Python Tools and Libraries Every Financial Analyst Should Know#

Installing Python#

The most commonly used distributions are the official Python installers from python.org or the Anaconda Distribution from Anaconda.com. Anaconda is often recommended for data science since it comes preinstalled with many scientific libraries like NumPy, Pandas, and Matplotlib.

Using Virtual Environments#

Virtual environments help isolate project dependencies so different projects dont clash. This is particularly important if you work on multiple financial projects requiring different library versions.

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# Create a new environment
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conda create -n finance_env python=3.9
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# Activate the environment
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conda activate finance_env
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# Install libraries within this environment
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pip install numpy pandas matplotlib

Or using venv:

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python3 -m venv finance_env
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source finance_env/bin/activate  # On Linux/Mac
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finance_env\Scripts\activate     # On Windows
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pip install numpy pandas matplotlib

Working with Jupyter Notebooks#

Jupyter Notebooks offer an interactive computing environment valuable for iterative analysis, where you combine code, results, and annotations in one place. This is particularly useful for exploratory data analysis (EDA) in finance.

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# Install Jupyter notebook in your environment
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pip install jupyter
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# Launch Jupyter
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jupyter notebook

A local webpage will open in your default browser, allowing you to create and render .ipynb files.

Choosing an IDE or Editor#

Popular choices include:

Depending on your preference, each tool can integrate with virtual environments and run Python code efficiently.

NumPy#

NumPy (Numerical Python) is the foundational library for numerical computations. It provides support for large, multi-dimensional arrays and matrices. Many other libraries, like Pandas and Scikit-Learn, depend on NumPy for efficient numerical operations.

Key Advantages:

• Fast array operations.
• Strong linear algebra capabilities.
• Vectorization for performance gains.

Example usage:

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import numpy as np
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# Create a NumPy array
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arr = np.array([10.2, 15.4, 20.1, 28.5])
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# Basic statistics
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mean_val = np.mean(arr)
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std_val = np.std(arr)
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print("Mean:", mean_val)
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print("STD:", std_val)

Pandas#

Pandas offers data structures and tools for data manipulation and analysis, particularly for time series. The DataFrame is its core object, similar to a spreadsheet or SQL table. Pandas excels at reading or writing to various file formats (CSV, Excel, SQL databases) and handling time-indexed data.

Typical Use Cases:

• Reading financial data from CSV or Excel.
• Merging, grouping, and aggregating data.
• Handling missing values.
• Time-series resampling and rolling window calculations.

Usage example:

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import pandas as pd
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# Read CSV data
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df = pd.read_csv("financial_data.csv")
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# Inspect the structure
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print(df.head())
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# Calculate daily returns
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df["Daily Return"] = df["Close"].pct_change()
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# Drop rows with NaN values
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df.dropna(inplace=True)

Matplotlib and Seaborn#

• Matplotlib: The fundamental plotting library that allows you to create static, animated, and interactive visualizations in Python.
• Seaborn: Built on top of Matplotlib, it provides a high-level interface for drawing attractive statistical graphics.

A typical finance use case involves plotting stock prices, moving averages, or correlation matrices.

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import matplotlib.pyplot as plt
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import seaborn as sns
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# Scatter plot with Seaborn
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sns.scatterplot(data=df, x="Volume", y="Daily Return")
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plt.title("Volume vs. Daily Return")
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plt.show()

yfinance#

yfinance is an API to fetch stock market data directly from Yahoo Finance. Its ease of use makes it a go-to tool for quick, real-time analyses.

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import yfinance as yf
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# Fetch data for Apple
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apple_data = yf.download("AAPL", start="2020-01-01", end="2021-01-01")
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print(apple_data.head())

Statsmodels#

Statsmodels focuses on statistical modeling, including:

• Time Series Analysis (AR, ARIMA, ARMA, SARIMAX).
• Linear and Logistic Regression.
• Hypothesis Testing.

Its particularly valuable for econometric analyses and advanced financial models.

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import statsmodels.api as sm
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X = df[["Volume"]]
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y = df["Close"]
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# Add a constant term for intercept
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X = sm.add_constant(X)
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model = sm.OLS(y, X).fit()
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print(model.summary())

Scikit-Learn#

Scikit-Learn is a foundational machine learning library in Python:

• Classification & Regression: Logistic Regression, Random Forest, etc.
• Clustering: KMeans, DBSCAN.
• Feature Engineering: Feature selection, dimensionality reduction (PCA).
• Model Evaluation: Cross-validation, metrics, etc.

Applications in finance often revolve around predicting stock trends, classifying market regimes, or clustering assets.

