# Neural SDK
<div align="center">
[](https://badge.fury.io/py/neural-sdk)
[](https://pypi.org/project/neural-sdk/)
[](https://opensource.org/licenses/MIT)
[](https://github.com/IntelIP/Neural)
**Professional-grade SDK for algorithmic trading on prediction markets**
[Documentation](https://neural-sdk.mintlify.app) • [Quick Start](#quick-start) • [Examples](./examples) • [Contributing](./CONTRIBUTING.md)
</div>
---
## ⚡ What is Neural?
Neural SDK is a comprehensive Python framework for building algorithmic trading strategies on prediction markets. It provides everything you need to collect data, develop strategies, backtest performance, and execute trades—all with production-grade reliability.
### 🔐 Real Data Guarantee
All market data comes from **Kalshi's live production API** via RSA-authenticated requests. This is the same infrastructure that powers a $100M+ trading platform—no simulations, no mocks, just real markets on real events.
### ⭐ Key Features
- **🔑 Authentication**: Battle-tested RSA signature implementation for Kalshi API
- **📊 Historical Data**: Collect and analyze real trade data with cursor-based pagination
- **🚀 Real-time Streaming**: REST API and FIX protocol support for live market data
- **🧠 Strategy Framework**: Pre-built strategies (mean reversion, momentum, arbitrage)
- **⚖️ Risk Management**: Kelly Criterion, position sizing, stop-loss automation
- **🔬 Backtesting Engine**: Test strategies on historical data before going live
- **⚡ Order Execution**: Ultra-low latency FIX protocol integration (5-10ms)
---
## 🚀 Quick Start
### Installation
```bash
# Basic installation
pip install neural-sdk
# With trading extras (recommended for live trading)
pip install "neural-sdk[trading]"
# Via uv (recommended)
uv pip install neural-sdk
uv pip install "neural-sdk[trading]" # with trading extras
```
### Credentials Setup
Neural SDK connects to Kalshi's live API using RSA authentication. You'll need valid Kalshi credentials:
#### Environment Variables
```bash
# Option 1: Set environment variables
export KALSHI_EMAIL="your-email@example.com"
export KALSHI_PASSWORD="your-password"
export KALSHI_API_BASE="https://trading-api.kalshi.com/trade-api/v2"
```
#### .env File (Recommended)
```bash
# Option 2: Create .env file in your project root
echo "KALSHI_EMAIL=your-email@example.com" > .env
echo "KALSHI_PASSWORD=your-password" >> .env
echo "KALSHI_API_BASE=https://trading-api.kalshi.com/trade-api/v2" >> .env
```
The SDK will automatically load credentials from your .env file using python-dotenv.
### Basic Usage
#### 1. Authentication
```python
from neural.auth.http_client import KalshiHTTPClient
# Initialize with credentials
client = KalshiHTTPClient()
# Verify connection
markets = client.get('/markets')
print(f"Connected! Found {len(markets['markets'])} markets")
```
#### 2. Collect Historical Data
```python
from datetime import datetime, timedelta
import pandas as pd
# Set time range
end_ts = int(datetime.now().timestamp())
start_ts = end_ts - (7 * 24 * 3600) # Last 7 days
# Collect trades with pagination
all_trades = []
cursor = None
while True:
response = client.get_trades(
ticker="KXNFLGAME-25SEP25SEAARI-SEA",
min_ts=start_ts,
max_ts=end_ts,
limit=1000,
cursor=cursor
)
trades = response.get("trades", [])
if not trades:
break
all_trades.extend(trades)
cursor = response.get("cursor")
if not cursor:
break
# Analyze
df = pd.DataFrame(all_trades)
print(f"Collected {len(df)} real trades from Kalshi")
```
#### 3. Build a Trading Strategy
```python
from neural.analysis.strategies import MeanReversionStrategy
from neural.analysis.backtesting import BacktestEngine
# Create strategy
strategy = MeanReversionStrategy(
lookback_period=20,
z_score_threshold=2.0
)
# Backtest
engine = BacktestEngine(strategy, initial_capital=10000)
