I need to see real growth in metrics like customer acquisition and trading volume before making a deeper commitment. From what I can tell, the news about EDXM will only be positive for Coinbase if it helps to expand the pie for the crypto industry as a whole. That's right -- they think these 10 stocks are even better buys. Independent nature of EDXM would also restrain the firm from the possibility of conflicts of interest. EDXM needed to prove its utility to stay relevant within the crypto space though. For now, I'm taking a wait-and-see **backed crypto exchange** with Coinbase. Meanwhile, the EDX exchange would work to accommodate both private and institutional investors.

While a VAR model potentially has the ability to model long memory and even GARCH effects, it is unable to produce stock prices that are guaranteed to be consistent, in the sense defined above. Another approach favored by some researchers is to stitch together sub-samples of the real data series in a varying time-order. This is applicable only to return series and, in any case, can introduce spurious autocorrelations, or overlook important dependencies in the data series.

Besides these defects, it is challenging to produce a synthetic series that looks substantially different from the original — both the real and synthetic series exhibit common peaks and troughs, even if they occur in different places in each series. Deep Learning Generative Adversarial Networks In a previous post I looked in some detail at TimeGAN, one of the more recent methods for producing synthetic data series introduced in a paper in by Yoon, et al link here.

Generating Synthetic Market Data TimeGAN, which applies deep learning Generative Adversarial Networks to create synthetic data series, appears to work quite well for certain types of time series. But in my research I found it be inadequate for the purpose of producing synthetic stock data, for three reasons: i The model produces synthetic data of fixed window lengths and stitching these together to form a single series can be problematic.

For both TimeGAN and DoppleGANger, the researchers have tended to benchmark performance using classical data science metrics such as TSNE plots rather than the more prosaic consistency checks that a market data specialist would be interested in, while the more advanced requirements such as long memory and GARCH effects are passed by without a mention.

The conclusion is that current methods fail to provide an adequate means of generating synthetic price series for financial assets that are consistent and sufficiently representative to be practically useful. Important if we are looking to mass-produce synthetic series for a large number of assets, for a variety of different applications. Some deep learning methods would struggle to meet this requirement, even supposing that transfer learning is possible.

In some case we want synthetic price series that are highly correlated to the original; in other cases we might want to test our investment portfolio or risk control systems under extreme conditions never before seen in the market. After researching the problem over the course of many years, I have at last succeeded in developing an algorithm that meets these requirements.

Before delving into the mechanics, let me begin by illustrating its application. Synthetic Price Series Generating ten synthetic series using the algorithm takes around 2 seconds with parallelization. I chose to generate series of the same length as the original, although I could just as easily have produced shorter, or longer sequences. The first task is to confirm that the synthetic data are internally consistent, and indeed is guaranteed to be so because of the way the algorithm is designed.

For example, here are the first few daily bars from the first synthetic series: This means, of course, that we can immediately plot the synthetic series in a candlestick chart, just as we did with the real data series, above. While the real and synthetic series are clearly different, the pattern of peaks and troughs somehow looks recognizably familiar.

Obviously this is a much more bullish scenario that we have seen in reality. Here, too, we see several very large drawdowns, especially in the period from , but there is also a general upward drift in the process that enables the Index to reach levels comparable to those achieved by the real series: Price Correlations Reflecting these very different price path evolutions, we observe large variation in the correlations between the real and synthetic price series.

For example: As these tables indicate, the algorithm is capable of producing replica series that either mimic the original, real price series very closely, or which show completely different behavior, as in the second example.

Dimensionality Reduction For completeness, as have previous researchers, we apply t-SNE dimensionality reduction and plot the two-factor weightings for both real yellow and synthetic data blue. We observe that while there is considerable overlap in reduced dimensional space, it is not as pronounced as for the synthetic data produced by TimeGAN, for instance.

However, as previously explained, we are less concerned by this than we are about the tests previously described, which in our view provide a more appropriate analysis benchmark, so far as market data is concerned. Furthermore, for the reasons previously given, we want synthetic market data that in some cases tracks well beyond the range seen in historical price series.

