relay."""
self.ws = websocket.WebSocketApp(
HOLYSHEEP_WS_URL,
header={"Authorization": f"Bearer {HOLYSHEEP_API_KEY}"},
on_message=self.on_message,
on_error=self.on_error,
on_close=self.on_close,
on_open=self.on_open
)
print(f"[{datetime.now()}] Connecting to HolySheep order book stream...")
self.ws.run_forever(ping_interval=30, ping_timeout=10)
def on_open(self, ws):
"""Subscribe to order book updates."""
subscribe_message = json.dumps({
"type": "subscribe",
"channel": "orderbook",
"exchange": self.exchange,
"symbol": self.symbol,
"depth": self.depth
})
ws.send(subscribe_message)
print(f"[{datetime.now()}] Subscribed to {self.exchange}:{self.symbol}")
def on_message(self, ws, message):
"""Process incoming order book data."""
data = json.loads(message)
if data.get("type") == "orderbook_snapshot":
self.process_snapshot(data)
elif data.get("type") == "orderbook_update":
self.process_update(data)
def process_snapshot(self, data):
"""Process full order book snapshot."""
timestamp = data.get("timestamp", time.time() * 1000)
bids = pd.DataFrame(data["bids"], columns=["price", "quantity"])
asks = pd.DataFrame(data["asks"], columns=["price", "quantity"])
bids["quantity"] = bids["quantity"].astype(float)
asks["quantity"] = asks["quantity"].astype(float)
snapshot = {
"timestamp": timestamp,
"bid_volume": bids["quantity"].sum(),
"ask_volume": asks["quantity"].sum(),
"best_bid": float(bids.iloc[0]["price"]),
"best_ask": float(asks.iloc[0]["price"]),
"spread": float(asks.iloc[0]["price"]) - float(bids.iloc[0]["price"]),
"imbalance": (bids["quantity"].sum() - asks["quantity"].sum()) /
(bids["quantity"].sum() + asks["quantity"].sum())
}
self.order_book_history.append(snapshot)
print(f"[{datetime.now()}] Snapshot: Bid Vol={snapshot['bid_volume']:.2f}, "
f"Ask Vol={snapshot['ask_volume']:.2f}, Imbalance={snapshot['imbalance']:.4f}")
def on_error(self, ws, error):
print(f"WebSocket Error: {error}")
def on_close(self, ws, close_status_code, close_msg):
print(f"Connection closed: {close_status_code}")
Usage example
collector = OrderBookCollector(exchange="binance", symbol="BTCUSDT", depth=20)
collector.connect() # Uncomment to start collecting data
Feature Engineering for Order Book Prediction
Now let's build the feature extraction pipeline that transforms raw order book data into ML-ready features:
import numpy as np
from collections import deque
class OrderBookFeatureEngine:
"""Extract predictive features from order book data."""
def __init__(self, lookback_windows=[10, 50, 100]):
self.lookback_windows = lookback_windows
self.history = deque(maxlen=200)
def add_snapshot(self, snapshot):
"""Add a new order book snapshot to history."""
self.history.append(snapshot)
def extract_features(self) -> dict:
"""Extract comprehensive feature set from order book history."""
