In the meantime, you can get insights as to optional parameters for each method, by using Python’s help method: help ( qs. ( view original html file) To view a complete list of available methods, run != '_' ] ['avg_loss', Output will generate something like this: Let’ create an html tearsheet ( benchmark can be a pandas Series or ticker ) qs. Qs.reports.html(.) - generates a complete report as html Qs.reports.full(.) - shows full metrics and plots Qs.reports.basic(.) - shows basic metrics and plots You can create 7 different report tearsheets: snapshot ( stock, title = 'Facebook Performance', show = True ) # can also be called via: # ot_snapshot(title='Facebook Performance', show=True)

sharpe ( stock ) # or using extend_pandas() :) stock. download_returns ( 'META' ) # show sharpe ratio qs. extend_pandas () # fetch the daily returns for a stock stock = qs. Here’s an example of a simple tear sheet analyzing a strategy: Quick Start % matplotlib inline import quantstats as qs # extend pandas functionality with metrics, etc. Quantstats.reports - for generating metrics reports, batch plotting, and creating tear sheets that can be saved as an HTML file. ots - for visualizing performance, drawdowns, rolling statistics, monthly returns, etc. Quantstats.stats - for calculating various performance metrics, like Sharpe ratio, Win rate, Volatility, etc. QuantStats Python library that performs portfolio profiling, allowing quants and portfolio managers to understand their performance better by providing them with in-depth analytics and risk metrics.Ĭhangelog » QuantStats is comprised of 3 main modules: Although the O(N^2) solution is, I think, an easier concept to grapple with, when it’s translated into code it just doesn’t grok.QuantStats: Portfolio analytics for quants The purist in me likes that fact that the O(N) solution looks like easier code to understand than the O(N^2) solution. If the next value is less than the maximum then we see if this difference is larger than any previously encountered difference, if it is then this is our new peak-to-trough. However, the second part of the ‘if’ is where the magic happens. We move through the prices and the first part of the ‘if’ will find the highest part of the peak so far.

This solution is a bit harder to explain. If (prices - prices) > (prices - prices):

# You can only determine the largest drawdown on a downward price! The challenge then is to find an O(N) solution to the problem and to save-those-much-needed-cycles for something really important: For small n this doesn’t really matter, but for any decently sized data-series this baby will be slow-as-molasses. That is for any data-series of size N the best and worst case will result in N * N-1 iterations, in shorthand this is O(N^2). The problem with this solution is that it has quadratic complexity. This is a natural way to solve the problem because it looks at all possible start points and assesses what the worst outcome would be. At the end you just return the two points you found. If this difference is also the largest seen in the data-set so far then make it the largest positive difference of all points. The deal is relatively straight-forward: compare the fist item against every item after it in the list and store the largest positive difference. I don’t know if that’s the proper term but it harks back to the days when I used to be a number-cruncher for some statisticians. It’s what I have come to know as a ‘between’ analysis. This led to the first solution (in Python): I believe what I really want is: the largest positive difference of high minus low where the low occurs after the high in time-order. Part of the problem is coming up with a consistent natural language of what you want your peak and trough to be. To do it reliably (and quickly) on a computer, is not that straight forward. Well, finding these two babys by eye is trivial. The upper one is the ‘peak’ and the lower one is the ‘trough’. Clearly past prices are not an indicator of future losses. This is the point of this analysis and is a way for investors in the asset to see how bad, ‘bad’ has really been in the past. You’ll see that there are two points on the graph (marked in red) where if you had invested at the first point and pulled out on the second point you would have the worst-case loss. Look at the following random graph of pretend asset prices: Sometimes investors want to be able to judge what the absolute worst case scenario would have been if they’d invested in something. Ok, so this is a little bit technical but it’s an intriguing puzzle that got me thinking quite hard.

Maximum drawdown python