Then we can import some useful packages: import yfinance import pandas as pd import numpy as np import matplotlib.pyplot as plt from scipy.stats import ttest_ind
It’s very useful for downloading stock data.
#SHIMO NAME MEANING INSTALL#
An example in Python Short-term investmentįirst of all, we must install yfinance library. The statistical significance of the mean values will be done using Welch’s test. If this check is satisfied, we can use the moving average length we found.įor this example, we’ll use different stocks and investment length. The last point is the most important one because it performs cross-validation that helps us avoid overfitting after the optimization phase.
Verify that the average return on the test set is statistically similar to the average return achieved on the training set. Use this moving average to calculate the average return on the test set. Choose the moving average length that maximizes such average return. Apply different moving averages on the training set and, for each one, calculate the average return value after N days when the close price is over the moving average (we don’t consider short positions for this example). Split this dataset into training and test sets. Take some years of daily data of our stock (e.g. The reason of using the average return after N days as an objective function is pretty simple: we want our moving average to give us the best prediction of the trend according to the time we want to keep stocks in our portfolio, so we want to maximize the average return of our investment in such a time. if we buy at 100 and after the chosen period the price rises to 105, we have a 5% return). After we choose this period, we’ll try different moving averages length and find the one that maximizes the expected return of our investment (i.e. The algorithm I propose here is an attempt to find the best moving average according to the investment period we choose. The algorithm for the best moving average So there isn’t the right choice for the moving average period, but we can build a model that self-adapts to market changes and auto-adjust itself in order to find the best moving average period. Markets change a lot during the time and they often make traders fine-tune their indicators and moving averages in order to follow volatility burst, black swans and so on. We have seen 20, 50 and 200 periods, but are they always good? Well not really. Once we have determined it, we must try to set a suitable SMA period. If we are portfolio traders and use moving averages as a technical filter in our stock screening plan, maybe we can focus on 200–300 days.Ĭhoosing the investment period is a discretionary choice of the trader. 
If we are position traders, maybe we must raise this threshold to 40–60 days.
If we are swing traders, we may want to keep it for 5–10 business days. In order to find the best period of an SMA, we first need to know how long we are going to keep the stock in our portfolio. So they are often looking for stocks whose price is above the 200-periods SMA. It’s a general rule of thumb among traders that if a stock price is above its 200-days moving average, the trend is bullish (i.e. Generally speaking, the most used SMA periods in trading are: As you can see, short moving averages are useful to catch short-term movements, while the 200-period SMA is able to detect a long-term trend.
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