Quant Mashup - Quant at Risk

Book Review: Python for Finance Cookbook, 2nd Ed. [Quant at Risk]

Thanks to the courtesy of Packt Publishing, I had the pleasure of receiving, reading, and studying the new release of Python for Finance Cookbook, the book by Eryk Lewinson. This is the second (and probably the last) edition, according to the author himself. Therefore, it must be solid and

*- 1 month ago, 24 Apr 2023, 10:28pm -*

Can ChatGPT Self-Improve Self-Written Python Code for Cholesky Decomposition? [Quant at Risk]

It is needless to say about next big thing in the field of artificial intelligence (AI) known as ChatGPT. ChatGPT is a large language model developed by OpenAI. It is based on the GPT (Generative Pre-training Transformer) architecture and is trained on a massive dataset of text data. This allows it

*- 4 months ago, 14 Jan 2023, 10:31am -*

Cryptocurrencies with Python: A new YouTube video series! [Quant at Risk]

We kicked off a new series of go-to solutions for #Cryptocurrencies with #Python. Subscribe to our YouTube channel for regular updates!

*- 5 months ago, 29 Dec 2022, 06:36pm -*

Vol-of-Vol for Crypto-Derivative Products [Quant at Risk]

In quantitative finance, the Volatility of Volatility (also referred to as Vol-of-Vol or VoV) is an important parameter for pricing various derivative products (e.g. Volatility Dispersion Swaps) and its correct estimation is frequently desired. VoV is usually a single number treated an an input

*- 6 months ago, 3 Dec 2022, 06:53pm -*

New Quant Podcast: So, you want to be a Quant? [Quant at Risk]

Here is the first episode in a new series of podcasts entitled Break into Finance. We will be talking about what it takes to launch your career in finance, what does it mean to become a quant, and where to start. Any questions welcomed!

*- 6 months ago, 16 Nov 2022, 09:17pm -*

Effective Allocation Measure with Entropy Application for Correlated Crypto Assets [Quant at Risk]

Surprisingly, in the literature there are only few effective formulae for asset allocation. They are based on the asset types and, in theory, they should define investors’ risk appetite. For instance, a large exposure in stocks should define aggressive investment style in comparison to investing

*- 10 months ago, 14 Jul 2022, 11:26pm -*

Top Quantitative Finance Blogs and Vlogs: Review 2022 [Quant at Risk]

The Internet is full of articles covering all kinds of aspects related to finance. Stocks, crypto, indexes have always been a hot topic and many are seeking new ideas in these areas. It is true that in a vast amount of the content one may get lost. There are tons of blogs with irrelevant, outdated,

*- 1 year ago, 7 May 2022, 11:00am -*

Hacking 1-Minute Crypto Candlesticks: (2) Custom Charts using Plotly [Quant At Risk]

A clear and informative time-series visualisation is often a challenge. Especially this is true when it comes to candlestick charts in Python. Searching the Web for a perfect solution may bring you to the Plotly package which stores in its arsenal the corresponding function allowing for easy

*- 1 year ago, 24 Apr 2022, 11:49am -*

Top N Crypto-Assets by MarketCap for Backtesting Purposes in Python [Quant at Risk]

A quantitative research over the construction of a perfect crypto-portfolio can be based on a number of crypto-assets. The selection of them is of paramount importance. If you are able to build the right portfolio, stick to it, or successfully manage its composition in time (e.g. through the method

*- 1 year ago, 27 Mar 2022, 08:42pm -*

Hacking 1-Minute Cryptocurrency Candlesticks: Capturing Binance Live Data [Quant at Risk]

There is no question about how profitable the trading of cryptocurrencies can be. If you create an algorithmic strategy and stick to it, you can capture a +10% PnL wave sometimes even twice a day for a selected asset. Unfortunately, the opposite is true, too! The crypto-risks seem to follow the same

*- 1 year ago, 22 Mar 2022, 11:30am -*

Quantitative Analysis of a Sample Drawn from the Unknown Continuous Population [Quant at Risk]

In quantitative finance, we very often deal with a sample mean and sample standard deviation being derived given a vector or a time-series or any other (1-dimensional) dataset. For many of us these calculations are so obvious that only a few understand the principles standing behind the scene.

