Stock Price Prediction On Commercial Data Using RNN

A type of neural network called a recurrent neural network (RNN) uses the output from a previous step as input for the current step. Traditional neural networks have inputs and outputs that are independent of one another, but there is a need to remember the previous words in situations where it is necessary to anticipate […]

Stock Price Prediction On Commercial Data Using GRU

GRU are a kind of Neural Networks which are designed to overcome some issues with RNN. There are two primary issues with recurrent neural networks Calculations of gradients either fail or explode. Gradient calculations are expensive Gradient clipping is a solution to the expanding gradient problem, and other topologies like the gated recurrent unit (GRU) […]

Stock Price Prediction On Commercial Data Using LSTM

LSTMS are a variety of RNNs but lets start with RNNs. RNNs are unable to remember long-term dependencies in time series data because of the vanishing gradient issue. An RNN version called LSTM was created to deal with this problem. Similar to RNN, LSTM features a hidden state that functions as short-term memory. Additionally, it […]

Apache Superset for COVID Dashboards

Apache Superset is a very useful and easy-to-use visualization and dashboard-making tool that can be an alternative to tools like Tableau and PowerBI. In this blog, we will explore how we can create awesome data dashboards using Apache superset with little to no code at all. But there are a few things one should do […]

Python for Stock Market Analysis: Alpaca API

Introduction Alpaca Trading API is an API using which we can retrieve stock data in realtime. It provides various APIs and even streaming services. Please read about it in their docs. What is most exciting about this API and the librariy is that it returns the data as a Pandas Dataframe or even simple Dict […]

Python for Stock Market Analysis: Getting Started into Modeling Timeseries

Introduction Hello there, this is the part 5 of Python for Stock Market Analysis and in this part, we will continue from where we left i.e. modeling a timeseries. Finding a best set of parameters that gives highly accurate prediction is always a hard job and there is not always a guarantee that one can […]

Python for Stock Market Analysis: Getting Started into Timeseries Analysis

Introduction This is the part 4 of our Python for Stock Market Analysis series and here, we will be getting started with timeseries analysis. This part will not be exploring any prediction techniques yet as we will explore fundamental concepts in timeseries. Making Things Ready Here, we will import Pandas for data analysis, install as […]

Python for Stock Market Analysis: Growth Rates

Introduction Interactive plot version of blog is available at here. This is the part 3 of our Python for Stock Market Analysis series and here, we will explore some of popular growth rates that can be used to see how well is our value is changing over the period of time. Lets take some of […]

Python for Stock Market Analysis: Exploring Technical Trend Indicators

Introduction Hello and welcome back everyone to our second part of the new blog series [Python for Stock Market Analysis](). In the last part, we explored different [types of moving averages]() like Simple Moving Average (SMA), Exponential Moving Average (EMA), Weighted Moving Average (WMA) and explored other moving metrics like Moving Median and Moving Variance. […]

Python for Stock Market Analysis: Working with Moving Averages

Introduction This blog is a part of our series Python for Stock Market Analysis. Disclaimer: This blog is for educational purpose only and we do not recommend taking the knowledge gained from this blog to implement in real financial exercises. This blog tries to implement preliminary metrics that are used in the stock market analysis. […]

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