Quantitative_Trading_with_AI

Some quantitative method and AI Algorithms used in trading.

Trading_with_Momentum

Resample Adjusted Prices

The trading signal you'll develop in this project does not need to be based on daily prices, for instance, you can use month-end prices to perform trading once a month. To do this, you must first resample the daily adjusted closing prices into monthly buckets, and select the last observation of each month.

Compute Log Returns

Compute log returns (R_t) from prices (P_t) as your primary momentum indicator:

Shift Returns

Shift the log returns to the previous or future returns in the time series.

Generate Trading Signal

A trading signal is a sequence of trading actions, or results that can be used to take trading actions. A common form is to produce a "long" and "short" portfolio of stocks on each date (e.g. end of each month, or whatever frequency you desire to trade at). This signal can be interpreted as rebalancing your portfolio on each of those dates, entering long ("buy") and short ("sell") positions as indicated.

Here's a strategy that we will try:

For each month-end observation period, rank the stocks by previous returns, from the highest to the lowest. Select the top performing stocks for the long portfolio, and the bottom performing stocks for the short portfolio.

T-Test

Our null hypothesis ($H_0$) is that the actual mean return from the signal is zero. We'll perform a one-sample, one-sided t-test on the observed mean return, to see if we can reject $H_0$. We'll need to first compute the t-statistic, and then find its corresponding p-value. The p-value will indicate the probability of observing a mean return equally or more extreme than the one we observed if the null hypothesis were true. A small p-value means that the chance of observing the mean we observed under the null hypothesis is small, and thus casts doubt on the null hypothesis. It's good practice to set a desired level of significance or alpha ($\alpha$) before computing the p-value, and then reject the null hypothesis if .

test_normality

Testing if a Distribution is Normal

Boxplot-Whisker Plot, Histogram and QQ-Plot

We can visually check if a distribution looks normally distributed.

1. A box whisker plot lets us check for symmetry around the mean.
2. A histogram lets us see the overall shape.
3. A QQ-plot lets us compare our data distribution with a normal distribution (or any other theoretical "ideal" distribution).

Testing for Normality

Shapiro-Wilk

Kolmogorov-Smirnov

Pairs_Candidates

Checking if a pair of stocks is cointegrated

Calculate hedge ratio with regression

Calculate the spread

Check if spread is stationary using Augmented Dickey Fuller Test