*- 1 year ago, 13 Dec 2021, 09:37am -*

The Longest Winning Streak for Bitcoin [Quant at Risk]

In the previous article Estimating Probability of Bitcoin Pullback in its Bullish Market we touched an interesting point worth exploring a bit further. Namely, the probability of Bitcoin close-price closing each day higher than a day ago days in a row. We had seen that in July 2021 Bitcoin moved and

*- 1 year ago, 4 Nov 2021, 10:11pm -*

Czekanowski Index-Based Similarity as Alternative Correlation Measure [Quant at Risk]

In quantitative finance we are used to measuring direct linear correlations or non-linear cross-bicorrelations among various time-series. For the former, by default, one adopts the calculation of Pearson product-moment correlation coefficients to quantify a linear relationship between two vectors.

*- 1 year ago, 27 Oct 2021, 10:33am -*

Break into Finance: New Podcast from Quant at Risk [Quant at Risk]

Let me kick off the series of QuantAtRisk’s podcasts Break into Finance. I address it to all of you who wish to join the financial industry but have no clue how to do it as well as to those of you who would like to make a change, improve your career, get better, and succeed within the industry.

*- 1 year ago, 20 Oct 2021, 10:12am -*

Honest Guide to Getting a Quant Job in Finance: (1) So, you want to be a Quant?! [Quant at Risk]

They say that a journey of thousand miles commences with a single step. So, here you are, firm in your own resolutions or hesitating where to go. Graduated from a university or standing and trembling about next move in your life. Fired from one job or looking for another opportunity to seize.

*- 1 year ago, 12 Jun 2021, 04:29am -*

Where You Can Trade Cryptocurrencies using Fiat Currencies? [Quant at Risk]

With a myriad of new crypto-exchanges popping up every quarter, lots of newcomers to this fields can be overwhelmed by their number. Big names can quickly stand out if you filter the list according to daily trading volume or the total number of cryptocurrencies available for trading. Some offer

*- 2 years ago, 12 Apr 2021, 09:04pm -*

Modelling Slippage for Limit Orders using Adaptive KDE-based Loss Severity Distribution [Quant at Risk]

Placing limit orders for trade execution is both quite popular and handy method in (algo)trading. A trader expects that the executed price of his buy/sell trade will ideally match the one requested in his limit order. Unfortunately, depending on a momentary market/asset liquidity, the difference

*- 2 years ago, 22 Mar 2021, 09:34pm -*

Introduction to Sell-Off Analysis for Crypto-Assets: Triggered by Bitcoin? [Quant at Risk]

They say that small fishes buy and sell driven by unstable waters but only big whales make the waves really huge. Recently, this quite popular phrase, makes sense when it comes to cryptocurrency trading influenced by sudden dives of the Bitcoin price. The strategies of buying and selling executed by

*- 2 years ago, 15 Mar 2021, 11:08pm -*

Probing Price Momentum of Bitcoin during its Bull Runs with a Piecewise Linear Model [Quant At Risk]

In 2020 Bitcoin delivered us another spectacular bull run. It was as impressive as the one we witnessed in 2017. The analysis of Bitcoin price time-series during its bull runs can uncover interesting results. By comparing a selected set of characteristics we could find some commonalities in trading.

*- 2 years ago, 31 Jan 2021, 08:04pm -*

Does It Make Sense to Use 1-Hour 1% VaR and ES for Bitcoin? [Quant at Risk]

Another day, another record. Today, at 17:35 GST+1, Bitcoin crossed U$33,000 in trading at Coinbase Pro exchange and did not fall sharply down. It took about 4.5 hours to accelerate from a psychological level of U$30k with more greed among investors rather than fear of bursting Bitcoin (second)

*- 2 years ago, 2 Jan 2021, 08:43pm -*

Scanning Crypto Exchange for Available Cryptocurrency Close Price-Series [Quant at Risk]

