Working With Data

Your Role

Your primary task is to take stock data, analyze it, and extract meaningful trends to generate buy and sell signals based on logical, programmatic rules.

What does this involve?

It can mean many things, not limited to but including:

• Calculating moving averages and identifying buy signals at low points.
• Detecting trend reversals using indicators like RSI or MACD.
• Parsing historical price data to find patterns in volatility.

To do this effectively, you need to understand the structure of the available data within your strategy function.

About the Stock Data

All stock data is stored in Pandas DataFrames. If you’re not familiar with Pandas DataFrames, think of them as a highly optimized table designed for data analysis and manipulation.

Pandas is an exceptionally robust library for data science, especially for tasks like working with stock data. Be sure to leverage its full capabilities when crafting your strategies.

     high    low     open    close   volume    timestamp
0    1.0034  0.9863  0.9983  1.0000  65434500  1708007400
1    1.0054  0.9881  0.9976  0.9916  49701400  1708093800
2    0.9922  0.9790  0.9887  0.9875  53665600  1708439400
3    0.9947  0.9826  0.9896  0.9916  41529700  1708525800
4    1.0060  0.9924  0.9979  1.0028  52292200  1708612200
...    ...     ...     ...     ...      ...         ...

As pictured above, every stock data DataFrame includes the following columns:
open, close, high, low, volume, and timestamp. These are available for use in generating trading signals. Avoid naming any additional columns with these names to preserve their intended use.

Caution

All stock data is normalized to start at $1. This won’t affect most trading strategies, but if your strategy depends on absolute dollar movements, adjustments may be needed.

For example, a stock that originally moves from $100 to $110 will instead be represented as moving from $1 to $1.10. This scaling ensures consistency across different stocks.

On the next page, you’ll learn how to access data—such as close prices—calculate key metrics, and generate trading signals.

Adding Constant Trading Signals to the DataFrame

To implement trading signals, you need to create a new column in your DataFrame, named signal, with values ranging from -1 (short) to 1 (buy).

To assign a constant value to the signal column, you simply set data['signal'] to a constant:

• data['signal'] = 1 creates a column with all 1s (buy and hold strategy).
• data['signal'] = -1 creates a column with all -1s (short and hold strategy).
• data['signal'] = 'lol' creates a column filled with the string 'lol' (this will cause an error, avoid doing this).

If you run and return data['signal'] = 1, your final signals column will look something like this:

signal
1
1
1
1
...
1
1
1
1

More Resources

We recommend scanning through the Pandas documentation. They do a better job than we do in describing the DataFrame and its capabilities!

• A brief intro to DataFrames: (Click Here)
• Pandas, a 10-minute crash course: (Click Here)
• DataFrames, for nerds, via the official API documentation: (Click Here)