Kamis, 01 April 2021

Conditional Parameter Optimization: Adapting Parameters to Changing Market Regimes via Machine Learning

Every trader knows that there are market regimes
that are favorable to their strategies, and other regimes that are not. Some
regimes are obvious, like bull vs bear markets, calm vs choppy markets, etc.
These regimes affect many strategies and portfolios (unless they are
market-neutral or volatility-neutral portfolios) and are readily observable and
identifiable (but perhaps not predictable). Other regimes are more subtle, and
may only affect your specific strategy. Regimes may change every day, and they may
not be observable. It is often not as simple as saying the market has two
regimes, and we are currently in regime 2 instead of 1. For example, with
respect to the profitability of your specific strategy, the market may have 5
different regimes. But it is not easy to specify exactly what those 5 regimes
are, and which of the 5 we are in today, not to mention predicting which regime
will be in tomorrow. We won’t even know
that there are exactly 5!

Regime changes sometimes necessitate a complete
change of trading strategy (e.g. trading a mean-reverting instead of momentum
strategy). Other times, traders just need to change the parameters of their
existing trading strategy to adapt to a different regime. My colleagues and I
at PredictNow.ai have come up with a novel way of adapting the parameters of a
trading strategy, a technique we called “Conditional Parameter Optimization”
(CPO). This patent-pending invention allows traders to adapt new parameters as
frequently as they like—perhaps for every trading day or even every single

CPO uses machine learning to place orders
optimally based on changing market conditions (regimes) in any market. Traders in these markets typically already
possess a basic trading strategy that decides the timing, pricing, type, and/or
size of such orders. This trading strategy will usually have a small number of
adjustable trading parameters. Conventionally, they are often optimized based
on a fixed historical data set (“train set”). Alternatively, they may be
periodically reoptimized using an expanding or rolling train set. (The latter
is often called “Walk Forward Optimization”.) With a fixed train set, the
trading parameters clearly cannot adapt to changing regimes. With an expanding
train set, the trading parameters still cannot respond to rapidly changing
market conditions because the additional data is but a small fraction of the
existing train set. Even with a rolling train set, there is no evidence that
the parameters optimized in the most recent historical period gives better out-of-sample
performance. A too-small rolling train set will also give unstable and
unreliable predictive results given the lack of statistical significance. All
these conventional optimization procedures can be called unconditional parameter optimization, as the trading parameters do
not intelligently respond to rapidly changing market conditions. Ideally, we
would like trading parameters that are much more sensitive to the market
conditions and yet are trained on a large enough amount of data.

To address this adaptability problem, we apply a
supervised machine learning algorithm (specifically, random forest with
boosting) to learn from a large predictor (“feature”) set that captures various
aspects of the prevailing market conditions, together with specific values of
the trading parameters, to predict the outcome of the trading strategy. (An
example outcome is the strategy’s future one-day return.) Once such
machine-learning model is trained to predict the outcome, we can apply it to
live trading by feeding in the features that represent the latest market
conditions as well as various combinations of the trading parameters. The set
of parameters that results in the optimal predicted outcome (e.g., the highest
future one-day return) will be selected as optimal, and will be adopted for the
trading strategy for the next period. The trader can make such predictions and
adjust the trading strategy as frequently as needed to respond to rapidly
changing market conditions.

In the example you can download here, I illustrate how we apply CPO using
PredictNow.ai’s financial machine learning API to adapt the parameters of a
Bollinger Band-based mean reversion strategy on GLD (the gold ETF) and obtain
superior results which I highlight here:




Unconditional Optimization

Conditional Optimization











The CPO technique is useful in industry
verticals other than finance as well – after all, optimization under time
varying and stochastic condition is a very general problem. For example, wait
times in a hospital emergency room may be minimized by optimizing various
parameters, such as staffing level, equipment and supplies readiness, discharge
rate, etc. Current state-of-the-art methods generally find the optimal
parameters by looking at what worked best on average in the past. There is also
no mathematical function that exactly determines wait time based on these
parameters. The CPO technique employs other variables such as time of day, day
of week, season, weather, whether there are recent mass events, etc. to predict
the wait time under various parameter combinations, and thereby find the
optimal combination under the current conditions in order to achieve the
shortest wait time.

We can provide you with the scripts to run CPO
on your own strategy using Predictnow.ai’s API. Please email
info@predictnow.ai for a free trial.

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