One of the most common problem encountered by all novice researches of the crypto-markets and (algo-)traders is knowing a list of all cryptocurrency pairs being actively traded at specific crypto exchange. This knowledge is a gateway to a vast research over correlations of crypto-assets, looking for

*- 2 years ago, 1 Nov 2020, 07:56pm -*

Tracking Bitcoin Gains since its 3rd Halving in May 2020 using Python [Quant at Risk]

The Bitcoin’s 3rd halving was the most anticipated event this year. This a moment when a reward for all Bitcoin block miners is cut by half. It happens every 4 years or every 210,000 blocks on the Bitcoin blockchain. The previous two halving events took place in 2012 and 2016, respectively. Before

*- 3 years ago, 15 May 2020, 09:38am -*

How to Design Intraday Algo-Trading Model for Cryptocurrencies using Bitcoin-based Signals? [Quant at Risk]

With a growing popularity of cryptocurrencies and their increasing year-over-year traded volumes, crypto algo-trading is a next big thing! If you study this market closely you will notice that it offers quick gains in much shorter unit of time comparing to stocks or FX. No wonder why a participation

*- 3 years ago, 28 Apr 2020, 10:47am -*

Brent Oil Price Time-Series in Python with 1-Minute Data Sampling [Quant at Risk]

Recent actions in WTI Futures pricing on Apr 20, 2020 caused my curiosity to have a deep look at intraday crude oil price time-series. With no surprise, I couldn’t find any free and effortlessly available dataset on the Internet. This is a common problem for lots of quants and data analysts:

*- 3 years ago, 23 Apr 2020, 10:35am -*

Inverting Differentiated Time-Series in pandas for Deep Learning Prediction Analysis [Quant at Risk]

A differentiation of the time-series is a common transformation used when we want to get a stationary time-series given a non-stationary one. The latter usually displays time-dependent relationships like trends, seasonality, quasi-cyclic patterns, and their Fourier power spectrum is characterised by

*- 3 years ago, 14 Apr 2020, 12:50pm -*

How to Predict Bitcoin Price with Deep Learning LSTM Network - Part 1 [Quant at Risk]

You can’t predict the future unless you have a crystal ball but you can predict an asset’s trading price in next time step if you have a right tool and enough confidence in your model. With the development of a new class of forecasting models employing Deep Learning neural networks, we gained

*- 3 years ago, 2 Apr 2020, 12:35pm -*

pandas for Quants: New Video Course from QaR [Quant at Risk]

Hi Guys! I’m happy to kick off a new series of free video lectures devoted to Python’s library of pandas. Every week, I will be uploading something between 2 to 4 new videos especially crafted around practicalities of pandas library applied to financial data and their analysis and processing.

*- 3 years ago, 28 Feb 2020, 05:26am -*

Deep Learning for Quants: (1) Setting Up Keras and TensorFlow 2.1+ Environment in Python [Quant at Risk]

It would be too easy to kick off the series of lectures supplementing my Python for Quants ebooks starting from Machine Learning (ML) as an innovation. ML-based algorithms pay dividends when your problem is fairly well defined and data allow to capture the patterns where they exist. Deep Learning

*- 3 years ago, 9 Feb 2020, 01:25pm -*

Employing Human-Order in pandas DataFrame Sorting: Risk Factors and Tenors [Quant At Risk]

There are various Python projects which require sorting but not the ones that employ a default alphanumeric functionality. We talk about manually specified order or human-order, in short. One of such examples is the case study presented below. Imagine that your risk system provides you with a list

*- 3 years ago, 2 Dec 2019, 07:24pm -*

Shapley Value Allocation of Operational Risk Capital Charges using Airport Problem Solution [Quant At Risk]

In Financial Risk Management the most challenging part for quantitative modeling is, beyond any doubt, the Operational Risk (Ops Risk). It is defined as the risk of loss resulting from inadequate or failed internal processes, people and systems or from external events. This definition includes legal

