Building an Automated Stock Analysis Pipeline with Polars and Alpaca API: Investment Strategies Based on Technical Indicators and Financial Data

This article introduces how to build an automated analysis pipeline that combines Polars' excellent data processing capabilities with Alpaca API's real-time stock data, enabling individual investors to quickly and efficiently analyze the stock market and make investment decisions. Through this pipeline, you can automate technical indicator calculations, financial data analysis, and backtesting to optimize your investment strategies.

1. The Challenge / Context

The stock market is full of vast amounts of data and constantly changing information, making it difficult for individual investors to effectively analyze and predict. Traditional methods require manually collecting and processing data from various sources, which is time-consuming and labor-intensive. Furthermore, it's challenging to respond quickly to real-time market fluctuations. This often leads many investors to make incorrect investment decisions due to a lack of information, highlighting the need for an automated analysis pipeline.

2. Deep Dive: Polars

Polars is a fast and efficient DataFrame library built on Apache Arrow. Written in Rust, it offers performance comparable to C++ and can be used in a Python environment. Polars maximizes data processing speed by leveraging multithreading and efficiently manages memory usage. It is particularly strong in handling large-scale datasets and can quickly process complex data transformations and analysis tasks. It supports lazy evaluation, performing only the necessary calculations, thereby further enhancing memory efficiency.

3. Step-by-Step Guide / Implementation

Now, let's take a detailed look at the steps to build an automated stock analysis pipeline using Polars and Alpaca API.

Step 1: Alpaca API Key Setup and Library Installation

To use the Alpaca API, you must first create an account on the Alpaca website and obtain an API key. Securely store the issued API key and secret key, and save them in environment variables or a configuration file. Install the necessary libraries (Polars, Alpaca Trade API).

# Install necessary libraries
    pip install polars alpaca-trade-api

Step 2: Fetching Stock Data via Alpaca API

Use the Alpaca API to fetch historical data for a specific stock. Set the date range and data frequency (e.g., 1 minute, 1 hour, 1 day) to collect the required data. Convert the data into a Polars DataFrame to facilitate subsequent analysis.

import alpaca_trade_api as tradeapi
    import polars as pl
    import datetime

    # Alpaca API key settings (recommended to get from environment variables)
    ALPACA_API_KEY = "YOUR_ALPACA_API_KEY"
    ALPACA_SECRET_KEY = "YOUR_ALPACA_SECRET_KEY"

    # Connect to Alpaca API
    api = tradeapi.REST(ALPACA_API_KEY, ALPACA_SECRET_KEY, 'https://paper-api.alpaca.markets') # Using a Test Account

    # Set stock ticker and date range
    symbol = "AAPL"
    start_date = datetime.datetime(2023, 1, 1)
    end_date = datetime.datetime(2023, 12, 31)

    # Fetch data via Alpaca API (1-hour bars)
    barset = api.get_barset(symbol, '1H', start=start_date.strftime('%Y-%m-%d'), end=end_date.strftime('%Y-%m-%d'))
    bars = barset[symbol]

    # Create DataFrame
    data = {
        "time": [bar.t for bar in bars],
        "open": [bar.o for bar in bars],
        "high": [bar.h for bar in bars],
        "low": [bar.l for bar in bars],
        "close": [bar.c for bar in bars],
        "volume": [bar.v for bar in bars]
    }

    df = pl.DataFrame(data)

    print(df)

Step 3: Calculating Technical Indicators

Use the Polars DataFrame to calculate technical indicators such as Moving Average, Relative Strength Index (RSI), and MACD (Moving Average Convergence Divergence). Leverage Polars' fast computation capabilities to efficiently process large amounts of data.

