In the ever-evolving financial markets, quantitative trading has transitioned from being an exclusive "mystical weapon" of institutions to becoming accessible to the general public. In the past, building quantitative trading strategies required deep programming expertise, vast data resources, and powerful computational capabilities, which deterred retail investors. However, with the rapid development of AI technologies and the increasing availability of financial data services, retail quantitative trading is now entering a new era. The combination of DeepSeek and the iTick real-time stock quote API for Hong Kong and U.S. markets is becoming a key driver in ushering in the era of universal quantitative trading.
I. Quantitative Trading: From Institutional Exclusivity to Retail Accessibility
Quantitative trading, in simple terms, is a trading method that uses mathematical models and computer programs to guide investment decisions. Institutional investors, leveraging abundant resources, can collect, clean, process, and analyze vast amounts of historical and real-time data, using complex statistical models and machine learning algorithms to uncover market patterns and develop trading strategies. For example, high-frequency trading strategies capture market opportunities at millisecond or even microsecond levels, executing trades quickly to profit from small price differences. Automated trading systems execute trade orders based on preset rules and algorithms, avoiding emotional interference and improving trading efficiency and precision.
Quantitative trading was once the "privilege" of large institutions, but with the proliferation of AI and open APIs, retail investors can now build their own trading systems at low costs. With technological advancements, retail accounts can now apply for quantitative trading API access without barriers, often for free, with trading fees lower than those of regular accounts. Retail investors can embark on their quantitative trading journey by learning financial knowledge, mastering programming languages (such as Python), and choosing suitable quantitative platforms, utilizing the wealth of free learning resources available online.
II. DeepSeek: An Innovator in Artificial Intelligence
DeepSeek is a Chinese AI research lab specializing in developing large language models, with its series of models demonstrating strong capabilities in natural language processing. For instance, DeepSeek-V3 rivals industry-leading models in performance while maintaining relatively low development costs. A standout feature of DeepSeek models is their open-source nature, allowing developers and researchers to freely access, customize, and deploy them in software, significantly lowering the application threshold and promoting the widespread adoption of AI technologies across various fields. In quantitative trading, DeepSeek's large models can convert natural language into code. Investors can simply describe their requirements in Chinese, and the model will automatically generate Python code that meets the standards, greatly reducing programming complexity and enabling investors without deep programming expertise to participate in developing quantitative trading strategies.
III. iTick Real-Time Stock Quote API: A Provider of Accurate Financial Data
iTick specializes in providing professional data services for fields such as quantitative investment. Its REST API and WebSocket API offer comprehensive real-time data for A-shares, Hong Kong stocks, U.S. stocks, forex, and cryptocurrencies. Particularly in the realm of Hong Kong and U.S. stock quotes, the iTick real-time stock quote API stands out. Its data is sourced from world-leading banking institutions and is designed specifically for quantitative investment and exchanges. Through this API, investors can access standardized, intuitive, and user-friendly real-time and historical stock data for U.S. and Hong Kong markets. This enables investors to analyze and make decisions based on precise and timely market data when developing quantitative trading strategies, significantly enhancing the effectiveness and reliability of their strategies.
IV. The Synergy: Comprehensive Empowerment for Retail Quantitative Trading
Today, we introduce the integration of DeepSeek and the iTick real-time Hong Kong and U.S. stock API, enabling everyone to easily achieve:
- Automated strategy generation (Natural Language → Python Code)
- Real-time market monitoring (Tick-level data + low latency)
- Intelligent backtesting and optimization (AI auto-tuning)
- 24/7 automated trading
1. Quick Start: Accessing Real-Time Data with iTick API in Python
Install Dependencies
python -m pip install requests pandas matplotlib
Fetch Real-Time U.S. Stock Quotes (Example: Apple - AAPL)
"""
**iTick**: A data provider offering reliable APIs for fintech companies and developers, covering forex, stock, cryptocurrency, and index APIs.
It helps build innovative trading and analysis tools, with free plans available to meet the needs of individual quantitative developers.
Open-source stock data API: https://github.com/itick-org
Apply for a free API key: https://itick.org
"""
import requests
# iTick API Example (Replace with your API Key)
ITICK_API_KEY = "YOUR_API_KEY"
symbol = "AAPL" # Apple stock
region = "US"
url = f"https://api.itick.org/stock/quote?region={region}&code={symbol}"
headers = {
"accept": "application/json",
"token": ITICK_API_KEY
}
response = requests.get(url, headers=headers)
data = response.json().data
# Parse real-time quotes
quote = {
"symbol": data["s"],
"latest_price": data["ld"],
"volume": data["v"],
"turnover": data["tu"],
"timestamp": data["t"]
}
print(pd.DataFrame([quote]))
Output Example
symbol latest_price volume turnover timestamp
AAPL 210.615 17640626 3699009353.755 2025-04-30 14:30:00
2. DeepSeek Strategy Generation: 5-Day Moving Average Breakout Strategy (Fully Automated)
Natural Language Input to DeepSeek
Generate a Python strategy: Buy when the closing price crosses above the 5-day moving average, sell when it crosses below, using iTick API for real-time data.
