Quantocracy

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Quantocracy’s Daily Wrap for 01/17/2021

This is a summary of links featured on Quantocracy on Sunday, 01/17/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Research impact of delta hedging with Python [Cuemacro]

    One of the things which I found confusing at first with options, is the fact that lots of the folks trading them, dont really have a view about whether the spot price will go up and down. They are basically trading the volatility parameter from options pricing models (Emanuel Derman explains the point about trading parameter much better than me here and also here). One of the most common

Quantocracy’s Daily Wrap for 01/16/2021

This is a summary of links featured on Quantocracy on Saturday, 01/16/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • More factors, more variance…explained [OSM]

    Risk factor models are at the core of quantitative investing. Weve been exploring their application within our portfolio series to see if we could create such a model to quantify risk better than using a simplistic volatility measure. That is, given our four portfolios (Satisfactory, Naive, Max Sharpe, and Max Return) can we identify a set of factors that explain each portfolios variance

Quantocracy’s Daily Wrap for 01/15/2021

This is a summary of links featured on Quantocracy on Friday, 01/15/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • How To Create A Fully Automated AI Based Trading System With Python (h/t @PyQuantNews)

    A couple of weeks ago I was casually chatting with a friend, masks on, social distance, the usual stuff. He was telling me how he was trying to, and I quote, detox from the broker app he was using. I asked him about the meaning of the word detox in this particular context, worrying that he might go broke, but nah: he told me that he was constantly trading. If a particular stock has been going
  • How to Get Historical Market Data Through Python Apis [Quant Insti]

    As a quant trader, you are always on the lookout to create and optimise your trading strategies. Backtesting forms a very important part of this process. And for backtesting, access to historical data is a necessity. But its a very daunting task to find decent historical price data for backtesting your trading strategies. While a simple google search can give you the end of day data for any
  • Research Review | 15 January 2021| Forecasting [Capital Spectator]

    Long-Term Stock Forecasting Magnus Pedersen (Hvass Laboratories) December 17, 2020 When plotting the relation between valuation ratios and long-term returns on individual stocks or entire stock-indices, we often see a particular pattern in the plot, where higher valuation ratios are strongly correlated with lower long-term stock-returns, and vice versa. Moreover the plots often show a particular

Quantocracy’s Daily Wrap for 01/13/2021

This is a summary of links featured on Quantocracy on Wednesday, 01/13/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Bayesian Portfolio Optimisation: Introducing the Black-Litterman Model [Hudson and Thames]

    The Black-Litterman (BL) model is one of the many successfully used portfolio allocation models out there. Developed by Fischer Black and Robert Litterman at Goldman Sachs, it combines Capital Asset Pricing Theory (CAPM) with Bayesian statistics and Markowitzs modern portfolio theory (Mean-Variance Optimisation) to produce efficient estimates of the portfolio weights. Before getting into the

Quantocracy’s Daily Wrap for 01/12/2021

This is a summary of links featured on Quantocracy on Tuesday, 01/12/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • The Definitive Study on Long-Term Factor Investing Returns [Alpha Architect]

    Interest in factor investing was hot several years back but seems to have died on the back of poor relative performance and a move to hotter products in thematics and ESG. But, for better or worse, we havent moved on. We are boring and we trust the process. We still believe that markets do a decent job at pricing risks and rewards, but they arent perfect. There is a bunch of noise caused by
  • How Does ETF Liquidity Affect ETF Returns, Volatility, and Tracking Error? [Alpha Architect]

    Although the ETF market has grown exponentially over the recent 20 years, ETFs that are less popular are not always liquid. A majority of the dollars flowing into ETFs are concentrated in 3 products, accounting for 46.7% of total ETF trading volume (see Figure 3 below). If the next 8 ETFs are included that percentage increases to 61.5%. If that doesnt astound the reader, consider that the AUM$

Quantocracy’s Daily Wrap for 01/11/2021

This is a summary of links featured on Quantocracy on Monday, 01/11/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Musings about Factor Exposure Analysis [Factor Research]

    There are few alternatives to regression analysis when explaining investment performance Too few as well as too many independent variables can be problematic The results are often not intuitive, but also encourage asking further questions that may prove insightful INTRODUCTION The older I become, the less I feel I know anything with certainty. Almost every aspect of life seems to have various