# Calculate Moving Average (20 hours)
    df = df.with_columns(pl.col("close").rolling_mean(window_size=20).alias("MA_20"))

    # Calculate RSI
    def rsi(series: pl.Series, period: int = 14) -> pl.Series:
        delta = series.diff().slice(1)
        up, down = delta.clone(), delta.clone()
        up = up.with_columns(pl.when(up < 0).then(0).otherwise(up))
        down = down.with_columns(pl.when(down > 0).then(0).otherwise(-down))
        avg_gain = up.rolling_mean(window_size=period)
        avg_loss = down.rolling_mean(window_size=period)
        rs = avg_gain / avg_loss
        rsi = 100 - (100 / (1 + rs))
        return rsi

    df = df.with_columns(rsi(df["close"]).alias("RSI"))

    # Calculate MACD (12-hour, 26-hour EMA, 9-hour Signal Line)
    def ema(series: pl.Series, period: int) -> pl.Series:
        return series.ewm_mean(alpha=2 / (period + 1))

    ema_12 = ema(df["close"], 12)
    ema_26 = ema(df["close"], 26)
    macd = ema_12 - ema_26
    signal = ema(macd, 9)

    df = df.with_columns(macd.alias("MACD"), signal.alias("Signal"))

    print(df)

Step 4: Integrating and Analyzing Financial Data

Fetch financial data using other data sources (e.g., Yahoo Finance API, Financial Modeling Prep API) in addition to the Alpaca API. Analyze financial indicators such as revenue, debt-to-equity ratio, and cash flow to assess a company's financial health. Integrate technical indicators and financial data using Polars' data manipulation capabilities. (This example does not cover connecting to APIs other than Alpaca API. To be updated later)

Step 5: Implementing Investment Strategies and Backtesting

Implement investment strategies based on technical indicators and financial data. For example, you can implement a strategy to buy when RSI falls below a certain threshold, or when MACD crosses above its signal line. Evaluate and optimize the strategy's performance using historical data through backtesting. More sophisticated backtesting can be performed using backtesting libraries (e.g., Backtrader, Zipline). (This example only provides basic strategy examples and does not cover the use of backtesting libraries. To be updated later)

# Generate simple buy/sell signals (Buy if RSI < 30, Sell if RSI > 70)
    df = df.with_columns(
        pl.when(pl.col("RSI") < 30)
        .then(1)  # Buy
        .when(pl.col("RSI") > 70)
        .then(-1) # Sell
        .otherwise(0) # Hold
        .alias("Signal")
    )

    # Calculate simple return (change in current bar's close price vs. previous bar's close price)
    df = df.with_columns((df["close"].shift(-1) - df["close"]) / df["close"] * 100)
    df = df.rename({"close": "current_price"})

    print(df)

Step 6: Building an Automated Pipeline

Automate the steps described above to build a pipeline that periodically (e.g., daily, hourly) collects and analyzes data. Use schedulers (e.g., Celery, APScheduler) to automatically run the pipeline. Visualize the results and build dashboards to support investment decisions.

4. Real-world Use Case / Example

I use this pipeline every morning to screen stocks for investment. Previously, I had to manually collect and analyze data by visiting various websites, but now, with the automated pipeline, I can get all the information within 10 minutes. This has saved me time and allowed me to make better investment decisions. In particular, Polars' fast data processing speed enabled efficient analysis of large amounts of data.

5. Pros & Cons / Critical Analysis

Pros:
- Fast data processing speed (Polars)
- Real-time stock data access (Alpaca API)
- Time savings through automated analysis pipeline
- Objective data-driven investment decisions
Cons:
- API usage costs incurred (Alpaca API)
- Limitations of historical data-based analysis (uncertainty of future predictions)
- Initial setup and maintenance effort required
- Reliability of analysis results depends on data quality

6. FAQ

Q: Can I use Pandas instead of Polars?
A: Pandas is also an excellent data analysis library, but Polars offers faster speed and lower memory usage. Polars is a better choice, especially when dealing with large datasets.
Q: Can I use other APIs besides Alpaca API?
A: Yes, various APIs such as Yahoo Finance API and Financial Modeling Prep API can be used. However, you should check the usage policies and data quality of each API.
Q: How should I perform backtesting?
A: You can evaluate the performance of your investment strategy based on historical data using backtesting libraries like Backtrader and Zipline. These libraries provide more realistic backtesting results by considering transaction fees, slippage, and other factors.

7. Conclusion

The automated stock analysis pipeline utilizing Polars and Alpaca API is a powerful tool that helps individual investors bridge the information gap and make better investment decisions. Follow the steps presented in this article to build your own pipeline and optimize your investment strategies. Get your Alpaca API key now and run the Polars code to experience the world of automated stock analysis!

Building an Automated Stock Analysis Pipeline using Polars and Alpaca API: Technical Indicators and Financial Data-Driven Investment Strategies