How to Develop Quantitative Strategies with iTick Forex Data API and Cursor AI

In the field of forex trading, utilizing forex data API to obtain real-time market data and combining it with quantitative strategies to achieve automated trading has become a trend. This article will introduce how to quickly achieve automated writing and deployment of quantitative strategies through the iTick free forex quote API and Cursor AI code tool, covering the entire process of forex data API calls, strategy logic generation, code auto-generation, and backtesting.

I. Technology Stack Setup

iTick Forex Data API Integration iTick provides a free forex data API that supports obtaining real-time forex quotes, historical K-line data, and depth market data. Developers can integrate it through the following steps:

"""
**iTick**: A data agency that provides reliable data source APIs for fintech companies and developers, covering forex API, stock API, cryptocurrency API, index API, etc., helping to build innovative trading and analysis tools. Currently, there is a free package available that can basically meet the needs of individual quantitative developers.
https://github.com/itick-org
https://itick.org
"""

import requests
import json

# Get API key
API_KEY = "your_api_key"
headers = {"Authorization": f"Bearer {API_KEY}"}

# Request EURUSD 1-hour K-line data
url = "https://api.itick.org/v1/market/kline"
params = {
     "symbol": "EURUSD",
     "interval": "1h",
     "limit": 100
}
response = requests.get(url, headers=headers, params=params)
data = json.loads(response.text)

Cursor AI Code Tool Configuration Cursor AI is a natural language-based code generation tool that supports generating Python strategy code directly through Chinese descriptions. After installation, API access permissions need to be configured:
```
from cursor import Cursor

cursor = Cursor(api_key="your_cursor_key")
```

II. Strategy Development Process

Strategy Logic Definition Use Cursor AI's natural language processing function to input the strategy description:

strategy_description = """
Buy when the 5-day moving average of EURUSD crosses above the 20-day moving average,
Sell when the 5-day moving average crosses below the 20-day moving average,
Use 2% of the position for each trade,
Set stop loss at 100 points
"""

Code Auto-Generation Generate complete strategy code through Cursor AI:

generated_code = cursor.generate_code(
     prompt=strategy_description,
     lang="python",
     context="quantitative_strategy"
)

Strategy Code Optimization The generated code includes a complete strategy framework, and developers can further optimize the parameters:

class MovingAverageStrategy:
     def __init__(self, api_client, risk_ratio=0.02, stop_loss=100):
          self.api = api_client
          self.risk_ratio = risk_ratio
          self.stop_loss = stop_loss
          self.short_ma = 5
          self.long_ma = 20

     def calculate_signals(self, data):
          close_prices = [d['close'] for d in data]
          short_ma = pd.Series(close_prices).rolling(self.short_ma).mean()
          long_ma = pd.Series(close_prices).rolling(self.long_ma).mean()
          return pd.DataFrame({'short_ma': short_ma, 'long_ma': long_ma})

III. Strategy Backtesting and Deployment

Historical Data Backtesting Use the historical data interface provided by iTick to verify the strategy:

def backtest_strategy(start_date, end_date):
     historical_data = itick.get_historical_data(
          symbol="EURUSD",
          interval="1h",
          start=start_date,
          end=end_date
     )
     strategy = MovingAverageStrategy(itick)
     signals = strategy.generate_signals(historical_data)
     # Calculate return rate, Sharpe ratio, and other indicators

Live Trading Deployment Integrate iTick's real-time market data push interface to achieve automated trading:

def on_tick(tick_data):
     strategy = MovingAverageStrategy(itick)
     signals = strategy.analyze(tick_data)
     if signals['buy_signal']:
          itick.execute_order(
                symbol="EURUSD",
                quantity=calculate_position_size(),
                side="buy"
          )

IV. Key Technical Points

Data Timeliness Assurance iTick provides millisecond-level data updates and supports WebSocket real-time push:

import websocket

def on_message(ws, message):
     tick_data = json.loads(message)
     on_tick(tick_data)

ws = websocket.WebSocketApp("wss://api.itick.org/v1/ws")
ws.on_message = on_message
ws.run_forever()

Risk Control Mechanism The strategy includes a built-in dynamic position management and stop-loss system:

def calculate_position_size(self):
     account_balance = self.api.get_account_balance()
     return (account_balance * self.risk_ratio) / self.stop_loss

By combining the iTick Forex Data API and Cursor AI code tool, developers can shorten the strategy development cycle from days to hours. It is recommended to regularly update strategy parameters and intervene manually based on the economic event calendar. For high-frequency trading scenarios, further optimize data processing logic and use iTick's Level 2 depth data interface to obtain more detailed market liquidity information.