results = engine.run(historical_data)
print(f"Total Return: {results['total_return']:.2%}")
print(f"Sharpe Ratio: {results['sharpe_ratio']:.2f}")
print(f"Max Drawdown: {results['max_drawdown']:.2%}")
```
#### 4. Live Trading
```python
from neural.trading.client import TradingClient
# Initialize trading client
trader = TradingClient()
# Place order
order = trader.place_order(
ticker="KXNFLGAME-25SEP25SEAARI-SEA",
side="yes",
count=100,
price=55
)
print(f"Order placed: {order['order_id']}")
```
---
## 📚 Documentation
### Core Modules
| Module | Description |
|--------|-------------|
| `neural.auth` | RSA authentication for Kalshi API |
| `neural.data_collection` | Historical and real-time market data |
| `neural.analysis.strategies` | Pre-built trading strategies |
| `neural.analysis.backtesting` | Strategy testing framework |
| `neural.analysis.risk` | Position sizing and risk management |
| `neural.trading` | Order execution (REST + FIX) |
### SDK Module Quickstart
#### Authentication Module
```python
from neural.auth.http_client import KalshiHTTPClient
# Initialize client with credentials from environment
client = KalshiHTTPClient()
# Test connection
response = client.get('/markets')
print(f"Connected! Found {len(response['markets'])} markets")
# Get specific market
market = client.get('/markets/NFLSUP-25-KCSF')
print(f"Market: {market['title']}")
```
#### Data Collection Module
```python
from neural.data_collection.kalshi_historical import KalshiHistoricalDataSource
from neural.data_collection.base import DataSourceConfig
import pandas as pd
# Configure historical data collection
config = DataSourceConfig(
source_type="kalshi_historical",
ticker="NFLSUP-25-KCSF",
start_time="2024-01-01",
end_time="2024-12-31"
)
# Collect historical trades
source = KalshiHistoricalDataSource(config)
trades_data = []
async def collect_trades():
async for trade in source.collect():
trades_data.append(trade)
if len(trades_data) >= 1000: # Limit for example
break
# Run collection and analyze
import asyncio
asyncio.run(collect_trades())
df = pd.DataFrame(trades_data)
print(f"Collected {len(df)} trades")
print(f"Price range: {df['price'].min():.2f} - {df['price'].max():.2f}")
```
#### Trading Module
```python
from neural.trading.client import TradingClient
# Initialize trading client
trader = TradingClient()
# Check account balance
balance = trader.get_balance()
print(f"Available balance: ${balance:.2f}")
# Place a buy order
order = trader.place_order(
ticker="NFLSUP-25-KCSF",
side="yes", # or "no"
count=10, # number of contracts
price=52 # price in cents
)
print(f"Order placed: {order['order_id']}")
# Check order status
status = trader.get_order(order['order_id'])
print(f"Order status: {status['status']}")
```
### Examples
Explore working examples in the [`examples/`](./examples) directory:
- `01_init_user.py` - Authentication setup
- `stream_prices.py` - Real-time price streaming
- `test_historical_sync.py` - Historical data collection
- `05_mean_reversion_strategy.py` - Strategy implementation
- `07_live_trading_bot.py` - Automated trading bot
### Authentication Setup
1. Get API credentials from [Kalshi](https://kalshi.com)
2. Save credentials:
```bash
# Create secrets directory
mkdir secrets
# Add your API key ID
echo "your-api-key-id" > secrets/kalshi_api_key_id.txt
# Add your private key
cp ~/Downloads/kalshi_private_key.pem secrets/
chmod 600 secrets/kalshi_private_key.pem
```
3. Set environment variables (optional):
```bash
export KALSHI_API_KEY_ID="your-api-key-id"
export KALSHI_PRIVATE_KEY_PATH="./secrets/kalshi_private_key.pem"
```
---
## 🧪 Testing
```bash
# Run all tests
pytest
# With coverage
pytest --cov=neural tests/
# Run specific test
pytest tests/test_auth.py -v
```
---
## 🤝 Contributing
We welcome contributions! Neural SDK is open source and community-driven.
