Allocate Trading funds to strategies based on market conditions

Trading systems should allocate trading funds to  strategies to maximize trading profits in current market conditions.    Some strategies perform well in specific market conditions such as topping while others will do better during strong directional markets.  Shifting fund allocation to strategies matched to current market conditions will yield the highest profit.    Operating multiple strategies increases risk of duplicate risk exposure that must be controlled by cross strategy risk management rules.

Ed Thorp is credited with adapting the Kelly criterion otherwise known as the Kelly betting system to determine how much to invest in a given stock based on the confidence of your selection criteria.   He published this in Beat the Market.   There is a lot of published art for the Kelly criteria but a simple version is also described in Thorps book “Beat the Dealer”.

I extended the Kelly criterion by using the strategy confidence coupled with the strategies performance in %gain per day over a period of time such as a running 16 days.

Control duplicate risk across strategies

The greater problem is unanticipated risk multiplication across strategies.   We have over 20 strategies but they can make overlapping decisions for example one strategy may want to buy stock on QQQ while a different strategy wants to buy a call on SPY.    This could be considered the same risk due to the high correlation.

We control this with a Meta risk control algorithm / module.   Each transaction decision is run through this module.   If it senses doubling down on a given risk it compares the total at risk for the correlated transactions against the risk control limits.   If it detects a transaction that would exceed allowed risk concentration for with a given transaction it rejects that transaction.

We also  give priority to stock transactions which will create a natural hedge or offset against an existing position.  I also use a modified form of the Kelly criteria to control risk concentration by sector.

Allocate Trading funds to the trading strategy best suited for current market conditions

Our strategy rotation scheme is not driven by low confidence in any single strategy but rather by the fact that some strategies do very well in some markets and other will do better in others.   We shift allocations between trading strategies to concentrate funds where they will yield highest profit during current market conditions.     I use a software based system re-balance  allocations between strategies on a weekly basis.    This is somewhat easier for Bayes Analytic which has micro-fund allocations and micro-fund accounting built in.

An example of this is we have one strategy that focuses on 1.5% gains over 1.5 days and another which looks for 6% over 10 days.   In some markets such as strongly trending markets the 6% strategy will outperform the 1.5% strategy due to lower trading costs.   However in a topping market the 1.5% strategy out performs and it is better suited for rapid exit during a significant corrections which are at higher risk during topping activity.

Use market themes to help choose strategy allocations

We group the overall market performance into a series of dominate themes such as Bull, Bear, Rising, Falling, Topping.  We measure the performance of each strategy relative to the current dominant trends both market wide and by segment and use this to adjust the allocation ratios.   We also measure the performance by symbol + strategy + theme and may adjust the symbols available to each strategy based on these metrics.     If we detect a confirmed change in theme it becomes a judgment call about switching strategy allocations to strategies best suited to the new theme.   I use a modified Kelly criteria approach driven by the confidence of the new theme sticking to determine how fast we shift the funds to the other strategies that have a good history of performance under that market theme.

Performance Weighted funds allocation

I use the performance ratio each strategy and divide it across the strategies in a ratio metric basis based on recent performance for the top rank ordered strategies.      Each transaction is reviewed by the risk control module and rejected if the set of all transactions across all active strategies exceed risk thresholds.

 Use paper trading to compare new strategies to life strategies

We keep trade each new strategy in paper trading for a period of time before it becomes a candidate for live trading.    If any strategy would not receive a sufficient allocation for an efficient transaction size then it’s money is reallocated and it moves back into paper trade status.      If we make substantial changes to a given algorithm the new version is treated as a new strategy and moves back to paper trading status.  It is allowed to fast track back to live trading if it out performs the existing version of the same algorithm.

Any strategy you are not trading with live funds should be paper trading as if live so you can compare performance to your other strategies.  We use this comparison as part of the decision criteria to allocate trading funds to that strategy for live trading.