Quantocracy’s Daily Wrap for 01/10/2021

This is a summary of links featured on Quantocracy on Sunday, 01/10/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Recovering Accurate Implied Dividend and Interest Rate Term-Structures from Option Prices [Sitmo]

    In this post we discuss the algorithms we use to accurately recover implied dividend and interest rates from option markets. Implied dividends and interest rates show up in a wide variety of applications: to link future-, call-, and put-prices together in a consistent market view de-noise market (closing) prices of options and futures and stabilize PnLs of option books give tighter true bid-ask
  • Calculating FX total returns in Python [Cuemacro]

    If you want a train, you have to build a train track. It doesnt matter, if its a steam train or bullet train, or any other train. Its a prerequisite. No track kind of implies the train cant run. Obviously, each train needs a different type of track, but ultimately the principle is the same in how the track works (admittedly, if its a maglev train then perhaps not). When it comes to
  • Classifying market states [SR SV]

    Typically, we cannot predict a meaningful portion of daily or higher-frequency market returns. A more realistic approach is classifying the state of the market for a particular day or hour. A powerful tool for this purpose is artificial neural networks. This is a popular machine learning method that consists of layers of data-processing units, connections between them and the application of

Quantocracy’s Daily Wrap for 01/07/2021

This is a summary of links featured on Quantocracy on Thursday, 01/07/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Value and Momentum and Investment Anomalies [Alpha Architect]

    The predictive abilities of value and momentum strategies are among the strongest and most pervasive empirical findings in the asset pricing literature. (here is a deep dive) For example, the study Value and Momentum Everywhere by Clifford Asness, Tobias Moskowitz and Lasse Pedersen, published in the June 2013 issue of The Journal of Finance, examined these two factors across eight different

Quantocracy’s Daily Wrap for 01/06/2021

This is a summary of links featured on Quantocracy on Wednesday, 01/06/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Exporting Zorro Data to CSV [Robot Wealth]

    Earlier versions of Zorro used to ship with a script for converting market data in Zorro binary format to CSV. That script seems to have disappeared with the recent versions of Zorro, so I thought Id post it here. When you run this script by selecting it and pressing [Test] on the Zorro interface, you are asked to select a Zorro market data file to convert to CSV format. Zorro then does the

Quantocracy’s Daily Wrap for 01/05/2021

This is a summary of links featured on Quantocracy on Tuesday, 01/05/2021. To see our most recent links, visit the Quant Mashup. Read on readers!

  • Using maximum drawdowns to set capital sizing – not as bad as I first thought [Investment Idiocy]

    Risk. Love it or hate it, well as a trader you have to deal with it even though none of us really like it. No, we'd all prefer to be one of those mythical traders you hear about on youtube or instagram who consistently make $1000 a day, and never lose any money. Sadly I am not in that unicorn like category, and as only real people read this blog neither are you unless you are one of the HFT
  • Strategy Development Phase Intraday Strategies Using Price Patterns 7/12 [Trade With Science]

    It is very popular among traders to develop and trade intraday strategies. The reasons are simple. This type of strategy greatly limits the potential maximum drawdown by not exposing the strategy to the risk of night movements and weekend gaps. The rule that the less total time a strategy is in position, the lower you expose it to overall risk certainly applies. On the other hand, you are quite
  • Simple versus Advanced Systematic Trading Strategies – Which is Better? [Quant Start]

    An age-old question in the quant community asks whether systematic traders should stick with simple quant strategies or expend the effort to implement more advanced approaches. It is often the perception that retail algo traders solely utilise simpler strategies while quantitative hedge funds carry out highly sophisticated and mathematically complex approaches. Recently however the situation has
  • Hurst Exponent – finding the right market for your trading strategy [Philipp Kahler]

    The Hurst exponent is a measure for the behaviour of the market. It shows if the market behaves in a random, trending or mean-reversion manner. This can be used to select the right trading strategy for your market. Hurst Exponent hurst spx hurst exponent spx The hurst exponent describes the self similarity of a market. Self similarity describes how similar past market snippets are to current ones.