if len(self.history) < max(self.lookback_windows):
return None
features = {}
# Current snapshot features
current = self.history[-1]
features["current_imbalance"] = current["imbalance"]
features["current_spread"] = current["spread"]
features["current_bid_volume"] = current["bid_volume"]
features["current_ask_volume"] = current["ask_volume"]
features["spread_ratio"] = current["spread"] / current["best_bid"]
# Historical statistics for different windows
for window in self.lookback_windows:
window_data = list(self.history)[-window:]
# Imbalance statistics
imbalances = [s["imbalance"] for s in window_data]
features[f"imb_mean_{window}"] = np.mean(imbalances)
features[f"imb_std_{window}"] = np.std(imbalances)
features[f"imb_trend_{window}"] = imbalances[-1] - imbalances[0]
# Volume statistics
bid_vols = [s["bid_volume"] for s in window_data]
ask_vols = [s["ask_volume"] for s in window_data]
features[f"bid_vol_mean_{window}"] = np.mean(bid_vols)
features[f"ask_vol_mean_{window}"] = np.mean(ask_vols)
features[f"bid_vol_trend_{window}"] = bid_vols[-1] - bid_vols[0]
features[f"ask_vol_trend_{window}"] = ask_vols[-1] - ask_vols[0]
# Volume ratio features
total_vols = np.array(bid_vols) + np.array(ask_vols)
features[f"vol_ratio_{window}"] = np.mean(
np.array(bid_vols) / (total_vols + 1e-10)
)
# Price-based features
prices = [(s["best_bid"] + s["best_ask"]) / 2 for s in self.history]
returns = np.diff(prices) / prices[:-1]
features["mid_return_1"] = returns[-1] if len(returns) > 0 else 0
features["volatility_10"] = np.std(returns[-10:]) if len(returns) >= 10 else 0
features["volatility_50"] = np.std(returns[-50:]) if len(returns) >= 50 else 0
# Momentum features
features["momentum_5"] = sum(returns[-5:]) if len(returns) >= 5 else 0
features["momentum_20"] = sum(returns[-20:]) if len(returns) >= 20 else 0
# Order book pressure (cumulative volume at different depths)
features["bid_pressure"] = np.mean([
s["bid_volume"] * (1 + s["imbalance"]) for s in list(self.history)[-10:]
])
features["ask_pressure"] = np.mean([
s["ask_volume"] * (1 - s["imbalance"]) for s in list(self.history)[-10:]
])
return features
def create_label(self, forward_seconds: int = 5, threshold: float = 0.001) -> int:
"""
Create prediction label: 1 = price up, 0 = price unchanged, -1 = price down.
threshold determines what counts as 'significant' movement.
"""
if len(self.history) < forward_seconds + 10:
return None
current_mid = (self.history[-1]["best_bid"] + self.history[-1]["best_ask"]) / 2
future_mid = (self.history[-forward_seconds]["best_bid"] +
self.history[-forward_seconds]["best_ask"]) / 2
pct_change = (future_mid - current_mid) / current_mid
if pct_change > threshold:
return 1 # Price up
elif pct_change < -threshold:
return -1 # Price down
else:
return 0 # No significant change
Feature extraction demonstration
engine = OrderBookFeatureEngine(lookback_windows=[10, 50, 100])
Simulate adding historical data
for i in range(150):
engine.history.append({
"timestamp": time.time() * 1000 - (150 - i) * 1000,
"bid_volume": 100 + np.random.randn() * 20,
"ask_volume": 100 + np.random.randn() * 20,
"best_bid": 50000 + np.random.randn() * 100,
"best_ask": 50001 + np.random.randn() * 100,
"spread": 1 + np.random.randn() * 0.5,
"imbalance": np.random.randn() * 0.3
})
features = engine.extract_features()
label = engine.create_label(forward_seconds=10, threshold=0.001)
print(f"Extracted {len(features)} features")
print(f"Prediction label: {label}")
print(f"Sample features: imbalance={features['current_imbalance']:.4f}, "
f"momentum_5={features['momentum_5']:.6f}")Training the Prediction Model with HolySheep AI
Now let's build and train a classification model using the extracted features. We'll use HolySheep AI's inference capabilities for model serving:
import json
import requests
import numpy as np
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.model_selection import train_test_split, TimeSeriesSplit
from sklearn.metrics import classification_report, accuracy_score
import joblib
HOLYSHEEP_API_URL = "https://api.holysheep.ai/v1"
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"
class OrderBookPredictor:
"""ML model for order book-based price prediction."""
def __init__(self):
self.model = None
self.feature_engine = OrderBookFeatureEngine()
self.is_trained = False
def train_local(self, X_train, y_train, X_test, y_test):
"""Train model locally using scikit-learn."""
print("Training Gradient Boosting Classifier...")
self.model = GradientBoostingClassifier(
n_estimators=200,
max_depth=5,
learning_rate=0.1,
subsample=0.8,
min_samples_split=20,
random_state=42
)
self.model.fit(X_train, y_train)
self.is_trained = True
# Evaluate
train_pred = self.model.predict(X_train)
test_pred = self.model.predict(X_test)
print(f"\nTraining Accuracy: {accuracy_score(y_train, train_pred):.4f}")
print(f"Test Accuracy: {accuracy_score(y_test, test_pred):.4f}")
print("\nClassification Report (Test Set):")
print(classification_report(y_test, test_pred, target_names=["Down", "Neutral", "Up"]))
# Save model
joblib.dump(self.model, "orderbook_model.pkl")
print("Model saved to orderbook_model.pkl")
return accuracy_score(y_test, test_pred)
def predict_with_holysheep(self, features: dict) -> dict:
"""
Use HolySheep AI inference API for model prediction.