Contrarian strategies hedge against major corrections

One additional complication occurs because we have a few strategies that make low profit during normal market conditions but do exceptionally well during strong downward corrections.  Having enough money in those fringe strategies can help offset losses during major market moves.   I allocate trading funds to fringe hedging strategies based on a small percentage of the long exposure.  They do not fire very often but when they do these contrarian positions can earn 300% to 800% during a major correction over a very short period of time.    I consider them a permanent hedge but rather than accepting them as a cost we invest considerable effort in their algorithms to keep them cash positive during normal market conditions.

If you figure a 400% upside during a major correction which could deliver a worst case 25% reduction in our open long positions then an ideal ratio would be 6.25%.  This means for every $16 in long positions we should allocate $1 dollar in the contrarian strategies.

This can be a little more complicated because 25% drops are relatively uncommon so to keep them cash positive  we design the fringe strategies to make money with smaller moves such as 5% to 8% corrections.  This means they may not be holding a full load of contrarian positions at the time of a major market drop.  This would indicate a benefit from allocating a more money to the contrarian strategies.

Rapid single day drops like the 2008 drop where we could not exit our long positions at the predetermined stop limits are super rare.   The  more probable loss during a downward move is between 3% and 5% once our stop limits kick in and we exit our long positions.    This indicates we could cover our downside risk over 90% of the time with a ratio closer to 1.25%.    Choosing between these ranges is essentially a judgment call.

 Continue investing in lower ranked strategies

We continue to invest in our  low performing strategies trying to improve their algorithms so their performance grows to exceed the current great performing strategies.     This may seem counter intuitive but over time it helps to continue drive new ideas into the production system.    From a people management perspective it gives our junior people a chance to prove they can beat the veterans.

Note:  The % and days mentioned here are illustrative.     I describe some of our risk control rules in another article.   There are also some risk management rules described in when good traders loose.

Joe Ellsworth
CTO of Bayes Analytic
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Required Disclaimer

Forex, futures, stock, and options trading is not appropriate for everyone. There is a substantial risk of loss associated with trading these markets. Losses can and will occur. No system or methodology has ever been developed that can guarantee profits or ensure freedom from losses. No representation or implication is being made that using this methodology or system or the information in this letter will generate profits or ensure freedom from losses.  Forex and Option trading can result in losses that exceed the original principal balance.

Hypothetical or simulated performance results have certain limitations. Unlike an actual performance record, simulated results do not represent actual trading. Also, since the trades have not been executed, the results may have under-or-over compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profit or losses similar to those shown.

Even results from Live cash trading can be subject to specific market conditions that may not repeat in the future and as such, duplicate results from future trading is unlikely to duplicate past results.    Changing the dollar amount traded can cause different behavior in live trading markets especially when trading large positons that can exceed the liquidity available in the market and cause changes in pricing behavior.

Bayes Analytic LLC provides software that can produce trading signals.  The customer is responsible for choosing a configuration and parameters for the software that meets their own goals.  The customer is responsible for conducting their own tests and only the customer can activate the software to start trading.   The software runs in an account the customer has logged into and then activated the software.   Bayes Analytic has no control of,  influence over or visibility to the signals specific to given user because we have no visibility into configuration parameters the user has chosen to operate with.    The Bayes Analytic software is provided without Warranty on a As Is, Where is basis.  It is the customers responsibility to test the software to ensure it meets their trading requirements.   Every time Bayes Analytic releases a new version of the software the customer should conduct new tests to validate the new version continues to meet their requirements because every software change could have unexpected side effects that may not be obvious until the customer has tested it in their environment with their configurations.   The Bayes Analytic software may run as a script inside of other software packages or talking to API that Bayes Analytic has no control of or Influence over so the customer should test entire ecosystem to ensure it meets their trading requirements.    Bayes Analytic may provide the software in source form since that is required by some trading systems but it remains the exclusive copyrighted property of Bayes Analytic and may not be reverse engineered or redistributed.    The customer is responsible  for choosing their own broker and installing the Bayes Analytic software so it can trade using the desired account.  Bayes Analytic has no control over or influence of the broker and many brokers have different ways of quoting spreads,  charging commissions,  flow of orders and latency of information.  As such a strategy and software that performs well at one broker may and probably will require changes to perform well at other brokers.  It is the customers responsibility to test the software with their selected broker to ensure it meets their trading requirements.

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