A strategy with less entry signals means the return will be lower, as well as the max drawdown. The model that I used to run on gtja quant platform has only 3 types of entry signals; I managed to migrate the model to joinquant to run further regression test, which shows impressive result from 2006-2022, with sharpe around 1.1 and calmar around 1.
| Component | Description |
|---|---|
| Index Filter | 1. long term trend: mas>mal |
| 2. short term trend: strength>55 and baseline travels up. | |
| 3. price action: price is above baseline. | |
| Momentum Rank | * calculates sharpew_shift(25,7) |
| * keep only stocks who have positive momentum. | |
| * find the median of positive ranks, and return the higher half of the list. | |
| Stock Filter | * take only top 50 of stocks if there’re more to choose. |
| * filter-ichimoku: ichimoku is above trend; and ichimoku hikes up. | |
| * filter-baseline: baseline hikes up and strength>50. | |
| Entry | * pricecross: strength>50 and balancer>baseline and price crosses above baseline. |
| * crossover : strength>50 and balancer crosses above baseline. | |
| * impuse : strength>55 and balancer>baseline and impulse crosses above 30 and spread contracts from above 30 to below 30. | |
| Buy Sequence | * wsharpe/slowd |
| Exit | * ema cross: balancer<baseline and strength<50. |
| * stoploss : price<ichimoku. | |
| Risk | risk parity: each stock has same amount of risk impact to the entire portfolio. |
ichihedge 
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