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from sklearn.ensemble import RandomForestRegressor
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from sklearn.model_selection import train_test_split
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X = df[["Open", "High", "Low", "Volume"]]
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y = df["Close"]
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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rf = RandomForestRegressor(n_estimators=100)
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rf.fit(X_train, y_train)
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score = rf.score(X_test, y_test)
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print("R-squared:", score)

SciPy#

SciPy builds upon NumPy and includes modules for optimization, integration, and statistical distributions. In finance, you might use SciPy for optimization problems like maximizing a portfolios Sharpe ratio.

Reading Financial Datasets#

Financial data can come in many forms: CSV, Excel, SQL databases, or APIs.

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# CSV
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df = pd.read_csv("historical_prices.csv")
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# Excel
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df_excel = pd.read_excel("financial_statement.xlsx", sheet_name="BalanceSheet")
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# SQL example
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import sqlite3
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conn = sqlite3.connect("finance_data.db")
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df_sql = pd.read_sql("SELECT * FROM transactions", conn)
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conn.close()

Time Series Handling#

Working with dates and times is common in finance. Pandas provides comprehensive time series functions:

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df["Date"] = pd.to_datetime(df["Date"])
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df.set_index("Date", inplace=True)
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# Resample to monthly frequency
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monthly_data = df.resample("M").last()
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# Calculate rolling mean
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monthly_data["RollingMean"] = monthly_data["Close"].rolling(window=3).mean()

Common Data Cleaning Techniques#

1. Handling Missing Values:

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   # Drop rows with any missing values
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   df.dropna(inplace=True)
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   # Fill missing values with the mean
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   df["Close"].fillna(df["Close"].mean(), inplace=True)
   

2. Removing Duplicates:

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   df.drop_duplicates(subset=["Date"], keep="first", inplace=True)
   

3. Filtering Outliers:

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   # Cap outliers at 1st and 99th percentile
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   lower_bound = df["Volume"].quantile(0.01)
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   upper_bound = df["Volume"].quantile(0.99)
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   df = df[(df["Volume"] >= lower_bound) & (df["Volume"] <= upper_bound)]
   

4. Feature Engineering (creating new columns like moving averages, cumulative returns, or volatility).

Plotly for Interactive Charts#

Plotly generates interactive plots you can hover over and zoom in on, making it particularly useful for exploring financial time series.

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import plotly.express as px
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fig = px.line(df, x=df.index, y="Close", title="Stock Closing Price Over Time")
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fig.show()

Dash and Streamlit for Dashboard Building#

• Dash: A framework by Plotly. You use Python for backend and define interactive web app components.
• Streamlit: Very quick prototyping for dashboards. You write script as if youre printing logs, and it automatically updates a web interface.

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import dash
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from dash import dcc, html
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import plotly.express as px
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app = dash.Dash(__name__)
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fig = px.line(df, x=df.index, y="Close")
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app.layout = html.Div([
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    html.H1("Stock Price Dashboard"),
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    dcc.Graph(figure=fig)
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])
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if __name__ == "__main__":
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    app.run_server(debug=True)

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import streamlit as st
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import plotly.express as px
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st.title("Stock Price Dashboard")
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fig = px.line(df, x=df.index, y="Close")
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st.plotly_chart(fig)

Portfolio Optimization with PyPortfolioOpt#

PyPortfolioOpt is a library that assists in optimizing portfolios (e.g., maximizing the Sharpe ratio). It reduces the complexities of optimization routines using a clean API.

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import pandas as pd
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import numpy as np
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import yfinance as yf
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from pypfopt.efficient_frontier import EfficientFrontier
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tickers = ["AAPL", "GOOGL", "MSFT", "TSLA"]
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data = yf.download(tickers, start="2020-01-01", end="2021-01-01")["Adj Close"]
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returns = data.pct_change().dropna()
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ef = EfficientFrontier(returns.mean(), returns.cov())
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weights = ef.max_sharpe()
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cleaned_weights = ef.clean_weights()
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print(cleaned_weights)

Algorithmic Trading Basics#

Financial analysts interested in algorithmic trading can leverage Python for:

• Strategy development.
• Backtesting.
• Deployment to live markets.

Popular frameworks:

• backtrader
• Zipline
• QuantConnect (cloud-based platform)

Example pseudo-code of a simple moving average crossover strategy:

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# Pseudo-code
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fast_ma = df["Close"].rolling(window=50).mean()
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slow_ma = df["Close"].rolling(window=200).mean()
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signals = (fast_ma > slow_ma).astype(int)
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df["Signal"] = signals.shift(1)  # Trade signal on next day

Feature Engineering for Financial Data#

1. Lag Features: Using past values of prices or returns.
2. Technical Indicators: RSI, MACD, Bollinger Bands (using TA-Lib).
3. Event-Based Features: Earnings announcements, Federal Reserve statements.