### How to Contribute
1. **Fork the repository**
2. **Create a feature branch**: `git checkout -b feature/amazing-feature`
3. **Make your changes** and add tests
4. **Run tests**: `pytest`
5. **Commit**: `git commit -m "Add amazing feature"`
6. **Push**: `git push origin feature/amazing-feature`
7. **Open a Pull Request**
See [CONTRIBUTING.md](./CONTRIBUTING.md) for detailed guidelines.
### Development Setup
```bash
# Clone repository
git clone https://github.com/IntelIP/Neural.git
cd neural
# Install in editable mode with dev dependencies
pip install -e ".[dev]"
# Run tests
pytest
# Run linting
ruff check .
black --check .
```
---
## 📖 Resources
- **Documentation**: [neural-sdk.mintlify.app](https://neural-sdk.mintlify.app)
- **Examples**: [examples/](./examples)
- **API Reference**: [docs/api/](./docs/api)
- **Issues**: [GitHub Issues](https://github.com/IntelIP/Neural/issues)
- **Discussions**: [GitHub Discussions](https://github.com/IntelIP/Neural/discussions)
---
## 🗺️ Roadmap
### Version 0.1.0 (Beta) - Current
- ✅ Core authentication
- ✅ Historical data collection
- ✅ Strategy framework
- ✅ Backtesting engine
- ⚠️ REST streaming (stable)
- ⚠️ WebSocket streaming (experimental)
### Version 0.2.0 (Planned)
- 🔄 Enhanced WebSocket support
- 🔄 Real-time strategy execution
- 🔄 Portfolio optimization
- 🔄 Multi-market strategies
### Version 1.0.0 (Future)
- 🚀 Deployment stack (AWS/GCP integration)
- 🚀 Production monitoring & alerting
- 🚀 Advanced risk analytics
- 🚀 Machine learning strategies
---
## ⚖️ License
This project is licensed under the MIT License - see [LICENSE](./LICENSE) file for details.
### What This Means
✅ **You CAN**:
- Use commercially
- Modify the code
- Distribute
- Use privately
❌ **You CANNOT**:
- Hold us liable
- Use our trademarks
📋 **You MUST**:
- Include the original license
- Include copyright notice
---
## 🙏 Acknowledgments
- Built for the [Kalshi](https://kalshi.com) prediction market platform
- Inspired by the quantitative trading community
- Special thanks to all [contributors](https://github.com/IntelIP/Neural/graphs/contributors)
---
## 📞 Support
- **Documentation**: [neural-sdk.mintlify.app](https://neural-sdk.mintlify.app)
- **Issues**: [GitHub Issues](https://github.com/IntelIP/Neural/issues)
- **Discussions**: [GitHub Discussions](https://github.com/IntelIP/Neural/discussions)
- **Email**: support@neural-sdk.dev
---
<div align="center">
**Built with ❤️ by the Neural community**
[⭐ Star us on GitHub](https://github.com/IntelIP/Neural) • [📖 Read the Docs](https://neural-sdk.mintlify.app)
</div>
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"description": "# Neural SDK\n\n<div align=\"center\">\n\n[](https://badge.fury.io/py/neural-sdk)\n[](https://pypi.org/project/neural-sdk/)\n[](https://opensource.org/licenses/MIT)\n[](https://github.com/IntelIP/Neural)\n\n**Professional-grade SDK for algorithmic trading on prediction markets**\n\n[Documentation](https://neural-sdk.mintlify.app) \u2022 [Quick Start](#quick-start) \u2022 [Examples](./examples) \u2022 [Contributing](./CONTRIBUTING.md)\n\n</div>\n\n---\n\n## \u26a1 What is Neural?\n\nNeural SDK is a comprehensive Python framework for building algorithmic trading strategies on prediction markets. It provides everything you need to collect data, develop strategies, backtest performance, and execute trades\u2014all with production-grade reliability.