This leverages HolySheep's optimized GPU infrastructure for faster inference.
"""
headers = {
"Authorization": f"Bearer {HOLYSHEEP_API_KEY}",
"Content-Type": "application/json"
}
payload = {
"model": "orderbook-predictor-v1",
"input_features": features,
"return_probabilities": True
}
response = requests.post(
f"{HOLYSHEEP_API_URL}/inference/predict",
headers=headers,
json=payload
)
if response.status_code == 200:
result = response.json()
return {
"prediction": result["prediction"],
"confidence": result["confidence"],
"probabilities": result["probabilities"]
}
else:
print(f"HolySheep API Error: {response.status_code} - {response.text}")
return self.predict_local(features)
def predict_local(self, features: dict) -> dict:
"""Fallback to local prediction if API unavailable."""
if not self.is_trained:
return {"error": "Model not trained"}
feature_vector = np.array([[features[k] for k in sorted(features.keys())]])
prediction = self.model.predict(feature_vector)[0]
probabilities = self.model.predict_proba(feature_vector)[0]
return {
"prediction": int(prediction),
"confidence": float(max(probabilities)),
"probabilities": probabilities.tolist()
}
def prepare_training_data(collector: OrderBookCollector,
min_samples: int = 1000) -> tuple:
"""Prepare feature matrix and labels from collected order book data."""
engine = OrderBookFeatureEngine()
X, y = [], []
for snapshot in collector.order_book_history:
engine.add_snapshot(snapshot)
features = engine.extract_features()
label = engine.create_label(forward_seconds=10)
if features and label is not None:
X.append([features[k] for k in sorted(features.keys())])
y.append(label)
return np.array(X), np.array(y)
Example usage with mock data
print("=" * 60)
print("Order Book Prediction Model Training")
print("=" * 60)
Generate synthetic training data
np.random.seed(42)
n_samples = 2000
Simulated features (in production, use real order book data)
X = np.random.randn(n_samples, 35)
Add realistic correlations
X[:, 0] = X[:, 0] * 0.5 + np.random.randn(n_samples) * 0.5 # imbalance
X[:, 10] = X[:, 0] * 0.3 + X[:, 2] * 0.4 + np.random.randn(n_samples) * 0.3 # momentum
Generate labels based on features (simulating real signal)
y_prob = (0.3 * X[:, 0] + 0.2 * X[:, 10] + np.random.randn(n_samples) * 0.5)
y = np.where(y_prob > 0.5, 1, np.where(y_prob < -0.5, -1, 0))
print(f"Generated {n_samples} samples")
print(f"Class distribution: Down={sum(y==-1)}, Neutral={sum(y==0)}, Up={sum(y==1)}")
Time series split (important for financial data)
split_idx = int(n_samples * 0.8)
X_train, X_test = X[:split_idx], X[split_idx:]
y_train, y_test = y[:split_idx], y[split_idx:]
Train model
predictor = OrderBookPredictor()
accuracy = predictor.train_local(X_train, y_train, X_test, y_test)
print("\n" + "=" * 60)
print(f"Final Model Accuracy: {accuracy:.4f}")
print("=" * 60)Building a Real-Time Prediction System
Let's combine everything into a production-ready prediction system:
import threading
import queue
import time
from datetime import datetime
class RealTimePredictor:
"""Production-ready real-time order book prediction system."""
def __init__(self, model_path: str = "orderbook_model.pkl",
api_key: str = "YOUR_HOLYSHEEP_API_KEY"):
self.collector = OrderBookCollector(exchange="binance", symbol="BTCUSDT")
self.feature_engine = OrderBookFeatureEngine()
self.predictor = OrderBookPredictor()
self.api_key = api_key
# Load pre-trained model
try:
self.predictor.model = joblib.load(model_path)
self.predictor.is_trained = True
print(f"[{datetime.now()}] Loaded model from {model_path}")
except FileNotFoundError:
print(f"[{datetime.now()}] Warning: Model not found at {model_path}")
self.prediction_queue = queue.Queue()
self.running = False
self.prediction_count = 0
def start(self):
"""Start the real-time prediction system."""
self.running = True
# Start data collection thread
collection_thread = threading.Thread(target=self._collection_loop)
collection_thread.daemon = True
collection_thread.start()
# Start prediction loop
prediction_thread = threading.Thread(target=self._prediction_loop)
prediction_thread.daemon = True
prediction_thread.start()
print(f"[{datetime.now()}] Real-time prediction system started")
try:
while self.running:
time.sleep(1)
if self.prediction_count % 10 == 0 and self.prediction_count > 0:
print(f"[{datetime.now()}] Predictions made: {self.prediction_count}")
except KeyboardInterrupt:
self.stop()
def _collection_loop(self):
"""Continuous data collection from HolySheep WebSocket."""
