#MachineLearning #AlgortihmicTrading #StockMarketAutomatedTrading #LogisticRegression #Boosting
Predictions and Suggestions from a machine learning
based Algorithmic trading
An algorithm is a specific set of clearly defined
instructions aimed to carry out a task or process.
Algorithmic trading (automated trading, black-box
trading, or simply algo-trading) is the process of using computers programmed
to follow a defined set of instructions for placing a trade in order to
generate profits at a speed and frequency that is impossible for a human
trader. The defined sets of rules are based on timing, price, quantity or any
mathematical model. Apart from profit opportunities for the trader,
algo-trading makes markets more liquid and makes trading more systematic by
ruling out emotional human impacts on trading activities.
We can create a Regression formula like below :
The dependent variable is the Return on capital invested and can be run across all stocks.
Error term ei can be boosted using Boosting Algos and thus increasing the prediction accuracy.
Now how to choose your Variables and what can be the
ideal STOCK Equation :
YOY Quarterly sales growth > 15 and
YOY Quarterly profit growth > 20 and
Net Profit latest quarter > 1 and
G Factor >= 7 and
Net Profit latest quarter > .33 AND
Other income latest quarter < Net Profit latest
quarter * .5 AND
Net Profit preceding year quarter <= 0 AND
Expected quarterly net profit > 0 AND
Sales latest quarter > Sales preceding year
quarter AND
Return on invested capital > 25 and
Earnings yield > 15 and
Book value > 0 AND
Market Capitalization > 15
AND
Graham Number > Current price AND
PB X PE <=22.50 AND
PEG Ratio >0 AND
PEG Ratio <1 .5="" and="" o:p="">1>
Altman Z Score >=2.5 AND
Sales growth 5Years >25 AND
Profit growth 5Years >15 AND
Current ratio >2 AND
Market Capitalization >250 AND
Sales >100
AND
Piotroski score > 7
AND
Dividend yield > 2 AND
Average 5years dividend > 0 AND
Dividend last year > Average 5years dividend AND
Profit after tax > Net Profit last year * .8 AND
Dividend last year > .35 AND
( Profit growth 3Years > 10 OR
Profit growth 5Years > 10 OR
Profit growth 7Years > 10 )
OR
(Market Capitalization > 3000) AND
(Average return on equity 10Years Years > 20) AND
(Debt to equity < 1.5) AND
(Interest Coverage Ratio > 2) AND
( PEG Ratio <= 1) AND
(Profit growth 5Years > 20)
AND
YOY Quarterly sales growth > 40 and
YOY Quarterly profit growth > 40 and
Average return on capital employed 3Years >30 and
Price to Earning <6 o:p="">6>
OR
Sales growth 10Years > 10 AND
Profit growth 10Years > 12 AND
OPM 10Year > 12 AND
Debt to equity < 0.5 AND
Current ratio > 1.5 AND
Altman Z Score > 3 AND
Average return on equity 10Years > 12 AND
Average return on capital employed 10Years >12 AND
Return on invested capital > 15 AND
Sales last year / Total Capital Employed > 2 AND
Average dividend payout 3years >15
AND
PEG Ratio <1 and="" o:p="">1>
Sales > 500 AND
Price to Earning < 40 AND
Profit growth > 20 AND
Debt to equity < 0.2 AND
Price to Cash Flow > 5
OR
EPS last year >20 AND
Debt to equity <.1 AND
Average return on capital employed 5Years >35 AND
Market Capitalization >500 AND
OPM 5Year >15
AND
Net Profit latest quarter > Net Profit preceding
quarter AND
Net Profit preceding quarter > Net profit 2quarters
back AND
Net profit 2quarters back > Net profit 3quarters
back
AND
EPS latest quarter > 1.2 * EPS preceding year
quarter AND
EPS latest quarter > 0 AND
YOY Quarterly sales growth > 25 AND
EPS last year > EPS preceding year AND
EPS > EPS last year AND
Profit growth 3Years > 25 AND
Return on equity > 17 AND
Down from 52w high < 15 AND
Market Capitalization > 100
AND
Price to Earning >0 and Price to Earning <10 5years="" and="" equity="" growth="" on="" return="">10 and Dividend yield >1 and Return on
capital employed >10 10>
AND
Profit growth 5Years > Sales growth 5Years AND
Sales growth 5Years > 3 AND
Return on equity > 15 AND
Working capital 5Years back < 0
AND
Price to Earning >0 and Return on equity 5years
growth > 5 and Dividend yield >0
Note : DEBT reacts inversely to the equation . Term period will be a spread over last 15 to 20 Years.
Now , applying boosting algorithm ( like XGBoost) you
can reduce the error coefficients.
Based on the above equation and a little variation
choosing a flattened NN( Neural Network ) below stocks can be looked upon for
Indian stock market.
1) RELIANCE
INDUSTRIES
2) DCB
BANK
3) KAJARIA
CERAMICS
4) INFOSYS
5) INDO
COUNT INDUSTRIES