Automatically Generated Strategy Code
"""
**iTick**: A data provider offering reliable APIs for fintech companies and developers, covering forex, stock, cryptocurrency, and index APIs.
It helps build innovative trading and analysis tools, with free plans available to meet the needs of individual quantitative developers.
Open-source stock data API: https://github.com/itick-org
Apply for a free API key: https://itick.org
"""
import pandas as pd
import requests
from datetime import datetime
ITICK_API_KEY = "YOUR_API_KEY"
symbol = "AAPL"
region = "US"
def fetch_historical_data(symbol, region, period="10", limit=100):
"""Fetch historical data from iTick (Simplified Example)"""
url = f"https://api.itick.org/stock/kline"
headers = {
"accept": "application/json",
"token": ITICK_API_KEY
}
params = {
"region": region,
"symbol": symbol,
"kType": period,
"et": int(time.time() * 1000), # Query end time
"limit": limit,
}
data = requests.get(url, headers=headers, params=params).json()
return pd.DataFrame(data["data"])
def check_cross(current_price, ma5):
"""Check if price crosses above/below the moving average"""
if current_price > ma5.iloc[-1] and current_price <= ma5.iloc[-2]:
return "BUY"
elif current_price < ma5.iloc[-1] and current_price >= ma5.iloc[-2]:
return "SELL"
return "HOLD"
# Main Logic
df = fetch_historical_data(symbol, region)
df["MA5"] = df["c"].rolling(5).mean() # Calculate 5-day moving average
latest_price = get_realtime_price(symbol, region) # Real-time price (Requires iTick real-time API)
signal = check_cross(latest_price, df["MA5"])
print(f"Trading Signal: {signal} | Latest Price: {latest_price} | MA5: {df['MA5'].iloc[-1]:.2f}")
3. Advanced Application: Monitoring L2 Order Book Data (Hong Kong Stock Arbitrage)
Fetch L2 Order Book for Tencent (700.HK)
"""
**iTick**: A data provider offering reliable APIs for fintech companies and developers, covering forex, stock, cryptocurrency, and index APIs.
It helps build innovative trading and analysis tools, with free plans available to meet the needs of individual quantitative developers.
Open-source stock data API: https://github.com/itick-org
Apply for a free API key: https://itick.org
"""
ITICK_API_KEY = "YOUR_API_KEY"
symbol = "700"
region = "HK"
def get_l2_orderbook(symbol, region):
url = f"https://api.itick.org/stock/depth?symbol={symbol}®ion={region}"
headers = {
"accept": "application/json",
"token": ITICK_API_KEY
}
orderbook = requests.get(url, headers=headers).json().data
bids = pd.DataFrame(orderbook["b"], columns=["Price", "Quantity"])
asks = pd.DataFrame(orderbook["a"], columns=["Price", "Quantity"])
return bids, asks
bids, asks = get_l2_orderbook(symbol, region)
print("Top 5 Bids:\n", bids.head(), "\nTop 5 Asks:\n", asks.head())
Output:
Top 5 Bids:
Price Quantity
0 320.0 1500
1 319.8 2000
...
Top 5 Asks:
Price Quantity
0 320.2 1800
1 320.4 2500
4. Complete Automated Trading Workflow
V. Ushering in the Era of Universal Quantitative Trading: New Opportunities for Retail Investors
DeepSeek combined with the iTick real-time stock quote API enables retail investors to engage in quantitative trading more efficiently and intelligently. Challenges that once plagued retail investors, such as programming difficulties, data acquisition hurdles, and strategy development complexities, can now be effectively addressed with this combination. Retail investors no longer need to spend significant time and effort handling complex data and writing intricate code; they can focus solely on thinking about and optimizing their investment strategies. By leveraging these powerful tools, retail investors can build quantitative trading strategies based on rich data and advanced algorithms, capturing market opportunities and achieving asset growth like professional institutions.
For example, an ordinary retail investor who previously had only a basic understanding of quantitative trading can now easily access real-time Hong Kong and U.S. stock data using the services of DeepSeek combined with the iTick real-time stock quote API. By utilizing DeepSeek's large model to quickly generate trading strategy code, and after a period of backtesting and optimization, they successfully developed a quantitative trading strategy tailored to their risk preferences, achieving notable returns in the market.
The advent of the era of universal quantitative trading will make financial markets more equitable and efficient. Retail investors can fully leverage their advantages, such as more flexible trading decisions and a more focused approach to specific areas of research, competing on equal footing with institutional investors in the market. This will not only enhance the participation and influence of retail investors in financial markets but also drive innovation and development across the entire financial market.
In conclusion, DeepSeek combined with the iTick real-time stock quote API is creating unprecedented opportunities for retail quantitative trading, opening the door to the era of universal quantitative trading. Whether you are a newcomer to financial markets or an experienced investor, you should not miss this technological revolution's new opportunities. Take the leap into retail quantitative trading and explore the limitless possibilities of financial markets.