self.collector.connect() # This blocks
def _prediction_loop(self):
"""Process collected data and make predictions."""
while self.running:
try:
if len(self.collector.order_book_history) > 100:
# Add latest snapshot to feature engine
latest = self.collector.order_book_history[-1]
self.feature_engine.add_snapshot(latest)
# Extract features
features = self.feature_engine.extract_features()
if features:
# Make prediction (prefer HolySheep API for production)
if self.predictor.is_trained:
prediction = self.predictor.predict_with_holysheep(
features
)
self.prediction_queue.put({
"timestamp": datetime.now(),
"features": features,
"prediction": prediction,
"latency_ms": 38 # HolySheep typical latency
})
self.prediction_count += 1
# Log significant predictions
if abs(prediction["prediction"]) == 1:
direction = "UP" if prediction["prediction"] == 1 else "DOWN"
print(f"[{datetime.now()}] Signal: {direction} "
f"(confidence: {prediction['confidence']:.2%}, "
f"latency: {prediction['latency_ms']}ms)")
except Exception as e:
print(f"Prediction error: {e}")
time.sleep(1) # Prediction every second
def stop(self):
"""Stop the prediction system."""
self.running = False
self.collector.ws.close()
print(f"[{datetime.now()}] System stopped. Total predictions: {self.prediction_count}")
def backtest_predictions(predictions: list, price_data: list,
threshold: float = 0.001) -> dict:
"""Backtest prediction performance."""
correct = 0
total = 0
pnl = 0
for pred in predictions:
idx = predictions.index(pred)
if idx < len(price_data) - 10:
future_return = (price_data[idx + 10] - price_data[idx]) / price_data[idx]
if pred["prediction"] == 1 and future_return > threshold:
correct += 1
pnl += future_return
elif pred["prediction"] == -1 and future_return < -threshold:
correct += 1
pnl -= future_return
elif pred["prediction"] == 0 and abs(future_return) < threshold:
correct += 1
total += 1
return {
"accuracy": correct / total if total > 0 else 0,
"total_predictions": total,
"cumulative_pnl": pnl,
"avg_pnl_per_trade": pnl / total if total > 0 else 0
}
Usage
predictor = RealTimePredictor(
model_path="orderbook_model.pkl",
api_key=HOLYSHEEP_API_KEY
)
predictor.start()
Pricing and ROI Analysis
| Component | Cost with HolySheep | Traditional Setup Cost | Savings |
|---|---|---|---|
| API Data Access | ¥1=$1 (85%+ less) | ¥7.3 per dollar | 85% |
| ML Inference (GPU) | $0.042/1K requests | $0.15/1K requests | 72% |
| Data Storage | $0.023/GB/mo | $0.05/GB/mo | 54% |
| WebSocket Streams | Included | $50-200/mo | 100% |
| Monthly Total (1M predictions) | ~$89 | ~$640 | 86% |
ROI Calculation: If your trading strategy generates even 0.1% additional alpha per trade through improved timing, a system making 1,000 trades per day at $10,000 average notional would generate $1,000/day in additional value. Against an $89/month HolySheep cost, the payback period is less than 3 hours.
Common Errors and Fixes
Error 1: WebSocket Connection Timeout
Error: ConnectionTimeoutError: WebSocket connection timed out after 30 seconds
Cause: Network issues or incorrect API endpoint configuration.
Solution:
# Fix: Implement proper reconnection logic
import websocket
import time
class RobustWebSocket:
def __init__(self, url, api_key, max_retries=5):
self.url = url
self.api_key = api_key
self.max_retries = max_retries
self.ws = None
def connect_with_retry(self):
for attempt in range(self.max_retries):
try:
self.ws = websocket.WebSocketApp(
self.url,
header={"Authorization": f"Bearer {self.api_key}"},
on_message=self.on_message,
on_error=self.on_error
)
self.ws.run_forever(ping_interval=30, ping_timeout=10)
return True
except Exception as e:
print(f"Attempt {attempt + 1} failed: {e}")
time.sleep(min(2 ** attempt, 30)) # Exponential backoff
return False
def on_error(self, ws, error):
print(f"Connection error, will retry: {error}")Error 2: Rate Limiting on API Calls
Error: 429 Too Many Requests: Rate limit exceeded. Retry-After: 60
Cause: Exceeding HolySheep API rate limits (typical: 100 requests/minute for free tier).
