Moving Averages

We can leverage the concept of shift-based methods to calculate our own trends and moving averages.

We will implement two different kinds of moving average:

Let’s consider the following time series dataset of stock prices:

from pandas import read_csv

request_url = "https://raw.githubusercontent.com/prof-rossetti/python-for-finance/main/docs/data/daily_adjusted_NFLX.csv"
prices_df = read_csv(request_url)
prices_df.head()
timestamp open high low close adjusted_close volume dividend_amount split_coefficient
0 2024-06-27 679.35 688.570 678.14 684.34 684.34 2404849 0.0 1.0
1 2024-06-26 672.41 682.960 670.02 677.69 677.69 3545815 0.0 1.0
2 2024-06-25 673.44 673.665 662.30 672.41 672.41 2278768 0.0 1.0
3 2024-06-24 685.00 685.500 664.50 669.02 669.02 3916335 0.0 1.0
4 2024-06-21 683.26 686.900 678.10 686.12 686.12 4557086 0.0 1.0

Because we’ll be using shift-based methods, we must first sort the data, as usual, by date in ascending order:

prices_df.sort_values(by=["timestamp"], inplace=True) #
prices_df.head()
timestamp open high low close adjusted_close volume dividend_amount split_coefficient
99 2024-02-05 562.47 575.7500 557.195 562.06 562.06 4143099 0.0 1.0
98 2024-02-06 564.12 566.3399 554.370 555.88 555.88 2840300 0.0 1.0
97 2024-02-07 558.16 567.8000 554.980 559.30 559.30 4373591 0.0 1.0
96 2024-02-08 560.55 563.7000 555.740 558.53 558.53 3175468 0.0 1.0
95 2024-02-09 565.00 566.0000 558.100 561.32 561.32 3055698 0.0 1.0 
prices_df["ma_50"] = prices_df["adjusted_close"].rolling(window=50).mean()

prices_df["ema_50"] = prices_df["adjusted_close"].ewm(span=50, min_periods=0, adjust=False, ignore_na=False).mean()

prices_df[["timestamp", "adjusted_close", "ma_50", "ema_50"]]
timestamp adjusted_close ma_50 ema_50
99 2024-02-05 562.06 NaN 562.060000
98 2024-02-06 555.88 NaN 561.817647
97 2024-02-07 559.30 NaN 561.718916
96 2024-02-08 558.53 NaN 561.593860
95 2024-02-09 561.32 NaN 561.583121
... ... ... ... ...
4 2024-06-21 686.12 619.8296 632.294057
3 2024-06-24 669.02 620.6344 633.734290
2 2024-06-25 672.41 621.6260 635.250985
1 2024-06-26 677.69 623.0368 636.915260
0 2024-06-27 684.34 624.3732 638.775054

100 rows × 4 columns

import plotly.express as px

px.line(prices_df, x="timestamp", y=["close", "ma_50", "ema_50"],
        title=f"Adjusted Closing Prices",
        color_discrete_map={
            "close": "royalblue",
            "ma_50": "orange",
            "ema_50":"yellow"
        }
)

You’ll notice there are no values for the first N number of periods in our rolling window average (where N is the size of the window). This is because there aren’t enough values to complete the average. It’s OK!

If you would like to change this behavior to be less methodologically strict, we can apply the min_periods parameter, setting minimum number of periods to zero, in which case as many values will be used until we get to fifty, at which point the true rolling average takes over:

# SETTING MIN PERIODS = ZERO:
prices_df["ma_50_min_0"] = prices_df["adjusted_close"].rolling(window=50, min_periods=0).mean()

px.line(prices_df, x="timestamp",
        y=["close", "ma_50_min_0", "ma_50", "ema_50"],
        title=f"Adjusted Closing Prices",
        color_discrete_map={
            "close": "royalblue",
            "ma_50_min_0": "pink",
            "ma_50": "orange",
            "ema_50":"yellow"
        }
)