SPY - SPX - S&P --- DAILY MODEL
This model is optimized for SPY on a daily time-frame.
Even though it is still profitable (Profit factor > 1) on other time-frames, such as 1h or weekly, I strongly advise you to NOT consider these signals.
You might also get positive returns on other assets, and time-frames, and I also strongly advise you to NOT consider them for your trades. For example:
AAPL-1h
GOOGL-D-W
TSLA-D-W
PYPL-D
INTC-W
MSFT-D-W
FDN-D-W
And so on …
This model is an optimization (parameters tuning) of a meta-model (generic model) for the SPY. It is mainly based on a conjunction of price & volume personal indicators for both entry and exit signals.
The relative portability of the model to other assets and time-frames, coupled with a "Development set -> Validation set" approach, confers it a stronger reliability, and a better warranty of not being « over-optimized ». The meta-model has also served for other model buildings, about 100 as of today.
Be advised that this model applied to real data will get much lower profit factors. During high-volatility periods (such as current times), the model might also be less accurate, as "News streams", more than "prices and volumes", make the market.
As always, this model is for an educational purpose only, and should never be considered as a single decision tool. So, study it, and make sure your decisions are still your own choice.
Even though it is still profitable (Profit factor > 1) on other time-frames, such as 1h or weekly, I strongly advise you to NOT consider these signals.
You might also get positive returns on other assets, and time-frames, and I also strongly advise you to NOT consider them for your trades. For example:
AAPL-1h
GOOGL-D-W
TSLA-D-W
PYPL-D
INTC-W
MSFT-D-W
FDN-D-W
And so on …
This model is an optimization (parameters tuning) of a meta-model (generic model) for the SPY. It is mainly based on a conjunction of price & volume personal indicators for both entry and exit signals.
The relative portability of the model to other assets and time-frames, coupled with a "Development set -> Validation set" approach, confers it a stronger reliability, and a better warranty of not being « over-optimized ». The meta-model has also served for other model buildings, about 100 as of today.
Be advised that this model applied to real data will get much lower profit factors. During high-volatility periods (such as current times), the model might also be less accurate, as "News streams", more than "prices and volumes", make the market.
As always, this model is for an educational purpose only, and should never be considered as a single decision tool. So, study it, and make sure your decisions are still your own choice.
版本注释:
Update to comply with the last data.
AGAIN PLEASE DON'T CONSIDER IT THESE DAYS. VOLATILITY IS TOO HIGH.
AGAIN PLEASE DON'T CONSIDER IT THESE DAYS. VOLATILITY IS TOO HIGH.
版本注释:
Version which takes into account last 2 months of data
版本注释:
New version with latest data
版本注释:
New version with latest data
版本注释:
1) Added stopLoss exits for both LONG and SHORT signals
2) Narrowed the spectrum of each substrategy to the best of the best
3) Added a max VIX parameter to avoid false entry/exit signals due to high volatility
4) Removed the swing-short substrategy which was not convincing enough on real data
2) Narrowed the spectrum of each substrategy to the best of the best
3) Added a max VIX parameter to avoid false entry/exit signals due to high volatility
4) Removed the swing-short substrategy which was not convincing enough on real data
版本注释:
(Republished for the daily Timeframe, sorry)
1) Added stopLoss exits for both LONG and SHORT signals
2) Narrowed the spectrum of each substrategy to the best of the best
3) Added a max VIX parameter to avoid false entry/exit signals due to high volatility
4) Removed the swing-short substrategy which was not convincing enough on real data
1) Added stopLoss exits for both LONG and SHORT signals
2) Narrowed the spectrum of each substrategy to the best of the best
3) Added a max VIX parameter to avoid false entry/exit signals due to high volatility
4) Removed the swing-short substrategy which was not convincing enough on real data
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