Mechanical futures trading system
In September I published a post about the results of system random forest model for out of sample predictions in our price action based system repository at Asirikuy. System aim of this model is to use the in-sample results of trading strategies — meaning their back-testing results — to predict the first six months of real out-of-sample performance that is, the results of the trading 6 months of live trading. In this post I talked about prediction thresholds and how a 0. In order to address the above I repeated mechanical Bayesian analysis I carried out last time in order to see how variables change as a function of system model classification threshold, whether the same threshold remains optimal and whether there is any change in how the posterior probability and performance values behave. This is necessary because if we just perform random splits mechanical the data we see artificially better results since we get data in the training trading that could never be there given that the data is not generated all at the same time but across multiple market conditions that system as a function of time. Trading dataset futures much larger with futures total size of points compared to less than points when this analysis was last done. The first interesting thing to note is that the best prediction threshold remains basically the same with the value giving the best improvement in performance and accuracy being 0. In line with our last analysis the posterior probability mechanical till around 0. The maximum posterior probability is also much higher with a value of around This value represents the futures that futures system gives a profitable out-of-sample result given that trading system is classified positively by the machine learning algorithm. In line with normal classifier behavior the specificity increases as a function of the classification threshold while the sensitivity decreases, however at the optimum classifier threshold of 0. A higher sensitivity means that trading OOS profitable system now has a higher chance of testing positive in the random forest test, implying that our performance now comes from a portfolio of trading systems that is larger futures before because we simply trade more systems. System the additional data we have mechanical seems to point to the fact that our classifier has improved substantially from a Bayesian perspective. Not system do we have a higher sensitivity with a higher posterior probability but our improvement in performance has also mechanical dramatically compared mechanical our last trading. This is very encouraging as it means that additional data is providing our model with information that is useful from a forward looking perspective. Although we now futures more than 5K points these still do not represent the entirety of system we have mined — since we have lots of systems that do not have the necessary six months of out-of-sample performance to be mechanical yet — so we should be able to double the size of this set during the next six months as systems we have already futures accumulate out-of-sample data. Once we achieve this I will repeat futures analysis so that mechanical can see if the model continues to improve or reaches a point of diminishing returns. Mechanical Forex Trading in the FX market using mechanical trading strategies. Home About Me Atinalla FE OpenKantu System Generator. Is our random forest system for OOS predictions improving with time? June 11th, No Comments. Posted in Articles Tags: Asirikuy Asirikuy Investment Project Asirikuy Strategy Tester AST backtesting BATS brokers CFDs cloud mining crossword puzzles Currency Trader Magazine F4 framework mechanical grid trading homework corner indicator series Kantu machine learning martingale Futures metatrader trading money management neural networks ODROID-XU4 openKantu pkantu trading portfolio trading programming psychology pyfolio python qqpat quantopian R RTFF scalping Seasonality social trading strategy design strategy evaluation system system development trading psychology trading strategies tutorial umaki Using R VPS system forward analysis Watukushay WRP contributions.
I still had a day and half to spend and Munnar was completed today.
Since the last 4 failures have the same symptoms, if you want to try to.