Quantitative Trading Strategy Development and Backtesting Based on Yahu API

In today’s investment research landscape, quantitative strategies have become increasingly mainstream, rooted in the principle of "letting the data speak." With the continuous evolution of data platforms and tools, building a fully functional quantitative trading system is no longer exclusive to financial institutions. All you need is a simple Python environment and a powerful data provider like Luckdata’s Yahu Financials API to efficiently retrieve data, construct trading strategies, perform backtesting, and visualize results — completing a full cycle from concept to verification.

This article provides a complete hands-on example that walks you through building an API-based quantitative strategy backtesting system, covering the full loop of "Data Acquisition → Strategy Implementation → Backtest Analysis → Visual Evaluation," helping you master the full data-to-insight pipeline.

1. Why Use API + Python for Quantitative Backtesting?

• Real-time data access via API for automated connection from market data to fundamentals: APIs enable on-demand access to the latest market and financial data without tedious manual updates.

• Python is the most popular language for quant analysis: With robust data analysis libraries like pandas, numpy, matplotlib, and scikit-learn, and a vast open-source quant community, you can quickly prototype and test ideas.

• Independence from broker platforms for greater flexibility and portability: You’re free from platform constraints, making your strategies easier to deploy across environments or in the cloud.

• Luckdata’s Yahu API offers structured endpoints for multi-dimensional data: These include market data, fundamentals, news sentiment, and valuation metrics — supporting comprehensive inputs for strategy design.

2. Data Preparation: Fetching Historical Market Data from the API

Let’s use Apple Inc. (AAPL) as an example to retrieve daily historical price data from 2020 to 2024 using the Yahu API:

import requests
import pandas as pd
import datetime
def fetch_price_history(symbol="AAPL", start="2020-01-01", end="2024-12-31"):
endpoint = "https://luckdata.io/yahu-financials/azr83087xcq0"
params = {
"symbol": symbol,
"region": "US",
"type": "history",
"period1": start,
"period2": end
}
headers = {"x-api-key": "YOUR_API_KEY"}
res = requests.get(endpoint, params=params, headers=headers)
data = res.json()
prices = pd.DataFrame(data["prices"])
prices["date"] = pd.to_datetime(prices["date"], unit="s")
return prices[["date", "open", "high", "low", "close", "volume"]]

The output will be a structured dataset of historical daily prices:

This dataset serves as the foundation for building and testing the strategy.

3. Strategy Implementation: Moving Average Crossover Strategy

We’ll implement a classic technical trading model: Buy when the 5-day moving average crosses above the 20-day moving average, and sell when it crosses below.

def compute_ma_cross(prices):
prices["ma_short"] = prices["close"].rolling(5).mean()
prices["ma_long"] = prices["close"].rolling(20).mean()
prices["signal"] = 0
prices.loc[prices["ma_short"] > prices["ma_long"], "signal"] = 1
prices.loc[prices["ma_short"] < prices["ma_long"], "signal"] = -1
return prices

This strategy captures trend shifts by comparing short-term and long-term price momentum — a widely used method in swing and trend-following strategies.

4. Strategy Backtesting: Simulating Capital Growth

Next, we simulate the performance of the strategy starting with an initial capital of $100,000 and evaluate its growth based on the generated signals.

def backtest_strategy(prices, initial_capital=100000):
prices = prices.copy()
prices["position"] = prices["signal"].shift(1)
prices["daily_return"] = prices["close"].pct_change()
prices["strategy_return"] = prices["daily_return"] * prices["position"]
prices["capital"] = (1 + prices["strategy_return"]).cumprod() * initial_capital
return prices

This function calculates daily returns and applies the position signal to model capital growth over time, producing data ready for visualization.

5. Visualizing Backtest Results

Using matplotlib, we can compare the performance of our strategy to a basic buy-and-hold benchmark:

import matplotlib.pyplot as plt
def plot_performance(prices):
plt.figure(figsize=(12,6))
plt.plot(prices["date"], prices["capital"], label="Strategy Capital")
plt.plot(prices["date"], (1 + prices["daily_return"]).cumprod() * 100000, label="Buy & Hold")
plt.legend()
plt.title("Strategy vs. Buy & Hold Capital Curve")
plt.xlabel("Date")
plt.ylabel("Capital ($)")
plt.grid(True)
plt.show()

The resulting chart will show two lines — one for your strategy’s performance, and one for holding AAPL throughout the period. It provides clear visual feedback on whether the strategy added value.

6. Optional Enhancements and Automation

To expand this system into a robust research or trading tool, consider the following upgrades:

• Multi-asset backtesting with performance comparison: Run the strategy across multiple tickers and rank based on risk-adjusted returns.

• Use professional backtesting frameworks: Leverage tools like Backtrader or vectorbt for advanced features like transaction costs, slippage modeling, and capital allocation.

• Automated scheduling with APScheduler or cron: Set up a job to fetch data and rerun the backtest daily or weekly, enabling automated strategy reports.

• Add fundamental screening using Yahu API’s screeners: Use APIs for financial data to filter high-quality stocks as inputs.

• Integrate sentiment/news data: Use Yahu’s news and sentiment modules to filter signals based on market sentiment, potentially improving strategy robustness.

7. Conclusion

In this article, we walked through the full cycle of building a quantitative strategy using Luckdata’s Yahu Financials API — from fetching structured market data to constructing a trading model, running a backtest, and visualizing results. The API’s rich and structured data endpoints eliminate the burden of manual data handling and empower flexible, scalable strategy development in Python. Whether you're a beginner or an experienced quant developer, this framework provides a powerful launchpad for exploring and refining your trading ideas.

? Interested in trying it yourself? Visit the Luckdata website to register and get a free API key for Yahu Financials — and begin your own strategy research journey.
[Luckdata Official Site: https://luckdata.io]

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