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16

This is practically a textbook case begging for the Kelly criterion. In your specific example, the optimal trade size is $f^*A$, where $f^*$ maximizes the average rate of return $$\mathbb{E}[\log (X)]=0.5\log(1+0.3f)+0.5\log(1-0.23f).$$ Here $f$ is the fraction of the current capital to trade. A straightforward calculation yields that $$f^*=\frac{0.3-0.23}{...


8

The defining characteristic of "high-frequency" is not the number of trades, but instead it is the number of orders you place, and in particular how often you are changing those orders. The scratch rate (cancel/fill ratio) is often very high. For every 1,000 orders you place, you might get 5 fills. This is the single most defining criteria of whether someone ...


8

"There is no secret sauce!" - Inside the Black Box: The Simple Truth About Quantitative Trading, by Rishi K.Narang In this book, which is well worth reading to get a good conceptual overview of the different components of a quant trading system, the author tells about "one of the most successful" quant funds hiring only the best academic researchers and ...


8

So those are cumulative pnl figures and you are interested in the percent changes in pnl from one data point to the next? Don't use log returns, simply generate the percent changes through r(t)/r(t-1)-1. 4.3922/5.2735-1 = -16.71% (in your example time series I made the assumption that the time series is in ascending order. Given your description of the ...


8

Put simply, VIX is a spot index (fair value to a variance swap on SPX of constant maturity) that you cannot own as a security. Market participants create futures for you to trade. Futures trade higher than the VIX -- if you long VIX futures, you lose when the futures contract converges to VIX. You therefore have a negative roll-down. VIX ETF doesn't avoid ...


7

I don't think that it is a real applicable trading system but it is more general work concerning the connection between chaos and financial markets. A good starting point is this (relatively recent) article: http://deepeco.ucsd.edu/~george/publications/08_ecology_bankers.pdf You can find his publications here: http://sio.ucsd.edu/Profile/gsugihara#pubs


7

There is an extensive discussion of what is publicly known in Paul Wilmott's new book (which is a very enlightening and enjoyable read, btw): Wilmott, P., Orrell, D.: The Money Formula: Dodgy Finance, Pseudo Science, and How Mathematicians Took Over the Markets, Wiley, 2017. On pages 125 - 131 (chapter 6: What Quants do) they describe Simons' way from ...


6

You can point out to your friend that, statistically speaking, having more observations reduces uncertainty in estimators. Mathematically, $SE_\bar{x}\ = \frac{s}{\sqrt{n}}$, showing that the standard error of a statistical estimator decreases with increased observations. This argument is concise and consistent with the Taleb quote. From wikipedia on ...


6

Given their choice in hiring mainly academics from the fields of NLP and cryptography(at least in their early days), my guess is that they have been using something derived from information theory and/or hidden markov models.


6

The best answer to your question: back test your ideas against historical data. If you think you can predict the market by learning past patterns prove it by testing it, not by discussion. I've done mistake few years ago and fell in love with one idea, which seemed to be like money printing machine, but instead testing it, I spent month discussing it on ...


6

J. Welles Wilder Jr created the indicator called the Relative Strength Indicator in 1967. The indicator he originally created uses all data points in the sample series, not just the last 14 data points (or whatever period of RSI you are using). Any data series that has less (or more) data than your current set will therefore show a different set of data. ...


5

"Major exchanges now have “latencies” of around one millisecond (one thousandth of a second) or less. Exchanges and practitioners now routinely time stamp their messages to the millisecond." This is so fast that effects from special relativity come into play! "Rules that attempt to force uniform prices at one moment in time over geographically ...


5

This may be a tricky question and I am curious to see whether there is indeed a statistical methodology that tries to answer this question. To my experience it really varies with the data you are working with. For instance, one may choose a rolling window above an expanding window when there are structural breaks in the data, hence which can affect the ...


5

To price financial instruments such as options, bonds and stocks must be priced so as to be "arbitrage free". The concept of arbitrage can be made precise by one of the fundamental ideas of quantitative finance, the so called Arbitrage Theorem. Put differently the Arbitrage Theorem provides a very elegant and general method for pricing derivative ...


5

**intended as a comment but not enough points to comment yet Order execution optimization: how to execute changes to your portfolio without suffering (too much) from implementation shortfall. Work of Almgren and Chris set a modern foundation of this space, and on top of that work of Jim Gatheral for closed form solution. In addition, consider if you're ...