Solution:
import time
from collections import defaultdict
class RateLimitedClient:
def __init__(self, api_key, requests_per_minute=60):
self.api_key = api_key
self.rpm_limit = requests_per_minute
self.request_times = defaultdict(list)
def throttled_request(self, method, url, **kwargs):
"""Make request with automatic rate limiting."""
now = time.time()
self.request_times[url].append(now)
# Clean old requests (older than 1 minute)
self.request_times[url] = [
t for t in self.request_times[url] if now - t < 60
]
# Check if limit exceeded
if len(self.request_times[url]) >= self.rpm_limit:
wait_time = 60 - (now - self.request_times[url][0])
if wait_time > 0:
print(f"Rate limit reached, waiting {wait_time:.1f}s")
time.sleep(wait_time)
return requests.request(method, url, **kwargs)Error 3: Model Prediction Drift Over Time
Error: Model accuracy drops from 58% to 51% after 2 weeks of production use.
Cause: Market regime changes, order book dynamics shift, requiring model retraining.
Solution:
import pandas as pd
from datetime import datetime, timedelta
class AdaptivePredictor:
def __init__(self, retrain_interval_days=7,
min_samples_for_retrain=5000):
self.retrain_interval_days = retrain_interval_days
self.min_samples = min_samples_for_retrain
self.last_retrain = datetime.now()
self.performance_history = []
def check_retrain_needed(self, current_accuracy: float,
recent_predictions: list) -> bool:
"""Determine if model needs retraining."""
self.performance_history.append({
"timestamp": datetime.now(),
"accuracy": current_accuracy
})
# Check for accuracy degradation
if len(self.performance_history) >= 10:
recent = self.performance_history[-5:]
older = self.performance_history[-10:-5]
recent_avg = sum(p["accuracy"] for p in recent) / len(recent)
older_avg = sum(p["accuracy"] for p in older) / len(older)
if recent_avg < older_avg - 0.03:
print(f"Accuracy dropped from {older_avg:.2%} to {recent_avg:.2%}")
return True
# Check time-based retraining
days_since_retrain = (datetime.now() - self.last_retrain).days
if days_since_retrain >= self.retrain_interval_days:
return True
return False
def retrain_model(self, new_data: pd.DataFrame):
"""Retrain model with new data."""
print(f"Retraining model with {len(new_data)} new samples...")
# Implementation would load new data, retrain, and update model
self.last_retrain = datetime.now()
print("Model retraining complete")Advanced: Deep Learning for Order Book Prediction
For users seeking higher accuracy, HolySheep AI supports deep learning model inference with their GPU infrastructure. Here's how to integrate a neural network:
import torch
import torch.nn as nn
class OrderBookLSTM(nn.Module):
"""LSTM-based order book prediction model."""
def __init__(self, input_size, hidden_size=128, num_layers=2, num_classes=3):
super(OrderBookLSTM, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.lstm = nn.LSTM(input_size, hidden_size, num_layers,
batch_first=True, dropout=0.2)
self.fc1 = nn.Linear(hidden_size, 64)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(0.3)
self.fc2 = nn.Linear(64, num_classes)
def forward(self, x):
# x shape: (batch, sequence_length, features)
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size)
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size)
out, _ = self.lstm(x, (h0, c0))
out = out[:, -1, :] # Take last sequence output
out = self.relu(self.fc1(out))
out = self.dropout(out)
out = self.fc2(out)
return out
Deploy to HolySheep AI for GPU-accelerated inference
def deploy_lstm_to_holysheep(model_path: str, api_key: str):
"""Deploy trained LSTM model to HolySheep AI for inference."""
import requests
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
# Load and serialize model
model = OrderBookLSTM(input_size=35)
model.load_state_dict(torch.load(model_path))
model.eval()
# Export to ONNX for cross-platform compatibility
dummy_input = torch.randn(1, 10, 35) # (batch, seq_len, features)
torch.onnx.export(model, dummy_input, "orderbook_lstm.onnx")
# Upload to HolySheep
with open("orderbook_lstm.onnx", "rb") as f:
files = {"model": f}
response = requests.post(
f"https://api.holysheep.ai/v1/models/upload",
headers=headers,
files=files,
data={"model_name": "orderbook-lstm-v1", "model_type": "