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14

There are few things to consider. Trading moves the price, to minimize market impact and maximize return it is generally optimal to split an order in several child orders. See the Kyle model. Splitting optimally dependents on specific assumptions that you make. The simplest (and first) approach is that of Berstsimas and Lo (Optimal Control of Execution ...


13

To respond to your questions in order: The formula looks deceptively simple. Does it actually work? That depends on what you mean by "work". Chan spends the rest of the chapter discussing the pitfalls of investing at "full Kelly". Do professionals use it at all? Professionals may maximize geometric growth, but I don't know anyone who does so with ...


10

First of all a very warm welcome to Quantitative Finance Stack Exchange :-) Concerning your question there are some basic points that seem to be unclear. In general "Quantitative Trading" by Ernie Chan is a good starting point for learning about quantitative trading strategies. The problem is of course that in this small book there are many concepts whose ...


7

This paper Dealing with the Inventory Risk. A solution to the market making problem, has a full bibliography and explains the intra day market making mechanism. The model is made of two components: a diffusion of the fair price (to model the market risk) a point process (with an intensity in $A \exp -k \delta$ (where $\delta$ is the distance to the fair ...


7

The investor's holdings is a consequence of an investor's utility function interacting with the investor's perceived trading opportunity subject to constraints. (Indeed, the Kelly criterion is also utility maximizing.) We produced trades by re-balancing -- that is to say, we have new expectations of alpha or risk and the optimal portfolio net of these ...


6

From your comments I have deciphered that what you actually want to know is what the maximum amount of size is that you can trade at any time. Holding aside exchange irregularities, the answer to this is the total amount of size on one side of the book in the direction that you want to trade (e.g. bid side if you want to sell), at the time that you want to ...


6

The Kelly criterion is a very popular bet-sizing method. Edward Thorp has written a great deal on this topic. You can try googling for more, or start with his review of the concept, or a recent paper, Medium Term Simulations of The Full Kelly and Fractional Kelly Investment Strategies. This is not specific to futures, but I'm not sure why you would need ...


5

You are trying to apply the Kelly Criterion, supposedly to maximize how aggressively to bet, and you are having trouble when the Kelly Value turns negative. The naive answer to your question is that when your kelly value turns negative, then $f=\frac{bp-q}{b}$ turning negative means the instantaneous expected return is negative, which means you should not ...


4

The optimal position size can be determined with the Kelly criterion. In your specific case, the long term growth rate of the capital X is maximized by betting $$(0.6-0.4)X=0.2X$$ at each opportunity.


4

Van Tharp's Definitive Guide to Position Sizing identifies 31 separate position sizing models (be sure to check out the extended table of contents). Specifically for futures trading, I quite like Ryan Jones' Fixed Ratio position sizing, a nice overview of which is available here, book on Amazon and OTT website here.


4

Money Management Strategies for Futures Traders by Nauzer J. Balsara is a good resource.


3

Actually your question englobes many questions. In my opinion, you shouldn't only focus on the total volume you're going to execute on a specific day, but also on how you're going to split it into meta-orders(orders of small amounts) all over the day. You need to have: A model for daily volume (which i think is what are you looking for, then an ...


2

If you have K strategies and each strategy has an expected return, a variance, and you can measure the covariance of your strategies performance then a mean-variance optimization would answer how to optimally allocate capital amongst your strategies. Key to this approach is accurate estimation of your the input parameters identified above.


2

You could try net positions: where you continuously buy and sell depending on the signals generated. Net positions may lead to unnecessary commissions/spread nickel-and-diming your profits away. Once you have picked a direction and already have trade entry, your system should instead continue looking for new signals in the BACKGROUND. New signals while in ...


2

The answer depends on the reasoning behind your forecast. Is this a mean-reversion signal? If so, perhaps the presence of a short signal shortly after a long signal indicates that the long signal was very profitable, and you should take profits immediately. Is it a momentum signal? If so, then perhaps the momentum of this stock is very choppy at the ...


2

You first need to clearly define your constraints first: max single position size max net exposure I am not sure why you want to limit order size. The whole idea of hft strategies is to maximize turnover. As long as your strategy generates alpha you should allow it to trade as often as the strategy prescribes. All you need to then do is to constrain the ...


2

If you designed the model to predict direction only, I would just use the current signal. You could test whether this is correct by calculating the signals and their 5-second lags, then regress 1-minute forward returns (or 55-second fwd returns) on them both, and see if the coeff on the 5-second lagged signal is significant. If it's not significant, just ...


2

You must look at passive volumes available on certain levels in orderbook, that feature is called market depth. There is a possibility that daily volume is correlated somehow with depth on market levels around a price, but I think you must gather some data and model that relationship when you want do that in this way.


1

Interesting idea. I would like to have the reference you are using for this scheme? You are treating X/Y as a stock and then using Bollinger Band kind of signalling mechanism. I would think you would define the entry/exits more strictly to make it profitable. If you are making a lot of trades using this system, then from backtesting, you can figure out ...


1

One solution I have been considering is to add a target position parameter with a time decay. For example, given the $t_1$ buy and $t_2$ short signals described in the question and assuming a 5 seconds signal window to simplify, we would have the following time-based target positions: ╔════════════════╦═══════╦═══════╦═══════╦═══════╦═══════╦═══════╦════╗ ║ ...


1

If you want to use the Kelly Criterion you might find this link, particularly part III, useful.


1

Use the Kelly Criterion (as suggested by @olaker). For the amount of money to put into each transaction, use Implied Volatility to calculate the amount you are risking to within a VAR (Value At Risk) of 99% (i.e. +/- 3 standard deviations of the underlying). See How to calculate future distribution of price using volatility? Caveats: Markets price ...



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