https://quantpedia.com/Screener/Details/307
-premium quandl data necessary for upcoming earnings announcements: https://www.quandl.com/databases/ZEA/documentation/a-earnings-announcements

https://quantpedia.com/Screener/Details/118

https://quantpedia.com/Screener/Details/237

-need access to the Institutional Brokers Estimate System (I/B/E/S) database to compute for each firm the mean absolute difference scaled by an indicator of earnings uncertainty (see page 26 in the source academic paper for detailed methodology)

https://quantpedia.com/Screener/Details/75
-will need to manually create the data source containing the FOMC meeting dates

https://quantpedia.com/Screener/Details/162

https://quantpedia.com/Screener/Details/80

-need volume of the previous 16 announcement months divided by the total volume in the previous 48 months. Not all companies release earnings announcements on same months.
-need a data source that has the past announcements of companies

https://quantpedia.com/Screener/Details/113

https://quantpedia.com/Screener/Details/155

https://quantpedia.com/Screener/Details/198

Need front rollover continuous adjusted futures prices (Premium data from Quandl)

Missing the implementation of Quantpedia algorithm 43 Gold Market Timing

https://quantpedia.com/Screener/Details/140
-a dataset of Ramadan dates will need to be manually created and read

https://quantpedia.com/Screener/Details/229

https://quantpedia.com/Screener/Details/77
CSRP database stocks: NYSE from 1926, AMEX from 1962, and NASDAQ from 1972

https://quantpedia.com/Screener/Details/38
-needs annual change in fundamental data, so will need a warm up of a year. Won't be implementable in live trading.
-reference this strategy general format: https://www.quantconnect.com/tutorials/strategy-library/liquidity-effect-in-stocks

https://quantpedia.com/Screener/Details/71

Need the futures volume data

https://quantpedia.com/Screener/Details/114

https://quantpedia.com/Screener/Details/52

• will need to store fundamental data for the past year
  -this can be used as a general format: https://www.quantconnect.com/tutorials/strategy-library/liquidity-effect-in-stocks

Implementation of Quantpedia 51: Momentum - Short Term Reversal Strategy

https://quantpedia.com/Screener/Details/238

• need previous announcement dates: checks abnormal performance of these stocks during previous earnings announcement.

https://quantpedia.com/Screener/Details/55

https://quantpedia.com/Screener/Details/125

https://github.com/pythonnet/pythonnet/wiki/Various-debugging-scenarios-of-embedded-CPython

Should be possible to setup debugging with pythonnet in visual studio; need a tutorial for this

https://quantpedia.com/Screener/Details/67

https://quantpedia.com/Screener/Details/102

https://quantpedia.com/Screener/Details/152
-universe consists of all US REITs listed on markets. Can be determined in fine fundamental selection with fine.CompanyReference.IsREIT

There seems to be the outline of a tutorial on machine learning, but all the files are blank.

https://quantpedia.com/Screener/Details/136
-uses regression model which is calculated every month for all eligible stocks using the Fama and French three factors as independent variables.

https://quantpedia.com/Screener/Details/96

I believe "out" should changed to "our".

I tried finding this in the code, but I was unable to find the bottom content HTML.

https://quantpedia.com/Screener/Details/78

Start the implementation of 23: Momentum Effect Combined with Term Structure in Commodities

https://quantpedia.com/Screener/Details/91

https://quantpedia.com/Screener/Details/376

https://quantpedia.com/Screener/Details/83
-need a data source containing holidays (see market-hours-database.json)

https://quantpedia.com/Screener/Details/271

-need premium data for companies which announce stock repurchase program: https://www.quandl.com/data/EVB-EventVestor-Corporate-Buybacks

https://quantpedia.com/Screener/Details/88
The algorithm needs the 52-weeks high for all stocks in the universe
Only the adjusted close price is available in coarse and fine universe selection(daily high is not available).

https://quantpedia.com/Screener/Details/122

-need balance sheet data for the past year. can't request historical fine fundamental data.

https://quantpedia.com/Screener/Details/85
-universe consists of mutual funds with no entrance fees

https://quantpedia.com/Screener/Details/129
-need a basket of forex currencies
-need quandl data for national interest rates: https://www.quandl.com/data/BCB-Central-Bank-of-Brazil-Statistical-Database
Daily Forward Premium/discount Rate data in Quandl but only for US dollars

Started the algorithm and will be submitting PR

https://quantpedia.com/Screener/Details/207
-will need to create a basket of stocks of country ETFs and perform a CAPE ratio: the ratio of the real price of the equity market (adjusted for inflation) to the 10-year average of the country's equity index (again adjusted for inflation). The whole methodology is explained well on Shiller's home page (http://www.econ.yale.edu/~shiller/data.htm)

The universe consists of all stocks on the London Exchange and fundamental data is required to determine the trading signals. Data necessary is not available.

In the Iron Condor tutorial:
payoff_short_put = [min(premium_put_higher, -(k_put_higher-i+premium_put_higher)) for i in price]

But in the Iron Butterfly tutorial:
payoff_short_put = [min(premium_atm_put, -(k_atm-i-premium_atm_put)) for i in price]

Both are payoff for the short put position, but the first one is -(k-i + premium),
the second one is -(k-i - premium), which one is correct? Thanks.

Dollar Carry Trade

https://quantpedia.com/Screener/Details/100

https://quantpedia.com/Screener/Details/108
-need to use multiple exchanges. Manchester United is listed on NYSE
-need to use quandl data: https://www.quandl.com/data/SSE-Boerse-Stuttgart, https://www.quandl.com/data/XLON-London-Stock-Exchange-Prices

https://quantpedia.com/Screener/Details/199

-Return on assets calculated as quarterly earnings (Compustat quarterly item IBQ - income before extraordinary items) divided by one-quarter-lagged assets (item ATQ - total assets).
-will need to warm up for a quarter

Implementation Quantpedia 53 Sentiment and Style Rotation Effect in Stocks

https://quantpedia.com/Screener/Details/144
-the entry signal occurs if today’s close is greater than or equal to the highest close during the stock’s entire history