AI for Trading Series №1: The Stock Prices

Purva Singh
12 min readNov 18, 2020

Learn about basic terminologies used while analyzing stocks.

Photo by M. B. M. on Unsplash



  1. Stock : An asset that represents ownership in a company. A claim on part of a corportation’s assets and earnings. There are two main types, common and preferred.
  2. Share : A single share represents partial ownership of a company relative to the total number of shares in existence.
  3. Common Stock : One main type of stock; entitles the owner to receive dividends and to vote at shareholder meetings.
  4. Preferred Stock : The other main type of stock; generally does not entail voting rights, but entitles the owner to a higher claim on the assets and earnings of a company.
  5. Dividend : A partial distribution of a company’s profits to shareholders.
  6. Capital Gains : Profits that result from the sale of an asset at a price higher than the purchase price.
  7. Security : A tradable financial asset.
  8. Debt Security : Money that is owed and must be repaid, like government or corporate bonds, or certificates of deposit. Also called fixed-income securities.
  9. Derivative Security : A financial instrument whereby its value is derived from other assets.
  10. Equity : The value of an owned asset minus the amount of all debts on that asset.
  11. Equity Security : A security that represents fractional ownership in an entity, such as stock.
  12. Option Contract : A contract which gives the buyer the right, but not the obligation, to buy or sell an underlying asset at a specified price on or by a specified date
  13. Futures Contract : A contract that obligates the buyer to buy or the seller to sell an asset at a predetermined price at a specified time in the future

Buy and Sell Side

  1. Buyers and sellers are those who go through the stock exchange to buy a stock that they think will do well, or sell a stock that they wish to remove from their investments.
  2. Market maker, is the one who serves as the counterparty of these buyers or sellers. Since every buyer needs a seller, and every seller needs a buyer, a market maker plays the role of seller to those who wish to buy, and plays the role of buyer for those who wish to sell and by convention they are known as sell side of the finance industry. The sell side usually includes investment banks such as Goldman Sachs, Morgan Stanley.
  3. The buy side refers to individual investors, and investment funds such as mutual funds and hedge funds.


Liquidity refers to a property of a financial asset which is meant to be bought or sold without causing sharp changes in its price.

Tick Data

Stock exchange publishes a stream of data that includes each individual trade. This is known as tick data. Ticks are an intuitive way to check the health of a stock. Tick data can also forms the basis of all market data available for analysis and help you make better intraday decisions.

Tick Data Source: AI for Trading nano degree course on Udacity

OHLC : Open, High, Low, Close

The most common terms used in practice are Open, High, Low, Close (OHLC). Below is an example of how we represent OHLC:

  • Open is the stock price at the begining of the period.
  • High and Low capture its range of movement
  • Close is where it ends.
  • Daily closing price is the one that is quoted most often. This is usually used by casual traders and investors interested in long term gains.
  • Opening price is where the first trade of the day to take place. There might be a gap from last day’s closing price due to pre-market trading or trading in other markets.
  • High-Low captures the movement of the stocks


Another valuable metric is the number of shares traded over a period of time known as Volume. Sum of unit_price times volume gives an accurate description of total amount of money moving around. Volume can also decide how sharply the price may rise or fall. In general:

  • large volume of buy order tends to increased stock price.
  • large volume of net sell orders tends to decreased stock price.

Intraday Volume

Stocks that are of active interest will see a lot of trading at the begining of the day. All the investors engage in a process called price discovery where they analyze all the new information gathered since the previous day’s market close. This process of price discovery helps buyers and sellers agree on a mutually accpetable market price value for the stock.

Intraday Volume, Source: AI for Trading nano degree course on Udacity

Then the volume falls as the day proceeds. Finally, towards the end of the trading day, activity tends to increase a little, resulting in higher volume. This can happen due to day-traders wanting to close out any open positions and funds that typically update their holdings at the end of the day.

Intraday Volume Pattern , Source: AI for Trading nano degree course on Udacity

Data Processing

Stock Splits

The set of data related to an event that a company an take which may affect the shareholders are called coroporate actions. Some of the corporate actions are-

  1. Stock Splits
  2. Divident

When a company decides to split its stocks into two, its price drops by half. This makes sure that total market capitalization has not changed by the split. Market Capitalization is the dollar value of a company’s outstanding shares.

Market Capitalization = Stock Price X Total number of shares outstanding
Stock Split for AMZN, Source: AI for Trading nano degree course on Udacity

Now when stock split happens (2:1), there are twice as many outstanding shares. In order to neutralize the market capitalization, the stock price has to drop by half.
One of the reasons as to why companies perform stock split is to make the stock more liquid in order to maintain healthy volume of transactions.