*- 5 years ago, 3 Jun 2018, 01:50am -*

Logistic Regression Analysis of Quant’s Resume during His Job Interview [Quant At Risk]

There are not too many creative opportunities to leave a person applying for a quant role dumbfounded with the interview question directly related to his CV. First of all, we want to test his quant skillset, check his ability to read a code, analyse the model, listen to his judgement on the

*- 5 years ago, 17 Apr 2018, 12:57pm -*

Cryptocurrencies with Python eBook: Apr 15 [Quant At Risk]

It is my pleasure to deliver a long-awaited QaR ebook on the introduction to blockchain and cryptocurrencies with Python on April 15, 2018. In the book we will cover inter alia the fundamental aspects of blockchain in general; the craze around cryptocoins like Bitcoin, Ether, LiteCoin, etc.; their

*- 5 years ago, 6 Mar 2018, 05:23am -*

Recalibrating Expected Shortfall to Match Value-at-Risk for Discrete Distributions [Quant at Risk]

By considering the same risk measure, ϱ, applied to two or more portfolios (credit loss distributions, profit-and-loss distributions, etc.) one desires to have a subadditivity property in place: ϱ(X1+X2)≤ϱ(X1)+ϱ(X2) i.e. meaning that two combined portfolios should never be more risky than the

*- 5 years ago, 19 Nov 2017, 11:29am -*

Earning Money in Cryptocurrency Markets by Spotting Statistical Arbitrage Opportunities [Quant at Risk]

When you come in contact with cryptocurrencies, e.g. Bitcoin (BTC), you quickly realise that there is no single price of BTC at any given moment. The reason is that Bitcoin is traded on different markets. It can be worth more on Coinbase exchange and less on Kraken exchange. In particular, the

*- 5 years ago, 7 Nov 2017, 08:00am -*

N-CryptoAsset Portfolios: Identifying Highly Correlated Cryptocurrencies using PCA [Quant at Risk]

IMHO, there is nothing more exciting these days than researching, analysing, and a good understanding of cryptocurrencies. Powered by blockchain technology, we live in a new world that moves fast forward as we sleep. In my first post devoted to that new class of tradable assets we have learnt how to

*- 6 years ago, 1 Apr 2017, 10:25am -*

Cryptocurrency Time-Series for N-CryptoAsset Portfolio Analysis in Python [Quant at Risk]

Welcome to a brand new era of “financial assets” – the crypto-assets. The impossible became possible. Yes, now you can trade cryptocurrencies: money that have been created in a virtual world with a physical impact onto our everyday cash-in-the-bank reality. The grande picture is still

*- 6 years ago, 21 Mar 2017, 02:26am -*

Python for Algo and Crypto-Currency Trading: 2-Day Workshop in London (July 8-9) [Quant at Risk]

Within our unique 2-Day Intensive Workshop in London, UK on Python for Algorithmic and Crypto-Currency Trading we dive into most recent and hot topics in algo-trading. We will cover and analyse a well explored world of classical assets (stocks, FX currencies) extended by trading techniques aimed at

*- 6 years ago, 10 Mar 2017, 03:30am -*

Machine Learning in Python for Finance: 2-Day Workshop in Warsaw, Poland [Quant at Risk]

After wonderful and rewarding 2-day workshop devoted to Python for Algo-Trading on March 4-5, it is my pleasure to announce a new, upcoming, on demand 2-Day Workshop on Machine Learning in Python for Finance (May 20-21, 2017). Since Machine Learning is the latest hottest topic covering different

*- 6 years ago, 8 Mar 2017, 01:30am -*

Hacking True Random Numbers in Python: Blockchain Miners [Quant at Risk]

The magnitude and importance of random numbers in finance does not have to be explained. We need them. Either it is an option pricing or a Monte Carlo simulation, random numbers are with us. However, we make a trade-off: the speed in their generation versus uniqueness. That is why a widely accepted

*- 6 years ago, 12 Dec 2016, 04:30pm -*

Conditional Value-at-Risk in the Normal and Student t Linear VaR Model [Quant at Risk]

