STOKE MARKET
1. Learn Python Basics & Data Handling
Tools: Python, Pandas, NumPy.
Topics to Learn:
Basic Python syntax (variables, loops, conditionals).
Pandas basics (DataFrames, Series, operations on data).
NumPy for numerical operations.
Why: You need these to work with stock data and perform data manipulation.
2. Working with CSV Files
Tools: Pandas.
Topics to Learn:
Reading and writing data from/to CSV files.
Data exploration (head, tail, info).
Saving and loading datasets.
Why: You’ll need to handle CSV files to store and process stock data.
Example:
Import pandas as pd
# Reading CSV
Data = pd.read_csv(‘sample_stock_data.csv’)
# Saving to CSV
Data.to_csv(‘output.csv’, index=False)
3. Download Stock Data
Tools: yfinance, Alpha Vantage.
Topics to Learn:
Fetch historical stock data using APIs.
Understand time periods (start, end dates).
Why: You’ll need stock data to perform all further analysis.
Example:
Import yfinance as yf
# Download data for AAPL
Stock_data = yf.download(‘AAPL’, start=’2022-01-01’, end=’2023-01-01’)
Stock_data.to_csv(‘apple_stock_data.csv’)
4. Data Preprocessing & Cleaning
Tools: Pandas.
Topics to Learn:
Handling missing values (fill, drop, interpolate).
Data normalization (scaling features).
Filtering and removing outliers.
Why: Clean data is essential before analysis or machine learning.
Example:
# Fill missing values
Stock_data.fillna(method=’ffill’, inplace=True)
5. Get Data in DataFrame
Tools: Pandas.
Topics to Learn:
Convert stock data to a Pandas DataFrame.
Perform basic operations on DataFrame (summarizing, inspecting).
Why: A DataFrame is the primary structure for analysis in Python.
Example:
Df = pd.DataFrame(stock_data)
Print(df.head())
6. Feature Engineering
Tools: Pandas, TA-Lib (for indicators).
Topics to Learn:
Create technical indicators (e.g., moving averages, momentum).
Adding new columns based on existing data.
Why: Additional features (technical indicators) improve your model’s prediction
accuracy.
Example:
# Calculate Moving Average
Stock_data[’50 Day MA’] = stock_data[‘Close’].rolling(window=50).mean()
7. Calculate Daily Returns
Tools: Pandas.
Topics to Learn:
Percentage change (daily return) formula.
Why: Daily returns are essential for financial analysis and machine learning models.
Example:
Stock_data[‘Daily Return’] = stock_data[‘Close’].pct_change()
8. Calculate Cumulative Returns
Tools: Pandas.
Topics to Learn:
Cumulative product of daily returns.
Why: Cumulative returns show overall growth over time.
Example:
Stock_data[‘Cumulative Return’] = (1 + stock_data[‘Daily Return’]).cumprod()
9. Bollinger Bands
Tools: Pandas.
Topics to Learn:
20-day moving average.
Upper and lower bands using standard deviation.
Why: Bollinger Bands are a popular technical indicator to identify volatility and
overbought/oversold conditions.
Example:
Stock_data[’20 Day MA’] = stock_data[‘Close’].rolling(window=20).mean()
Stock_data[‘Upper Band’] = stock_data[’20 Day MA’] + 2 *
stock_data[‘Close’].rolling(window=20).std()
Stock_data[‘Lower Band’] = stock_data[’20 Day MA’] – 2 *
stock_data[‘Close’].rolling(window=20).std()
10. Ichimoku Cloud (and Conversion Line)
Tools: Pandas, TA-Lib (if desired).
Topics to Learn:
Ichimoku Cloud calculations, including conversion line.
Why: Ichimoku Cloud is another popular technical indicator that helps determine
market trends and support/resistance levels.
Example:
Stock_data[‘Conversion Line’] = (stock_data[‘High’].rolling(window=9).max() +
stock_data[‘Low’].rolling(window=9).min()) / 2
11. Backtesting Strategies
Tools: Backtrader, Zipline.
Topics to Learn:
Define your trading strategy (e.g., moving average crossover).
Implement backtesting to evaluate strategy performance.
Why: Backtesting helps validate whether a trading strategy would have been profitable
in the past.
Example:
Import backtrader as bt
# Define a simple moving average strategy
Class MovingAverageStrategy(bt.Strategy):
Def __init__(self):
Self.moving_avg = bt.indicators.SimpleMovingAverage(self.data.close, period=20)
Def next(self):
If self.data.close[0] > self.moving_avg[0]:
Self.buy()
Elif self.data.close[0] < self.moving_avg[0]:
Self.sell()
12. Machine Learning (ML) for Stock Prediction
Tools: Scikit-learn, TensorFlow.
Topics to Learn:
Train and test machine learning models (e.g., Random Forest, Linear Regression).
Understand how to evaluate models using metrics like accuracy, MAE, MSE.
Why: ML can help predict future stock prices or trends based on historical data.
Example:
From sklearn.ensemble import RandomForestRegressor
# Train-test split
X = stock_data[[‘Open’, ‘High’, ‘Low’, ‘Volume’]]
Y = stock_data[‘Close’]
# Create model
Model = RandomForestRegressor()
Model.fit(X, y)
13. Model Evaluation
Tools: Scikit-learn.
Topics to Learn:
Evaluate model performance using accuracy, R-squared, MAE, MSE.
Why: Evaluating your model ensures that it generalizes well to unseen data.
Example:
From sklearn.metrics import mean_absolute_error
# Calculate MAE
Mae = mean_absolute_error(y_test, model_predictions)
Print(f’Mean Absolute Error: {mae}’)
14. Plotting and Visualization
Tools: Matplotlib, Seaborn.
Topics to Learn:
Visualize stock data, technical indicators, and backtest results.
Why: Visualization helps in better understanding of data and model predictions.
Example:
Import matplotlib.pyplot as plt
# Plot Closing Prices
Plt.figure(figsize=(12,6))
Plt.plot(stock_data[‘Close’], label=’Close Price’)
Plt.plot(stock_data[’20 Day MA’], label=’20 Day MA’, linestyle=’—‘)
Plt.legend()
Plt.show()
15. Feed Data into Models (Live Data/Automation)
Tools: API Integration, Real-time data streaming.
Topics to Learn:
Automate data collection and feeding into models.
Use APIs to get live stock data for predictions.
Why: Live data feeding is essential for real-time trading or predictive models.
Final Advice:
Practice as You Learn: Apply each step to small projects to get hands-on experience.
Take One Step at a Time: Don’t rush through concepts; make sure you understand each
one before moving on to the next.
Use the Right Resources: Kaggle, Coursera, and books like “Python for Finance” will give
you great resources.
Backtest and Paper Trade First: Test your strategies using historical data before investing
real money.
This roadmap now covers everything from data handling, stock analysis, machine
learning, and strategy backtesting, in a logical and clear order. You’re all set to start your
journey! Let me know if you need help with any specific step or code.
