The ranking system for both All Fundamental and SP1500 mid&big caps universes (14 years)

The ranking system for both All Fundamental and SP1500 mid&big caps universes (5 years)

All my R2G models (present and future) have in common the following:

1. The ranking system is the core of the strategy, that’s why it’s the most sophisticated

part, buy and sell rules are very simple, by keeping things simple we just avoid

overoptimization or curve fitting.

2. They all use different ranking systems in order to avoid overslapping and excesive

slippage. Each ranking system is completely different one from another, some are

more based on growth+momentum, and others are more focused on value, technical

analysis, or pure fundamentals…

3. The more robustness test they pass, the better, if you have additional robustness test

you want me to make, please contact me.

Diversification across strategies is my own way to operate in the markets, that’s why I’m

building so many different types of strategies and doing as many robustness test as I can in

order to verify that the performance is not the result of mere chance and to get some clue as

to whether the strategy will continue to outperform in the future.

BUY RULES

SELL RULES

SYSTEM OVERVIEW

ROBUSTNESS TESTS1% FIXED SLIPPAGE WITH NEXT DAY HI LOW

NEXT CLOSE

10 STOCKS EVENID=1

10 STOCKS EVENID=0

EXCLUDING TOP 10 PERFORMERS

LET’S TRADE WITH A PORTFOLIO OF 20 STOCKS

LET’S TRADE WITH A PORTFOLIO OF 50 STOCKS

LET’S TRADE WITH A PORTFOLIO OF 100 STOCKS

CHANGING THE UNIVERSE TO SP500

CHANGING THE UNIVERSE TO SP400

CHANGING THE UNIVERSE TO SP600 (AFTER ERASING THE MktCap > 1000 BUY RULE

because SP600 is a small caps index)

WITHOUT MARKET TIMING

TURNING OFF ALL BUY & SELL RULES AND SETTING LIQUIDATION EVERY 2 WEEKS

INCREASING TURNOVER MAX TURNOVER = 978.54%

Now we are going to run a backtest with severely degraded factors:

First we remove the weights of every node and sub-node in the ranking system.

On the universe, we add 20% to every factor, namely liquidity and minimum stock

price.

For each buy and sell rule that depends on a threshold (market timing, ranking

signals etc..) we will add 20% (first image) and remove 20% (second image) on each

threshold.

We run the simulation with (high+low)/2 as the transaction price and variable

slippage.

CONCLUSIONS

It is very unlikely the performance is the result of chance. The model kept

outperforming the market even as we radically altered its parameters.

Drawdowns down to 55% have to be expected in case of a generalized market

decline.

This strategy did provide an edge on the market for the last 14 years. We can

reasonably expect it will continue to do so in the future.

These curves are a better representation of the model going forward, since they are

the result of sub-optimal parameters.

Remember, nobody can be certain about the future so trade only with money you

can afford to lose.

Happy trading !

Disclaimers

Simulation results must be interpreted in light of differences between

simulated performance and actual trading, differences between

subscriber performance and live out-of-sample model performance, and

the fact that past performance is no guarantee of future results. (See

Subscriber Terms.)

Please note I am not a registered adviser. I am not offering personal

advice regarding the suitability of a particular investment. If you are

unsure as to the suitability of a particular investment for your own

circumstances please contact a registered financial adviser for advice.