Good starting point.
First thing that popped into my mind was to sort stocks into sectors and try and match stocks within each sector.
Will have a bit of a play and see how it goes.

Thanks for the reply, Max! But, because it's about quantitative trading, I prefer technical analysis to fundamental. Patterns in the price data. No disrespect. Cheers!

It's not totally unreasonable to try and neutralize sector-specific risk factors. It would help isolate the source of your returns, whether you are making bets on idiosyncratic stock time-series momentum, or broader sector momentum, in which case one might more efficiently implement it using sector ETFs.

I agree with Simon. Using the sector ETFs wouldnt be a bad test

Check logs and you will find a pattern
Buying ABEO at 2.77 and then selling for 7.60
Buying ADXS at 7 then selling for 17

Need to fix stock list and remove bias

OK, thank you all for the feedback. I will try to upgrade it with your suggestions.

@Adrian
Could you explain this line of code ? If I get it right you are checking if the price change for past 5 days was more than +20% and for the last 30 days was less than -3% ?

 barCnt2 = 30  
  dif2 = 0.03  
  barCnt1 = 5  
  dif1 = 0.2  
  #1  
  if get_datetime().hour == 17 and get_datetime().minute == 40:  
       positions = context.portfolio.positions  
       for stock in context.RUS_1:  
         s1 = stock  
         if not get_open_orders(s1):  
           if s1 not in positions:  
             A_history_close = history(bar_count=barCnt2,frequency='1d', field='price')  
             close_A_past2 = A_history_close[stock][-barCnt2]  
             close_A_past1 = A_history_close[stock][-barCnt1]  
             close_A_1 = A_history_close[stock][-1]  
             A_move_2 = close_A_past2 / close_A_1 - 1  
             A_move_1 = close_A_past1 / close_A_1 - 1  
             if A_move_1 > dif1:  
               if A_move_2 < -dif2:

Does anyone know of a place to scrape a historical russell 2000 list?

@ Yatharth
Exactly. If it was a positive percentual change ( A_move_2 > 0% ) in the last 30 workdays, it means the trend for the stock is not bearish, but bullish for the last 30 days. Yes, it seems a little bit ticky, but practically the algo checks if the current price of the stock is not higher than 30 days ago, even with the 20%+ rise in price.

Dear Mr. Yolo,

check out this post by James: https://www.quantopian.com/posts/simulating-s-and-p-500-russel-1000-russell-3000-in-research

The Russell indices have relatively clear definitions so you can build off of James' post there to create your own simulation of the 2000 using fundamentals.

@Andre
I guess this would be only possible with extreme moves in volatile stocks. If a stocks was down say 7-8% in past 1 month , 20% positive move in last 5 days would definitely cover it up. Only a large move in past one month such as 15-16% down and then 20% up in past 5 days would satisfy this condition.

@Max - Russell 2000 historical prices are available at https://finance.yahoo.com/q/hp?s=^RUT as an HTML page with a table containing dates and open, high, low, and close levels, volumes, and adjusted close levels. You can extract the data with BeautifulSoup.

At the bottom of the page, there is a link containing a Web query for the same data in CSV format. When you reverse-engineer the query format, you should be able to load that data for any given date range via Fetcher.

Frankly, I think index levels are such a basic necessity that Quantopian should provide them as non-tradable Securities.