### How to Make Stock Price Predictions Using Reinforcement Learning?

Analyzing the stock market using artificial intelligence has been a work in progress recently. Here, weâ€™ll discuss how you can develop an AI stock prediction model using reinforcement learning. Analyzing the behavior of the stock market has been a subject of interest and challenge in the AI industry. Data scientists, market analysts, and financial experts have been curious to determine whether it is possible to overcome these challenges. The biggest concern is the need for an extra large dataset to build a predictive system based on supervised learning algorithms. Furthermore, even the most advanced technologies seem to be inadequate to accurately predict the changing prices in the stock market. Yet, accurate AI stock price prediction could be possible without relying on large datasets. In this blog, weâ€™ll try to identify the challenges of stock market prediction and understand if we can use reinforcement learning for stock prediction and data analysis in Python, that too, using limited or no data to train the algorithm. Before we proceed to read more about stock price prediction using machine learning, letâ€™s understand more about the data analysis methods used to process stock market data. Types of Data Analysis Techniques Used on Share Market Data The stock market data is analyzed in different techniques. These are categorized as – Time Series Analysis and Statistical Data Analysis. 1. Time Series Analysis A time series is defined as a sequence of data points that appear/ occur in successive order in a given period. It is the opposite of cross-sectional data, where the events that occur at a specific time point are captured. The time series analysis tracks the movement of the chosen data points over the specified period. The data points are usually the price of the stock/ share/ security. The prices are collected at regular intervals to analyze the patterns. There are various techniques to perform the time series analysis on stock market data. Letâ€™s check them out in brief. a. Moving Averages: The moving average of a stock is calculated to smooth the price data and constantly update the average price. In finance, the MA (moving average) is considered a stock indicator and is used in technical analysis. The short-term price fluctuations are mitigated in this process. The MA is further divided into the following: i. Simple Moving Average (SMA) SMA is calculated using the arithmetic mean for a given set of values over a specific period. Here, the set of values is the stock prices. These are then added and divided by the number of prices in the set. Formula: A1+ A2+ A3+â€¦ Ann Here, A= average in the period; nn= number of periods; SMA= n ii. Exponential Moving Average (EMA) The EMA gives more importance to recent prices to make the average price more relevant based on the new information. The SMA is calculated first to use in the EMA formula. The smoothing factor is calculated next to determine the weighting of EMA- 2/(selected period+1). Formula: EMAt= [VtÃ—(1+ds)]+EMAyÃ—[1âˆ’(1+ds)] Here, EMAt= todayâ€™s EMA; Vt= todayâ€™s value; EMAy= yesterdayâ€™s EMA; ds= smoothing (number of days) Some other types of moving averages are: b. ARIMA: It is another approach to time series forecasting. ARIMA and exponential smoothing are widely used methods as they offer a complementary approach to the problem. ARIMA describes the auto-correlations in data, while exponential smoothing relies on seasonality in data and trend description. c. Box Jenkins Model: This model can analyze different types of time series data for forecasting. It is a mathematical model that uses inputs from specified time series to forecast data ranges. The Box Jenkins model determines the outcomes based on the differences between data points. It identifies trends for forecasting stock prices using autoregression, moving averages, and seasonal differences. d. Rescaled Range Analysis: It is a statistical technique developed to assess the magnitude and nature of data variability over a certain period. The rescaled range analysis method is used to identify and evaluate persistence, randomness, and mean reversion based on the time series data from the stock markets. This insight is used to make proper investment strategies. 2. Statistical Data Analysis It is the common value that occurs in the dataset. It is the middle number in the dataset. For example, in 4, 6, 7, 9, and 11, the median is 7. It is the average value of the dataset. It is also called standard normal distribution or Gaussian distribution model. It is charted along the horizontal axis, representing the total value spectrum in the dataset. The values of half the dataset will be higher than the mean, while the other half will be longer than the mean. And the other data points will be around the mean, with a few lying on extreme/ tail ends on both sides. It measures the asymmetry/ symmetry of the price/ data point distribution. The skewness will be zero in a standard normal distribution. A negative skewness will lead to a distorted bell curve on the left, while positive skewness will cause a distorted bell curve on the right side. What is Reinforcement Learning? It is an area of machine learning that takes the appropriate action to maximize returns for a given situation. Many software applications and machines use reinforcement learning (RL) to identify the best behavior/ path to arrive at the desired result for a specific situation. Reinforcement learning is different from supervised learning. In the latter, the training data is the answer key to training the model with the correct answer. However, in RL, the reinforcement agent decides which task to perform, as there is no specific answer used for training. It allows machine learning developers to train the algorithm without using a dataset. The algorithm will learn from experience and improve itself over time. What are the Different Datasets Available for Stock Market Predictions? Fortunately, there are a few datasets available to train the algorithms. Developers can access the datasets from the following: NIFTY-50 Stock Market Data The data is available from 1st January 2000 to 31st April 2021. It provides

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