Returns Distributions Moving on, we next consider the characteristics of the returns in the synthetic series in comparison to the real data series, where returns are measured as the differences in the Log-Close prices, in the usual way. A more detailed look at the distribution characteristics for the first four synthetic series indicates that there is a very good match to the real returns process in each case the results for other series are very similar : We observe that the minimum and maximum returns of the synthetic series sometimes exceed those of the real series, which can be a useful characteristic for risk management applications.

The median and mean of the real and synthetic series are broadly similar, sometimes higher, in other cases lower. Only for the standard deviation of returns do we observe a systematic pattern, in which returns volatility in the synthetic series is consistently higher than in the real series. This feature, I would argue, is both appropriate and useful. Standard deviations should generally be higher, because there is indeed greater uncertainty about the prices and returns in artificially generated synthetic data, compared to the real series.

Moreover, this characteristic is useful, because it will impose a greater stress-test burden on risk management systems compared to simply drawing from the distribution of real returns using Monte Carlo simulation. Put simply, there will be a greater number of more extreme tail events in scenarios using synthetic data, and this will cause risk control parameters to be set more conservatively than they otherwise might.

This same characteristic — the greater variation in prices and returns — will also pose a tougher challenge for AI systems that attempt to create trading strategies using genetic programming, meaning that any such strategies are more likely to perform robustly in a live trading environment.

I will be returning to this issue in a follow-up post. Returns Process Characteristics In the following plot we take a look at the autocorrelations in the returns process for a typical synthetic series. These compare closely with the autocorrelations in the real returns series up to 50 lags, which means that any long memory effects are likely to be conserved.

Finally, when we come to consider the autocorrelations in the square of the returns, we observe slowly decaying coefficients over long lags — evidence of so-called GARCH effects — for both real and synthetic series: Summary Overall, we observe that the algorithm is capable of generating consistent stock price series that correlate highly with the real price series.

Also, some high frequency trading firms which have practically no losing days over many years are of course fully automated. I know you have been trading equities and futures but that you are now quite involved in Forex. Can you tell-us why? Are you trading spot? We trade only spot Forex. There are few fundamental strategies that are applicable. This plays to our strength. Also, FX market is more liquid than either equities or futures markets, which we find conducive to automated executions.

On top of all that, it has the lowest transaction costs. What are your thoughts about Forex microstructure? At Alpha Novae, we have a strong opinion against Last Look and lack of transparency on Forex market…What do you think about it? Does it impact your algorithmic trading? If yes, how? We currently only trade in FX markets that do not have last look. I agree with you on this: last look and the fragmented nature of FX markets are exploitative of buy-side traders.

We had spent many months developing a strategy that backtested very well but fell apart in live trading due to last look, to our great frustration. Henceforth, we avoided those markets. Do you consider optimal execution important for your trading? If you do, how do you measure execution quality? We measure our executions against a walk-forward test and see whether slippage is significant. Can you tell-us a bit about your live trading infrastructure? Did you build-it in house?

What are the most important part for you? My partner Roger is an experienced software entrepreneur. His products written in C are sophisticated and well-polished. The main advantage of building in-house is of course that we know exactly the internal logic and can change it in any way we like.

There is a lot to read here. NOPE - Net options pricing effect - is a normalized measure of the net delta imbalance between the put and call options of a traded instrument across its entire option chain, calculated at the market close for contracts of all maturities. The imbalance estimates the amount of delta hedging by market markers needed to keep their positions delta-neutral.

This hedging causes price movement in the underlying, which NOPE should ideally capture. The data for this has been sourced from Delta Neutral. It was calculated as the traded volume of the constituents of the index. This indicator comes from the dual momentum strategies of Vigilant and Defensive Asset allocation. The canary value can be either 0,1 or 2. This indicates what proportion of the asset portfolio was allocated to global risky assets equity, bond and commodity ETFs and what proportion was allocated to cash.