\n\n### \ud83d\udd10 Real Data Guarantee\n\nAll market data comes from **Kalshi's live production API** via RSA-authenticated requests. This is the same infrastructure that powers a $100M+ trading platform\u2014no simulations, no mocks, just real markets on real events.\n\n### \u2b50 Key Features\n\n- **\ud83d\udd11 Authentication**: Battle-tested RSA signature implementation for Kalshi API\n- **\ud83d\udcca Historical Data**: Collect and analyze real trade data with cursor-based pagination\n- **\ud83d\ude80 Real-time Streaming**: REST API and FIX protocol support for live market data\n- **\ud83e\udde0 Strategy Framework**: Pre-built strategies (mean reversion, momentum, arbitrage)\n- **\u2696\ufe0f Risk Management**: Kelly Criterion, position sizing, stop-loss automation\n- **\ud83d\udd2c Backtesting Engine**: Test strategies on historical data before going live\n- **\u26a1 Order Execution**: Ultra-low latency FIX protocol integration (5-10ms)\n\n---\n\n## \ud83d\ude80 Quick Start\n\n### Installation\n\n```bash\n# Basic installation\npip install neural-sdk\n\n# With trading extras (recommended for live trading)\npip install \"neural-sdk[trading]\"\n\n# Via uv (recommended)\nuv pip install neural-sdk\nuv pip install \"neural-sdk[trading]\" # with trading extras\n```\n\n### Credentials Setup\n\nNeural SDK connects to Kalshi's live API using RSA authentication. You'll need valid Kalshi credentials:\n\n#### Environment Variables\n\n```bash\n# Option 1: Set environment variables\nexport KALSHI_EMAIL=\"your-email@example.com\"\nexport KALSHI_PASSWORD=\"your-password\"\nexport KALSHI_API_BASE=\"https://trading-api.kalshi.com/trade-api/v2\"\n```\n\n#### .env File (Recommended)\n\n```bash\n# Option 2: Create .env file in your project root\necho \"KALSHI_EMAIL=your-email@example.com\" > .env\necho \"KALSHI_PASSWORD=your-password\" >> .env\necho \"KALSHI_API_BASE=https://trading-api.kalshi.com/trade-api/v2\" >> .env\n```\n\nThe SDK will automatically load credentials from your .env file using python-dotenv.\n\n### Basic Usage\n\n#### 1. Authentication\n\n```python\nfrom neural.auth.http_client import KalshiHTTPClient\n\n# Initialize with credentials\nclient = KalshiHTTPClient()\n\n# Verify connection\nmarkets = client.get('/markets')\nprint(f\"Connected! Found {len(markets['markets'])} markets\")\n```\n\n#### 2. Collect Historical Data\n\n```python\nfrom datetime import datetime, timedelta\nimport pandas as pd\n\n# Set time range\nend_ts = int(datetime.now().timestamp())\nstart_ts = end_ts - (7 * 24 * 3600) # Last 7 days\n\n# Collect trades with pagination\nall_trades = []\ncursor = None\n\nwhile True:\n response = client.get_trades(\n ticker=\"KXNFLGAME-25SEP25SEAARI-SEA\",\n min_ts=start_ts,\n max_ts=end_ts,\n limit=1000,\n cursor=cursor\n )\n\n trades = response.get(\"trades\", [])\n if not trades:\n break\n\n all_trades.extend(trades)\n cursor = response.get(\"cursor\")\n if not cursor:\n break\n\n# Analyze\ndf = pd.DataFrame(all_trades)\nprint(f\"Collected {len(df)} real trades from Kalshi\")\n```\n\n#### 3. Build a Trading Strategy\n\n```python\nfrom neural.analysis.strategies import MeanReversionStrategy\nfrom neural.analysis.backtesting import BacktestEngine\n\n# Create strategy\nstrategy = MeanReversionStrategy(\n lookback_period=20,\n z_score_threshold=2.0\n)\n\n# Backtest\nengine = BacktestEngine(strategy, initial_capital=10000)\nresults = engine.run(historical_data)\n\nprint(f\"Total Return: {results['total_return']:.2%}\")\nprint(f\"Sharpe Ratio: {results['sharpe_ratio']:.2f}\")\nprint(f\"Max Drawdown: {results['max_drawdown']:.2%}\")\n```\n\n#### 4. Live Trading\n\n```python\nfrom neural.trading.client import TradingClient\n\n# Initialize trading client\ntrader = TradingClient()\n\n# Place order\norder = trader.place_order(\n ticker=\"KXNFLGAME-25SEP25SEAARI-SEA\",\n side=\"yes\",\n count=100,\n price=55\n)\n\nprint(f\"Order placed: {order['order_id']}\")\n```\n\n---\n\n## \ud83d\udcda Documentation\n\n### Core Modules\n\n| Module | Description |\n|--------|-------------|\n| `neural.auth` | RSA authentication for Kalshi API |\n| `neural.data_collection` | Historical and real-time market data |\n| `neural.analysis.strategies` | Pre-built trading strategies |\n| `neural.analysis.backtesting` | Strategy testing framework |\n| `neural.analysis.risk` | Position sizing and risk management |\n| `neural.trading` | Order execution (REST + FIX) |\n\n### SDK Module Quickstart\n\n#### Authentication Module\n\n```python\nfrom neural.auth.http_client import KalshiHTTPClient\n\n# Initialize client with credentials from environment\nclient = KalshiHTTPClient()\n\n# Test connection\nresponse = client.get('/markets')\nprint(f\"Connected! Found {len(response['markets'])} markets\")\n\n# Get specific market\nmarket = client.get('/markets/NFLSUP-25-KCSF')\nprint(f\"Market: {market['title']}\")\n```\n\n#### Data Collection Module\n\n```python\nfrom neural.data_collection.kalshi_historical import KalshiHistoricalDataSource\nfrom neural.data_collection.base import DataSourceConfig\nimport pandas as pd\n\n# Configure historical data collection\nconfig = DataSourceConfig(\n source_type=\"kalshi_historical\",\n ticker=\"NFLSUP-25-KCSF\",\n start_time=\"2024-01-01\",\n end_time=\"2024-12-31\"\n)\n\n# Collect historical trades\nsource = KalshiHistoricalDataSource(config)\ntrades_data = []\n\nasync def collect_trades():\n async for trade in source.collect():\n trades_data.append(trade)\n if len(trades_data) >= 1000: # Limit for example\n break\n\n# Run collection and analyze\nimport asyncio\nasyncio.run(collect_trades())\n\ndf = pd.DataFrame(trades_data)\nprint(f\"Collected {len(df)} trades\")\nprint(f\"Price range: {df['price'].min():.2f} - {df['price'].max():.2f}\")\n```\n\n#### Trading Module\n\n```python\nfrom neural.trading.client import TradingClient\n\n# Initialize trading client\ntrader = TradingClient()\n\n# Check account balance\nbalance = trader.get_balance()\nprint(f\"Available balance: ${balance:.2f}\")\n\n# Place a buy order\norder = trader.place_order(\n ticker=\"NFLSUP-25-KCSF\",\n side=\"yes\", # or \"no\"\n count=10, # number of contracts\n price=52 # price in cents\n)\n\nprint(f\"Order placed: {order['order_id']}\")\n\n# Check order status\nstatus = trader.get_order(order['order_id'])\nprint(f\"Order status: {status['status']}\")\n```\n\n### Examples\n\nExplore working examples in the [`examples/`](./examples) directory:\n\n- `01_init_user.py` - Authentication setup\n- `stream_prices.py` - Real-time price streaming\n- `test_historical_sync.py` - Historical data collection\n- `05_mean_reversion_strategy.py` - Strategy implementation\n- `07_live_trading_bot.py` - Automated trading bot\n\n### Authentication Setup\n\n1. Get API credentials from [Kalshi](https://kalshi.com)\n2. Save credentials:\n ```bash\n # Create secrets directory\n mkdir secrets\n\n # Add your API key ID\n echo \"your-api-key-id\" > secrets/kalshi_api_key_id.txt\n\n # Add your private key\n cp ~/Downloads/kalshi_private_key.pem secrets/\n chmod 600 secrets/kalshi_private_key.pem\n ```\n\n3. Set environment variables (optional):\n ```bash\n export KALSHI_API_KEY_ID=\"your-api-key-id\"\n export KALSHI_PRIVATE_KEY_PATH=\"./secrets/kalshi_private_key.pem\"\n ```\n\n---\n\n## \ud83e\uddea Testing\n\n```bash\n# Run all tests\npytest\n\n# With coverage\npytest --cov=neural tests/\n\n# Run specific test\npytest tests/test_auth.py -v\n```\n\n---\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! Neural SDK is open source and community-driven.