trading_strategy.txt

This file explains our trading stategy.  

Our purpose is to develop a trading stategy that construct and maintains a portfolio that yield the best absolute return.  

Constraints and desciplines: 
1. We will start with US$100 million and maintain a portfolio of about 50 stocks, i.e. $2 million on each stock on average.  
2. We maintain the overall stock position to be 100% (i.e. spend every dollar without cash balance).  No single stock will account for more than 15% of total value.  
3. We don't do margin financing or short selling.  
4. We do no more than 40 trades each day and assume no transaction cost.  
5. We either buy or sell certain numbers of a particular stock in one trading day.  No high-frequency trading.  
6. We transact at the end of a trading day, i.e. always use closing price.  

Benchmarks: 
1. We benchmark S&P500 and evaluate portfolio performance mainly based on the difference of portfolio return and S&P500 return over the same testing period.  
2. We also consider portfolio risk to be the largest retreats over 1M, 3M, 6M periods.  

Model: 
1. Our model is built on general stock performance with respect to a number of features without refering to any particular stock.  
2. The model is based on the following dependent variable Y and independent variable X: 
	Y: (1)  Daily adjusted closing price % change of 500 stocks from 2002 to 2019
	X: (4)  Last-twelve-month (LTM) price-to-sales (P/S), price-to-earning (P/E), price-to-book (P/B), price-to-cashflow (P/CF) ratio of each quarter
	   (3)  Quarterly revenue, adjusted earning, cash flow yoy growth rate (convert quarterly to daily)
	   (12) Prior day technical indicators of respective stock, including SMA, EMA, VWAP, MACD, STOCH, RSI, ADX, CCI, AROON, BBANDS, AD, OBV
	   (5)  Overnight stock index performance of other major stock markets, e.g. Nikkei, DAX, FTSE, HKSE, SHSE.  
	   (6)  Other market daily performance, including Tresaury Bond (1-yr, 3yr, 10-yr), forex market (USD/EUR, USD/JPY, USD/AUD)
	   (2)  Economic data, including monthly unemployment rate (converted to daily), monthly yoy GDP growth (converted to daily)
	Total 32 independent variables.  
3. We should have a dataframe of 35 columns (stock code, stock price, % price change + 32 features) and 2.25 million rows (500 stocks * 18 year * 250 trading days)
4. We run a linear regression model with daily numbers for 500 stocks and develop a model Y ~ 32X
5. Use 2002-2017 15-year data as training set, and test on 2018-2019.  

Corresponding trading Strategy: 
1. Everyday, our model shall tell us the estimated daily price change for each of the 500 stock based on testing data of 32 features.  
2. Sell the lowest 20 projected price change stock in the portfolio and receive XXX cash proceeds.  
3. Use the cash proceeds to buy the highest 20 projected price change stock with equal allocation.  
4. Sell 20 and buy 20 everyday, maintain a portfolio of 50 stocks.  
5. Calculate the absolute return from the portfolio and benchmark with S&P500.