Conditional Value-at-Risk (CVaR), also referred to as the Expected Shortfall (ES) or the Expected Tail Loss (ETL), has an interpretation of the expected loss (in present value terms) given that the loss exceeds the VaR (e.g. Alexander 2008). For many risk analysts, CVaR makes more sense: if VaR is a

*- 6 years ago, 8 Dec 2016, 07:35am -*

Non-Linear Cross-Bicorrelations between Oil Prices and Stock Fundamentals [Quant at Risk]

When we talk about correlations in finance, by default, we assume linear relationships between two time-series “co-moving”. In other words, if one time-series changes its values over a give time period, we seek for a tight correlation reflected within the other time-series. If found, we say they

*- 6 years ago, 1 Dec 2016, 08:07am -*

Financial Time-Series Segmentation Based On Turning Points in Python [Quant at Risk]

A determination of peaks and troughs for any financial time-series seems to be always in high demand, especially in algorithmic trading. A number of numerical methods can be found in the literature. The main problem exists when a smart differentiation between a local trend and “global” sentiment

*- 6 years ago, 3 Nov 2016, 10:19am -*

Computation of the Loss Distribution not only for Operational Risk Managers [Quant at Risk]

In the Operational Risk Management, given a number/type of risks or/and business line combinations, the quest is all about providing the risk management board with an estimation of the losses the bank (or any other financial institution, hedge-fund, etc.) can suffer from. If you think for a second,

*- 6 years ago, 5 Jun 2016, 02:01am -*

Probability of Black Swan Events at NYSE [Quant at Risk]

The prediction of extreme rare events (EREs) in the financial markets remains one of the toughest problems. Firstly because of a very limited knowledge we have on their distribution and underlying correlations across the markets. Literally, we walk in dark, hoping it won’t happen today, not to the

*- 7 years ago, 18 Apr 2016, 05:32am -*

Detecting Human Fear in Electronic Trading: Emotional Quantum Entanglement [Quant at Risk]

This post presents an appealing proof for the progressing domination of algorithmic trading over human trading. By analysing the US stock market between 1960 and 1990, we estimate a human engagement (human factor) in live trading decisions taken after 2000. We find a clear distinction between

*- 7 years ago, 5 Apr 2016, 03:00pm -*

Python in Singapore: Intensive Workshop (Apr 7, 2016) [Quant at Risk]

About this Course Our Python 1-day intensive course is addressed to all who wishes start programming in Python language straight away! We will cover the fundamentals of Python (2.7, 3.5), numerical aspects of coding, and over 100 individually crafted examples covering various applications coming

*- 7 years ago, 27 Feb 2016, 09:10am -*

Python in Sydney: Course+Workshop Wednesday, March 16, 2016 [Quant at Risk]

Python in Sydney: Course+Workshop Wednesday, March 16, 2016

*- 7 years ago, 22 Feb 2016, 10:09am -*

Quant Hunt: Ignore Tick-Box Companies [Quant at Risk]

I was really surprised by a huge popularity of the past section of QuantAtRisk entitled Motivation for Quants. My readers made me thinking. Again. If there is a need for posts that expose and discuss the naked truth about quant job space, let’s make it, again! This time bigger, better, and with

*- 7 years ago, 7 Feb 2016, 01:32am -*

Supercomputing Frontiers 2016 [Quant at Risk]

Hi Guys, please find the information about the upcoming event in Singapore where I also submitted my proposal to host and conduct a full-day workshop on Frontiers of Python for Finance. I hope to see You there! -Pawel SCF2016-logo_final_retina2 You are cordially invited to contribute as an author or

*- 7 years ago, 22 Jan 2016, 03:41am -*

Predicting Heavy and Extreme Losses in Real-Time for Portfolio Holders (2) [Quant at Risk]

This part is awesome. Trust me! Previously, in Part 1, we examined two independent methods in order to estimate the probability of a very rare event (heavy or extreme loss) that an asset could experience on the next day. The first one was based on the computation of the tail probability, i.e.: given

*- 7 years ago, 6 Dec 2015, 02:18am -*