We calculate carry for 1 global equities - calculated as a ratio of expected dividend and daily close prices; 2 SPX futures - calculated from price of front month SPX futures contract and spot price of the index; and3 Currency - calculated from the two nearest months futures data.

Macro factors - macro factors are derived from global macroeconomic data, from the US and 12 other major economies. All these features are daily percentage changes, to make them stationary. More can be explored in the paper, Equity tail risk in the treasury bond market. This is sourced from the FRED.

The methodology for the term structure model used to calculate term premia is covered in the paper, Three-Factor Nominal Term Structure Model. All the term premia features are daily percentage changes, to make them stationary. Read Gross domestic product - GDP adjusted for inflation. Only the trend is extracted to get a seasonally adjusted signal. E-mail Books Dr. If you want to be a competitive swimmer, you need to learn the fundamentals of swimming first. Trading is no different; Ernie makes the fundamentals as simple as possible, but no simpler as Einstein would say and strikes the perfect balance between intuition and technical depth.

Those specifically interested in trading, and anyone generically interested in understanding how modern financial markets work, will benefit from reading the Second Edition of Quantitative Trading. Ernest Chan does all traders, current and prospective, a real service by succinctly outlining the tremendous benefits, but also some of the pitfalls, in utilizing many of the recently implemented quantitative trading techniques. This holds especially true for fields like quantitative trading, which are shrouded in mystery and protected by impenetrable jargon.

Readers of this book will not only learn the foundations of research and strategy development, but also gain pragmatic insight into the operational sides of the business. Chan has written the ideal guide for those looking to go from zero-to-one in their quantitative trading journey.

Readers of Quantitative Trading can find the password to the Matlab, Python, and R codes associated with this book and other premium content in the last paragraph of page of page 38 at the end of Example 3. It delves into the reasons certain markets display either mean reversion or momentum, and describes the common techniques that can exploit these profit opportunities.

The imbalance estimates the amount of delta hedging by market markers needed to keep their positions delta-neutral. This hedging causes price movement in the underlying, which NOPE should ideally capture. The data for this has been sourced from Delta Neutral.

It was calculated as the traded volume of the constituents of the index. This indicator comes from the dual momentum strategies of Vigilant and Defensive Asset allocation. The canary value can be either 0,1 or 2. This indicates what proportion of the asset portfolio was allocated to global risky assets equity, bond and commodity ETFs and what proportion was allocated to cash. We calculate carry for 1 global equities - calculated as a ratio of expected dividend and daily close prices; 2 SPX futures - calculated from price of front month SPX futures contract and spot price of the index; and3 Currency - calculated from the two nearest months futures data.

Macro factors - macro factors are derived from global macroeconomic data, from the US and 12 other major economies. All these features are daily percentage changes, to make them stationary. More can be explored in the paper, Equity tail risk in the treasury bond market. This is sourced from the FRED. The methodology for the term structure model used to calculate term premia is covered in the paper, Three-Factor Nominal Term Structure Model.

All the term premia features are daily percentage changes, to make them stationary. Read Gross domestic product - GDP adjusted for inflation. Only the trend is extracted to get a seasonally adjusted signal. After seasonal adjustment, we calculate the month-on-month and year-on-year change. This is a leading indicator and can inform us about movement in indicators like Gross domestic product in the future. What sets this book apart from many others in the space is the emphasis on real examples as opposed to just theory.

Concepts are not only described, they are brought to life with actual trading strategies, which give the reader insight into how and why each strategy was developed, how it was implemented, and even how it was coded. This book is a valuable resource for anyone looking to create their own systematic trading strategies and those involved in manager selection, where the knowledge contained in this book will lead to a more informed and nuanced conversation with managers.

Readers will find most of the materials quite accessible to anyone who has some experience in a quantitative field. This book can be treated as a continuation of my first two books, with coverage on topics that I have not discussed before, but it can also be read independently. Software codes for all the described strategies can be found on epchan.

The userid and password can be found in Box 1. It is far more difficult to make complex ideas seem simple. In this book, Ernie has done exactly that. Available for order now at Amazon.

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