ML Quantitative Analysis

Objectives
1) Can I achieve a risk-adjusted excess returns using my model?
a) the maximum return and minimum risk
2) Input: My model
a) Uses range bars resolution .xls
i) SPY 10 to 400 ticks
ii) ES 4 to 160ticks
b) Six indicators
i) Custom Indicators
(1) Hull moving average series
(2) Mgapsum
(3) TGapsum
(4) MACD- modified
ii) Regular indicators
(1) Slow Stochastics
(2) Double Slow stochastic
3) Data
a) I have already presented the data from my model for spy and es from 1997
b) Use the data set for training the model
4) Trading
a) Emini
b) Spy options
5) Does this model produce a trading strategy to attain goal # 1?
6) Data Preprocessing
a) Find a trading strategy using these six technical indicators for each of the 42 resolution
i) Then ML to train using all 42 resolutions at all times- (n)Composite strategies
b) Find Sell and Buy signals from SPY and ES
c) Select Trading Strategy using rank functions
i) Average Rate of Returns
ii) Trading Frequency
iii) Maximum Drawdown
iv) Best sharp ratio
d) Select top (nn) strategies for implementation
i) Long and short
(1) Entry and exit

7) Processed trading STRATEGIES
a) Background python to process the live tick data using the same six indicators and apply the strategies with highest ranking
b) If the objective # 1 is attainable then go to implantation phase
8) Implementation phase:
a) Apply to Processed trading STRATEGIES
b) Multicharts
i) Trade
(1) SPY Options
(2) Think or swim platform
ii) ES:
(1) Tradestation platform
About the recuiter
Member since Mar 14, 2020
Sandeep Jain
from California, United States

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