Python has taken the data analytics space by storm – more so in the financial services space. With the rapid growth of data and ever-increasing need to process data faster and getting results of back-testing in shorter spans of time, there is no other better time than now to delve into the world of languages like Python. To give a perspective, the Indian Market Fundamental Data on Quandl has a dataset of a little over 9.1 million rows.
In the last tutorial we explored Kalman filter and how to build kalman filter using pykalman python library. In this section we will be dealing with python com server to integrate Amibroker + Python to compute Kalman Filter and Unscented Kalman Filter Mean Estimation and plot the same in Amibroker.
Kalman Filter is an optimal estimation algorithm to estimate the variable which can be measured indirectly and to find the best estimate of states by combining measurement from various sensors in the presence of noise. This tutorial talks about implementation of Kalman Filter Estimation of Mean in IPython Notebook using PyKalman, Bokeh, NSEPy and pandas to plot Interactive Intraday Candlestick Charts with Kalman Filter
Trading systems evolve with time and any programming language choices will evolve along with them. If you want to enjoy best of the both the worlds in algorithmic trading-benefits of a general purpose programming language and powerful tools of the scientific stack- choose an algorithmic trading course that introduces python trading platforms.
Here is a quick tutorial in python to compute Correlation Matrix between multiple stock instruments using python packages like NSEpy & Pandas. Generally Correlation Coefficient is a statistical measure that reflects the correlation between two stocks/financial instruments. Determining the relationship between two securities is useful for analyzing intermarket relationships, sector/stock relationships and sector/market relationships.
First of all thanks for your impressive and motivational reponse for the Nifty Returns Heatmap Generation post. Here is yet another simple visualization stuff using python to compute bunch of NSE Sectoral Indices returns (52 Weeks, YTD , MTD, Last month returns) and visualize the same in a barchart.
Everyone love to visualize this market in their own way. Python comes handy when comes to visualization. One of the Awesome programming language to code any level of complexity. However I start here by simply generating a heatmap which visualizes the Historical Nifty returns since 2000 and there by visualizing the reality of the market.
Here is an yet another interesting python tutorial to fetch intraday data using Google Finance API , , store the data in csv format and also plot the intraday data as candlestick format. We are using plotly library for plotting candlestick charts and pandas to manage time-series data.
In the last tutorial we had seen how to import data from NSEindia using NSEpy library and how to compute co-integration. In this tutorial we will be using plotly – a library to visualize your data interactively and pandas – library to manage time series data to build interactive candle stick charts.
Here is a simple example to compute Cointegration between two stock pairs using python libraries like NSEpy, Pandas, statmodels, matplotlib
Cointegration is used in Statistical Arbitrage to find best Pair of Stocks (Pair Trading) to go long in one stock and short(Competitive peers) another to generate returns. Statistical Arbitrage(StatArb) is all about mean reversion, looking for deviation in the spreads and expecting mean reversion from the spread.
In case if you missed the Quantcon 2015 a disruptive quant trading event, will break down the existing walls to algorithmic trading by giving you an inside look at tools and content sets. Mebane Faber’s Market Outlook 2015 and EP Chan’s session on “Beware of Low Frequency data” , Tukar Balsh session on “10 ways backtest lie” are my favorites. So here is the recorded sessions happened in Quantcon2015.