\n\n### How to Contribute\n\n1. **Fork the repository**\n2. **Create a feature branch**: `git checkout -b feature/amazing-feature`\n3. **Make your changes** and add tests\n4. **Run tests**: `pytest`\n5. **Commit**: `git commit -m \"Add amazing feature\"`\n6. **Push**: `git push origin feature/amazing-feature`\n7. **Open a Pull Request**\n\nSee [CONTRIBUTING.md](./CONTRIBUTING.md) for detailed guidelines.\n\n### Development Setup\n\n```bash\n# Clone repository\ngit clone https://github.com/IntelIP/Neural.git\ncd neural\n\n# Install in editable mode with dev dependencies\npip install -e \".[dev]\"\n\n# Run tests\npytest\n\n# Run linting\nruff check .\nblack --check .\n```\n\n---\n\n## \ud83d\udcd6 Resources\n\n- **Documentation**: [neural-sdk.mintlify.app](https://neural-sdk.mintlify.app)\n- **Examples**: [examples/](./examples)\n- **API Reference**: [docs/api/](./docs/api)\n- **Issues**: [GitHub Issues](https://github.com/IntelIP/Neural/issues)\n- **Discussions**: [GitHub Discussions](https://github.com/IntelIP/Neural/discussions)\n\n---\n\n## \ud83d\uddfa\ufe0f Roadmap\n\n### Version 0.1.0 (Beta) - Current\n\n- \u2705 Core authentication\n- \u2705 Historical data collection\n- \u2705 Strategy framework\n- \u2705 Backtesting engine\n- \u26a0\ufe0f REST streaming (stable)\n- \u26a0\ufe0f WebSocket streaming (experimental)\n\n### Version 0.2.0 (Planned)\n\n- \ud83d\udd04 Enhanced WebSocket support\n- \ud83d\udd04 Real-time strategy execution\n- \ud83d\udd04 Portfolio optimization\n- \ud83d\udd04 Multi-market strategies\n\n### Version 1.0.0 (Future)\n\n- \ud83d\ude80 Deployment stack (AWS/GCP integration)\n- \ud83d\ude80 Production monitoring & alerting\n- \ud83d\ude80 Advanced risk analytics\n- \ud83d\ude80 Machine learning strategies\n\n---\n\n## \u2696\ufe0f License\n\nThis project is licensed under the MIT License - see [LICENSE](./LICENSE) file for details.\n\n### What This Means\n\n\u2705 **You CAN**:\n- Use commercially\n- Modify the code\n- Distribute\n- Use privately\n\n\u274c **You CANNOT**:\n- Hold us liable\n- Use our trademarks\n\n\ud83d\udccb **You MUST**:\n- Include the original license\n- Include copyright notice\n\n---\n\n## \ud83d\ude4f Acknowledgments\n\n- Built for the [Kalshi](https://kalshi.com) prediction market platform\n- Inspired by the quantitative trading community\n- Special thanks to all [contributors](https://github.com/IntelIP/Neural/graphs/contributors)\n\n---\n\n## \ud83d\udcde Support\n\n- **Documentation**: [neural-sdk.mintlify.app](https://neural-sdk.mintlify.app)\n- **Issues**: [GitHub Issues](https://github.com/IntelIP/Neural/issues)\n- **Discussions**: [GitHub Discussions](https://github.com/IntelIP/Neural/discussions)\n- **Email**: support@neural-sdk.dev\n\n---\n\n<div align=\"center\">\n\n**Built with \u2764\ufe0f by the Neural community**\n\n[\u2b50 Star us on GitHub](https://github.com/IntelIP/Neural) \u2022 [\ud83d\udcd6 Read the Docs](https://neural-sdk.mintlify.app)\n\n</div>\n",
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