the ranking system for both all fundamental and sp1500 mid
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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.