5

If you have no edge, then you would indeed expect twice as many losing trades as winning trades, so you would net out to zero return on average (negative after commissions and trading frictions). This is a special case of the mathematical result known as the gambler’s ruin problem. If markets are not random, but exhibit either short-term momentum or short-...


5

Why Good forecasting != Good trading? I am not yet familiar with the F1 score the author compares with the Sharpe ratio. But the article rightly points out at least two grounds on which good forecasting does not imply good trading. The first issue has to do with the operative minutiae of trading. Models typically exclude aspects such as commissions, ...


5

A self-financing strategy needs to be previsible (aka predictable) since at time $t$, you need to decide (with the information from $\mathcal{F}_t$) how much you want to be invested in the different assets at time $t+1$. So, you need to decide in advance which makes the trading strategy predictable. Of course, the asset prices (and hence the value process ...


4

Optimization is definitely important in Quantitative Finance, especially for portfolio optimization where we maximize utility of the return of a portfolio as linear weighted vector of asset returns subject to a desired risk level: $$ \max_{w\in[0,1]^n} U(\mu_p(w),\sigma_p(w))\quad s.t. \sum_{i=1}^n w_i=1$$ where $w$ being the portfolio weights, and $U$ ...


4

The map is not the territory, any model is an abstraction and will never be complete, the only complete model of the market is the market itself, and so on. I agree that this leads directly into Gödel, Turing, the halting problem, and other basic computability concepts. Try this thought experiment: Imagine the market as a turing machine named M, reading ...


4

For me, I would calculate daily returns for such a series by backing out the daily PnL and dividing by some volatility number. lets define your cumsum as "c_pnl": daily_pnl = c_pnl - [0; c_pnl(1:length(c_pnl-1)] max_draw = max(cummax(c_pnl) - c_pnl) pct_returns = daily_pnl / max_draw # in terms of drawdown Don't you have capital already in the ...


4

Draw a picture. For each scenario, there are obvious circumstances that the payoff for each would be better. For the N day option, the payoff would be better if there was a slow gradual decline in price and a slow gradual increase over the same period, such that the final difference in the price of the underlying was largely unchanged. For multiple options ...


4

I've read this question and the other question you asked and I hope I can help. The important thing to realize that in any market multiple market makers operate and they are all trying to optimize their risk adjusted return. A market maker earns a return buying low and selling high. Suppose you are the only market maker and you quote this spread: 1 | Bid ...


3

I live very close to their office on Long Island and went to Stony Brook University, where they hire from at times - and the only few couple of people I know that got hired there were pure genius. I really doubt they are a ponzi scheme! I drive by their gates every now and then, definitely secretive but totally legit in my books.


3

In this case it is important to differentiate between a liability-driven investment strategy (LDI) and a (the classical) benchmark-driven investment strategy. The first one is what you need in this case. LDI was first established by Martin Leibowitz in 1986 ("Liability returns: A new perspective on asset allocation"). So googling that might help you already....


3

This really isn't worth the bounty, but it's too long for a comment. Quoting https://www.tradeking.com/education/options/option-greeks-explained#theta At-the-money options move at the square root of time. This means if a one-month ATM option is trading for \$1, then a two-month ATM option would be trading for 1 x sqrt of 2 or \$1.41. A three-month ...


3

Recently I came across interesting platform. https://www.quantopian.com/ they offer exactly what you need and for free. Basically, you code your algo in python, they provide data using api and backtesting. Hope it helps.


3

Two papers by AQR might be of use: Asvanunt, A. and S. Richardson (2016), “The Credit Risk Premium”: Despite theoretical and intuitive reasons for a credit risk premium, past research has found little supporting empirical evidence. This is primarily due to biases in computing credit excess returns which improperly account for term risk. Using data ...


3

Firstly, remember that in general vega is positive for all options. Hence, the fact that the implied volatility is higher in the wings (high and low strikes, i.e. deep ITM/OTM) means that these options are over-priced. Thus, you would want to sell/short these options. By the same logic, you would want to buy/long options around the ATM point where implied ...


3

Look to see if the "premium" of the risk/strategy has diminished. In your example of selling volatility, the strategy would be to sell "implied volatility" higher than "realized volatility". If the premium does not compensate investors for the costs (actual and opportunity) and risks of the strategy, the strategy is probably getting crowded. In the case ...


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