Reddit reviews Building Winning Algorithmic Trading Systems, + Website: A Trader's Journey From Data Mining to Monte Carlo Simulation to Live Trading (Wiley Trading)

First of all, thanks for sharing. Code & idea implementation sucks, but it might turn into a very interesting discussion! By admitting that your trade idea is far from being unique and brilliant, you make a super important step in learning. This forum needs more posts like that, and I encourage people to provide feedback!

Idea itself is decent, but your code does not implement it:

• You want to holds stocks that are going up, right? Well, imagine a stock above 100ma, 50ma, 20ma, but below 20ma and 10ma. It is just starting to turn down. According to your code, this stock is labeled as a 'rising stock', which is wrong.
  
  
• SMAs are generally not cool. Not cool due to lag of 1/2 of MA period.
  
  
• Think of other ways to implement your idea of gauging "going up stocks". Try to define what is a "stock that is going up".
  
  
• Overbought/oversold part. This part is worse. You heard that "RSI measures overbought/oversold", so you plug it in. You have to define "Overbought/oversold" first, then check if RSI implements your idea of overbought/oversold best, then include it.
  
  
• Since you did not define "overbought / oversold", and check whether RSI is good for it, you decided to throw a couple more indicators on top, just to be sure =) That is a bad idea. Mindlessly introducing more indicators does not improve your strategy, but it does greatly increase overfit.
  
  
• Labeling "Sell / Neutral / Buy " part. It is getting worse =)) How did you decide what thresholds to use for the labels? Why does ma_count and oscCount with a threshold of 0 is the best way to label? You are losing your initial idea!
  Just because 0 looks good, you decide that 0 is the best threshold. You have to do a research here. You'd be surprised by how counter intuitive the result might be, or how super unstable it might be=))
  
  
• Last but not least. Pls count the number of parameters. MAs, RSI, OSC, BBand + thresholds for RSI, OSC + Label thresholds ... I don't want to count, but I am sure it is well above 10 (maybe 15+?). Now even if you test at least 6-7 combinations of your parameters, your parameter space will be 10k+ of possible combinations. And that is just for a simple strategy.
  
  
• With 10k+ combinations on a daily data, I can overfit to a perfect straight line pnl. There is no way with so many degrees of freedom to tell if you overfit or not. Even on a 1min data!
  
  The lesson is: idea first. Define it well. Then try to pick minimal number of indicators (or functions) that implement it. Check for parameter space. If you have too many parameters, discard your idea, since you will not be able to tell if it is making/losing money because it has an edge or just purely by chance!
  
  What is left out of this discussion: cross validation and picking best parameters going forward
  
  Recommended reading:
  
• https://www.amazon.com/Building-Winning-Algorithmic-Trading-Systems/dp/1118778987/
  
• https://www.amazon.com/Elements-Statistical-Learning-Prediction-Statistics/dp/0387848576/

Probably start with something like:

• Mathematical Financial Economics
  
  
  
• Trading and Exchanges: Market Microstructure for practioners
  
  
  
• Building Winning Algorithmic Trading Systems
  
  And then you decide whether you are going to focus on one of the two primary trading strategies (mean reversion or momentum) and focus on that one for a bit.
  
  
• Optimal Mean Reversion Trading
  
• Algorithmic Trading: Winning Strategies and their Rationale
  
  And that's assuming you have the programming knowledge to implement a system that can handle this of course - otherwise I'd go start with C# or C++ until further notice.

It actually does use indicators, and those indicators predict trends.

Mathematical models: I have only studied indicators. In the beginning of my project, I tried to create my own indicators using parametric equations, but it wasn't working. I couldn't get the algorithms to produce results better than random backtests. So I moved from that into real indicators.

Books:
The Ultimate Day Trader
It was the most helpful when I was getting started and learning about indicators. It taught me how trading was done, and it introduced the typical algorithmic trading like MACD crossovers, bullish convergence/divergence. It may be too much for beginners. As a warning, reviewers on Amazon don't think highly of the book.

I had to learn a lot on my own through trial and error and the occasional google search, so I The Ultimate Day Trader is the only book that I fully read.

Building Winning Algorithmic Trading Systems
Gives a lot of good information in getting good backtest results, and the steps an algorithm should have to pass in order to be traded with.

Algorithmic trading: Winning strategies and their rationale.
Currently reading this, and it starts off basic, like most books. It talks about look-ahead biases and that sort of stuff. It also talks about the different backtesting software and programming languages. I'm only on page 40/200, and it looks like it gets more complex.

I also have a few books on options, but those don't have to do with algorithmic trading.

I like Kevin Davey's book = https://www.amazon.com/Building-Winning-Algorithmic-Trading-Systems/dp/1118778987/ref=sr_1_1?keywords=Kevin+davey&qid=1558288403&s=gateway&sr=8-1

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Don't really know your level, but what I like about his book is that he is honest and shares an actual trading strategy of his. Lots of good advice in there about the process he follows.

We've all seen those backtests that promise super high compound annual growth, the trick is getting that kind of result on a market that your algo hasn't seen before. Out of sample validation is key.

How about these books? have you heard anything about them?

http://www.amazon.com/Trading-Systems-Methods-Website-Wiley/dp/1118043561/ref=pd_bxgy_14_img_2?ie=UTF8&refRID=1X0Y2BRSS0H1EWVB5HKC

http://www.amazon.com/Building-Winning-Algorithmic-Trading-Systems/dp/1118778987/ref=pd_sim_14_1?ie=UTF8&dpID=51eM1r6mrRL&dpSrc=sims&preST=_AC_UL160_SR127%2C160_&refRID=0HJDDFQYBW3A4V6WGXXX

You should read building algorithmic trading systems by Kevin Davey
There is tons of information on sizing, risk management, risk of ruin of a strategy and how to automated them

https://www.amazon.com/Building-Winning-Algorithmic-Trading-Systems/dp/1118778987/ref=sr_1_2?crid=2MD5SFTI494L&keywords=kevin+davey&qid=1565714567&s=gateway&sprefix=kevin+da%2Caps%2C217&sr=8-2

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Note: I am not affiliated with Amazon, I don't get commission on the sales :) but it is still a kick a$$ book