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Outrun the Market: March 2011

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Tuesday, 29 March 2011. Bottom Fishing,Top Spotting - 4 (Understanding Chart Patterns). Objective: Introduce Price Patterns, Reversal and Continuation Patterns,. Head and Shoulder-Top' and 'Inverse Head and Shoulder'. Predicting changes in trend or turning points in the stock price is key to making money in stock markets. Judging the trend reversal is bit difficult as many minor reversal will send out confusing signals to the traders and they may press the panic button. Double Tops and Bottoms. First sig...

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Outrun the Market: MODELING STOCK PRICES

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Wednesday, 6 June 2012. A random walk is a nonstationary time series. Nonstationary time series are usually transformed to stationary time se-. The logarithm of the stock price series is usually modeled as a random. Walk If you take the difference (yt - yt-1) of the logarithm of the stock price, the resultant series will be a white noise. Checking with the following piece of python code :. From datetime import date. From StringIO import StringIO. Import numpy as np. Import numpy as np. MeanArray[pairInde...

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Outrun the Market: September 2013

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Tuesday, 17 September 2013. Options Strategies - Excel Work Book. Here is an excel workbook which comes handy when you want to try out various strategies using options. You can download this at. Options Strategy- Excel Work sheet. Found under http:/ www.nsecentral.com/home/reference-books. Also, visit http:/ optionstrategies.nsecentral.com/strategist/. For some live strategies based on Nifty Index (NSE India). Subscribe to: Posts (Atom). Options Strategies - Excel Work Book. Cool Links - Archive.

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Outrun the Market: OLS Estimation

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Monday, 11 June 2012. Simple Regression with ORDINARY LEASE SQUARES:. Assume the following Data:. Excess Return on Funds. Excess Return on Markets. Excess Return over Risk Free Rate. Independent variable (xt)= Excess Return on Markets. Dependent variable (yt) = Excess return on Funds. Yt = α βxt u t. True line with OLS regression:. Slope β= cov(x,y)/var(x). Intercept α = mean(y) - β*mean(x). X1=nparray([13.7,23.2,6.9,16.8,12.3]). Look at the results. From the above summary,. Α = -1.7366, β= 1.6417.

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Outrun the Market: Heteroscedasticity - Applying correction

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Monday, 18 June 2012. Heteroscedasticity - Applying correction. Procedure to apply correction for heteroskedasticity. Step 1 : Primary Regressiion. Run the primary regression on yi = const beta1*x1i beta2*x21 . ui. Var(ui) = (σi)* 2. Step 2: Auxilliary Regression to determine σi. Run the regression on the absolute value of the error term ut , the 'resid' of the primary regression ( ut = alpha1*x1i alpha2*x2i .). The fitted values of this regression is the estimate of σi. The resulting regression is BLUE.

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Outrun the Market: April 2011

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Friday, 22 April 2011. Qtstalker for Trend Trading - crashed. Been using Qtstalker for sometime now and I am happy with it's large number of technical indicators and charting options (as provided by TA-LIB). I had enhanced it for bulk downloading of quotes from Yahoo - so downloading of quotes for almost 3000 stocks takes less than 20 sec. Will see if Merchant of Venice's Gondola tool is of any use and post the details later. If you are willing to explore more, here is one list. Monday, 18 April 2011.

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Outrun the Market: June 2012

http://outrunthemarket.nsecentral.com/2012_06_01_archive.html

Monday, 18 June 2012. Heteroscedasticity - Applying correction. Procedure to apply correction for heteroskedasticity. Step 1 : Primary Regressiion. Run the primary regression on yi = const beta1*x1i beta2*x21 . ui. Var(ui) = (σi)* 2. Step 2: Auxilliary Regression to determine σi. Run the regression on the absolute value of the error term ut , the 'resid' of the primary regression ( ut = alpha1*x1i alpha2*x2i .). The fitted values of this regression is the estimate of σi. The resulting regression is BLUE.

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Outrun the Market: Testing for Heteroscedasticity

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Friday, 15 June 2012. Testing for Heteroscedasticity by conducting whites test:. Data file : http:/ goo.gl/ghtdr. Refer the comments in the code. Heteroscedasticity - White's Test. Cheryan M. T. Data file : macro.xls. Save the content as macro.csv file. Import numpy as np. Import statsmodels.api as sm. From scipy.stats import chi2. Macro = pandas.read csv("macro.csv"). Header from the panda data frame. Date 254 non-null values. Microsoft 254 non-null values. SANDP 254 non-null values. Dprod = np.diff...

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Outrun the Market: Historical Volatility

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Friday, 11 May 2012. Investopedia definition of volatility – “. A statistical measure of the dispersion of returns for a given security or market index. Volatility can either be measured by using the standard deviation or variance between returns from that same security or market index. Commonly higher the standard deviation, higher is the risk”. A python code sample given below:. Install Python and Numpy ( http:/ enthought.com/products/epd.php. For installation of python and associated packages). Let us...