Stock Split Normalization

If we look at the graph of a company’s stock price that has recently performed stock split, it may look like that company’s stock has reduced drastically, which is not the case. The value of the company has not changed since the split. In order to correct this, we need to normalize the data to mitigate the sudden changes.

Source: AI for Trading nano degree course on Udacity

One of the ways to normalize stock-split data is to half the price before 2:1 split, thirds the price before any 3:1 split and so on. Stock prices normalized in such manner are called adjusted prices.

Normalization of Stock Split data, Source: AI for Trading nano degree course on Udacity


Dividends are when companies share some fraction of their profits with their shareholders. Dividends are given only to those share-holders who have bought the shares before the ex-dividend date.

Dividends Normalization

In order to normalize stocks based on dividends, we need to first calculate adjusted price factor as per the formula below :

Adjusted Price Factor = 1 + Dividend/(Stock price at ex-dividend date)

To normalize the price, we need to divide the historical price by adjusted price factor.

Technical Indicators

In this section, we will learn about the following terminologies-

  1. Moving Window/ Rolling Mean/ Simple Moving Average
  2. Bollinger Bands
  3. Price to Earnings Ratio
  4. Exchange Traded Funds (ETFs)
  5. Stock Returns : Raw Returns and Log Returns

Moving Window or Rolling Mean

After adjusting the stock prices based on dividends, in order to use this information for buying/selling stocks, we need to first compute statistical measures known as indicators for ex. raw price of the stock. In order to make a decision on which stock to buy/sell, we need to compute the expected price of that stock. This can be computed by calculating the recent average price (average over few weeks/months) of the stock. This can be done by calculating average over fixed window length over time. This is known as Simple Moving Average (Rolling Mean).

Simple Moving Average (Rolling Mean), Source: AI for Trading nano degree course on Udacity

If the stock price falls too far below this average, we should buy it and if stock price rises well above this average, we should sell it.

Source: AI for Trading nano degree course on Udacity

Bollinger Bands

So how low is too low or how high is too high? We basically need a threshold value before we can buy/sell stocks. For this we need a measure that is tied to the stock price. One such measure can be standard deviation over the rolling window. In general we basically create 2 bands:

  1. 2 Standard Deviations above the mean.
  2. 2 Standard Deviations below the mean.

The lines that form 2 S.D above and below the mean are called Bollinger Bands.

Bollinger Bands, Source: AI for Trading nano degree course on Udacity

Now if we point the plots that are above and below these Bollinger Bands, we can see very few outliers and picture becomes clearer.

Source: AI for Trading nano degree course on Udacity
  1. When a particular point below the lower Bollinger band tries to crawl back inside the mean, that’s when we should buy the stocks.
  2. On the other hand, we can sell the stock if it starts to decrease towards the mean.
Buy/Sell Strategy using Bollinger Bands, Source: AI for Trading nano degree course on Udacity

Price to Earnings Ratio

A term you’ll see often is price to earnings ratio, or PE ratio for short.

PE Ratio = (stock’s current market price) / (most recently reported earnings per share (EPS))

You can sort of interpret the PE ratio as how much the company is valued compared to how much money it made. The market price of a stock is based on both its current assets minus liabilities, but also estimates of the company’s future performance.

A high PE ratio may indiciate that based on historical earnings growth, investors expect potential for high earnings growth. On the other hand, it’s also possible that investor optimism towards the company’s future never materializes, in which case the stock may be overpriced.

Also, an example of a company with a low PE ratio may be one that has high and stable earnings, but less expectations for future growth.

Exchange Traded Funds (ETFs)

Many banks and other financial institutions offer mutual funds where the professionals pull the money from multiple investors and buy shares on their behalf. We can chose funds according to our investment goals -

  1. Lower rate of return : Reduced risk
  2. Higher rate of return : High risk

In addition to combining multiple stocks, some funds are traded on stock exchange themselves i.e. in order to invest money in these funds, we need to buy their shares on the market. Hence they are known as Exchange Traded Funds (ETFs). A popular ETF is Standard & Poor’s 500 (S&P500)

Considering an example where I want to diversify my investment to reduce market risk. This can be done by analyzing individual performance or correlations between stocks. This generates a portfolio that is well balanced and not too corelated.

