New Standards in Trading Indicators

January 31st, 2022 2 minutes read

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Analysts and portfolio managers face many challenges in developing new systematic trading systems. Finding valuable trading signals is difficult given the information overload and asymmetry. Their use must yield gains and comply with risk-management practices. And some think developing these systems is pointless.  

In more illiquid markets, asset prices and trading volumes are rare (or very lagged). That is, without market makers, even with the presence of intermediaries, the party that initiates gets into a weaker position and so players would rather not. This disincentivizes discretionary traders from having a wide, active trading reach, as they are disadvantaged from the lack of information. 

On the other hand, modern behavioral economic theory points out their importance. Signals are relevant in the presence of noise traders. Additionally, the collective cost of their discovery is likely to be limited by their benefits. The latter is essential to understand both the boundaries of signals and the significance of proper backtesting. One can apply backtesting in the case of the volume of web queries. What does that mean, more specifically? 

It means finding trading strategies that are stochastically superior when methodic protocols are applied. In addition to the issue of methodology, two problems arise: 

a. if one knows, all might know, and 

b. over time, if it will disappear. 

The market microstructure literature widely documents the first one. The answer is ‘no’ as it depends on the effort(s) and knowledge of each trader. The response to the second one lies in continuously backtesting the trading strategy. This way, one can prevent the alpha decay, which means one can always obtain relevant, prevailing alpha.

Backtesting is an essential tool for a certain type of strategy. When you are trading live, the market always responds to your actions. Your trades and submitted orders always have an impact regardless of how small your trade size is. The orders that you submit change market depth and you can see these changes in real-time. This can never be simulated in backtesting as no algorithm is able to recreate market reaction on market depth change. 

Backtesting provides a host of advantages for algorithmic trading. However, it is not always possible to backtest a strategy. In general, as the frequency of the strategy increases, it becomes harder to correctly model the microstructure effects of the market and exchanges. This leads to less reliable backtests and thus a trickier evaluation of a chosen strategy. This becomes even more problematic when the execution system is the key to the strategy performance, as with ultra-high frequency algorithms.

It is important to understand that every trading strategy type needs different simulation: scalping strategies require one simulation type, position trading needs another one etc. Backtesting, optimization, and forward testing (real-time simulation) provide an insight into the potential performance of your strategy. Moreover, backtesting serves as a key element in preventing alpha decay and it can help a trader picture a range of possible scenarios. It is up to the trader to decide if this range is acceptable for live trading or not.






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