Good features can drastically improve a models forecasting or classification accuracy.

Dask#

Dask extends the capabilities of Pandas to handle larger-than-memory datasets and parallel computing.

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import dask.dataframe as dd
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# Read a large CSV file
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df_large = dd.read_csv("very_large_file.csv")
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# Perform parallel operations
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df_grouped = df_large.groupby("ticker")["Close"].mean().compute()

Pandas vs Dask vs Spark#

If your daily tasks involve small datasets, Pandas suffices. For bigger volumes, consider Dask or Spark.

TA-Lib#

TA-Lib offers built-in functions for over 200 technical indicators (e.g., RSI, SMA, EMA, Bollinger Bands). This library is extremely handy if youre heavily into technical analysis.

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import talib
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close_prices = df["Close"].values
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rsi = talib.RSI(close_prices, timeperiod=14)
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df["RSI"] = rsi

QuantLib#

QuantLib is a C++ library with Python bindings specializing in fixed income, derivatives pricing, and more. Its often used at advanced levels of quantitative finance.

Typical tasks include:

• Yield curve construction.
• Option pricing.
• Interest rate models.

pyfolio or quantstats#

• pyfolio: Allows you to analyze and visualize the performance of trading strategies.
• quantstats: Similar to pyfolio, offers performance metrics and tear sheets.

Analyzing Stock Prices#

Below is a step-by-step approach to quickly analyze and visualize a single stocks performance:

1. Retrieve Data using yfinance.
2. Generate rolling averages to observe trends.
3. Plot the resultant data.

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import yfinance as yf
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import matplotlib.pyplot as plt
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# Fetch stock data
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df = yf.download("AAPL", start="2021-01-01", end="2022-01-01")
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# Compute moving averages
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df["MA50"] = df["Close"].rolling(50).mean()
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df["MA200"] = df["Close"].rolling(200).mean()
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# Plot
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plt.figure(figsize=(12,6))
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plt.plot(df["Close"], label="AAPL Close")
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plt.plot(df["MA50"], label="MA50")
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plt.plot(df["MA200"], label="MA200")
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plt.legend()
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plt.show()

Building a Simple Trading Strategy#

A rudimentary momentum strategy could be: Buy if RSI < 30, Sell if RSI > 70?

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import yfinance as yf
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import talib
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import numpy as np
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df = yf.download("AAPL", start="2021-01-01", end="2022-01-01")
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df.dropna(inplace=True)
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df["RSI"] = talib.RSI(df["Close"], timeperiod=14)
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df["Position"] = np.where(df["RSI"] < 30, 1, 0)
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df["Position"] = np.where(df["RSI"] > 70, -1, df["Position"])
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df["Strategy_Return"] = df["Position"].shift(1) * df["Close"].pct_change()
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cumulative_return = (1 + df["Strategy_Return"].dropna()).cumprod() - 1
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print("Cumulative return of strategy:", cumulative_return.iloc[-1])

This code snippet demonstrates how easy it is to incorporate technical indicators into Python-based strategies.

Version Control#

Use Git (and platforms like GitHub or GitLab) to track code changes, collaborate with peers, and maintain reproducibility.

1. Initialize a Git repository in your project folder.
2. Commit regularly.
3. Use branches for new features or experimental strategies.

Continuous Integration & Deployment#

Set up CI pipelines (via GitHub Actions, Jenkins, or Travis CI) to automate testing. Automated testing ensures that each new commit doesnt break existing functionality. For deployment, you might containerize your applications with Docker.

Example GitHub Actions Workflow:

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name: CI
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on: [push, pull_request]
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jobs:
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  build:
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    runs-on: ubuntu-latest
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    steps:
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      - uses: actions/checkout@v2
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      - name: Set up Python
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        uses: actions/setup-python@v2
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        with:
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          python-version: "3.9"
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      - name: Install dependencies
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        run: pip install -r requirements.txt
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      - name: Run tests
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        run: pytest

Documentation#

Well-documented code benefits future you and your colleagues. Use docstrings and tools like Sphinx or mkdocs for documentation generation.

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def calculate_momentum(df, period=14):
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    """
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    Calculate momentum based on percentage difference between current price
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    and the price 'period' days ago.
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    Parameters:
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    df (pd.DataFrame): DataFrame containing a 'Close' column.
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    period (int): Number of days to look back.
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    Returns:
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    pd.Series: Momentum values.
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    """
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    return (df["Close"] / df["Close"].shift(period) - 1) * 100