Stock Returns

Raw Returns

The measure of how much the client’s investment has increased or decreased in value is known as Stock Return. The raw return may be referred to simply as the return, or alternatively, as the percentage return, linear return, or simple return. It can be calculated as below :

Stock Returns, Source: AI for Trading nano degree course on Udacity

Notebook on how to calculate stock returns : calculate_stock_returns.ipynb

Log Returns

Log returns provide more intuitiive way to analyze how much the client’s investment has increased or decreased in value. Log returns can be calculated as follows :

Why log returns?

These are some generally accepted reasons that quantitative analysts use log returns:

  1. Log returns can be interpreted as continuously compounded returns.
  2. Log returns are time-additive. The multi-period log return is simply the sum of single period log returns.
  3. The use of log returns prevents security prices from becoming negative in models of security returns.
  4. For many purposes, log returns of a security can be reasonably modeled as distributed according to a normal distribution.
  5. When returns and log returns are small (their absolute values are much less than 1), their values are approximately equal.
  6. Logarithms can help make an algorithm more numerically stable

Trading Strategies

Momentum Trading Strategy

Taking a reference from Newton’s Laws of Motion, an object at rest continues to stay at rest or an object moving at a constant speed continues to move at that constant speed unless acted upon by an unbalanced force.
Somewhat similar strategy can be applied to stock market as well. In momentum trading we assume that the stocks that are going up, will continue to go up and stocks that are falling down will continue to keep falling.

Long and Short Positions

Lets assume that you are using momentum signals as a way of predicting stock’s movement. There are 2 cases that arise here -

  1. Stock has an upward momentum.
    In this case, you will buy the stocks and hold on to it for a fixed time or until stock starts to fall. This is known as taking a long position on the stock. And when you sell your stock at a higher position than you bought it, that is known as closing your position.
Long Position, Source: AI for Trading nano degree course on Udacity
  1. Stock has a downward momentum.
    In this case, you believe that, due to momentum strategy, it will continue to fall down for some time. In this scenario, you take a short position on stock, where you sell first and buy back later.
Short Position, Source: AI for Trading nano degree course on Udacity

It means that you borrow shares from someone, ex. broker and promise to return them once your short position is closed. Brokers earn from the profit you make on your short sale. In case you fail to fulfill the promise and never buy-back the shares, in this case, brokers ask you to keep some money in margin account.

Cross-sectional Strategy

It can be risky to invest in individual stock markets. Instead, you can adopt a cross-sectional strategy, where you invest in multiple stocks at the same time and use the ranking to select stocks for long (top-performers) and short (bottom performers) positions.

Cross-sectional Trading Strategy, Source: AI for Trading nano degree course on Udacity


A portfolio is a collection of investments held and/or managed by an investment company, hedge fund, financial institution or individual.


A long (or long position) is the purchase of an asset under the expectation that the price of the asset will rise.


A short (or short position) is the selling of an asset under the expectation that the price of the asset will decline. In practice, an investor profits from a short position by borrowing shares from a brokerage firm (agreeing to pay an interest rate as a fee), selling them on the open market, and later buying them back on the open market at a lower price and returning them to the brokerage firm.

Trading Strategy

Below figure describes the 5 steps to formulate your trading strategy and create a portfolio of long and short positions -

  • Stock Universe : group of stocks that share some common features or belong to the same market.
  • By daily close prices, the figure means adjusted closing prices.
  • In the example below, we have chosen a stock universe from S&P500. Make sure that the dataset you choose for testing your strategy for a particular year, say 2020, contains the companies that were a part of stock universe in 2020.
Formulating your Trading Strategy, Source: AI for Trading nano degree course on Udacity

Calculating the performance of our trading strategy

Consider a scenario where our goal is to check if mean monthly return of our portfolio is greater than 0. Now, based on our trading strategy we see that monthly mean is 0.53% (greater than 0). This mean could also be a random fluctuation and our true mean could be less than or equal to 0.

Statistical Test

One way to test our monthly mean is actually greater than 0 or not is by performing t-statistic test. The formula for t-test is shown in the figure below:

t-statistic test, Source: AI for Trading nano degree course on Udacity

Using this t-statistic, we can calculate the probability of getting mean monthly return of 0.53% or greater if true mean monthly return zero, given the assumptions made to build our model are correct. This probability is called p-value.

p-value, Source: AI for Trading nano degree course on Udacity
  • p-value is very small : unlikely that true mean is zero. We need to also set a threshold for p-value to conclude that if p-value falls below this threshold, the true-mean is unlikely to be zero. This threshold is denoted by the term alpha. Commonly used value is 0.1.
t-Test, Source: AI for Trading nano degree course on Udacity



Purva Singh

Hi! I am a tech enthusiast currently working on leveraging language technologies to solve financial use-cases! View my work here: