finding winning strategy
finding winning strategy
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Alessio Martone · External communityPost link
External question — Data Science Stack Exchange
Author: Alessio Martone
Original post: https://datascience.stackexchange.com/questions/98294
License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/
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For a given asset, I have simulations of the price and implied volatility for T periods in N scenarios. Furthermore, assuming that I know the value of the risk-free asset (and the dividend yield), I can calculate the price of the calls and puts options on different maturities and strikes (at least with Black-Scholes). I would like to build a program that autonomously selects a winning strategy on average in the N scenarios - combining the underlying, call and put so that -the expected profit in the different scenarios is maximized. The aim is to have a rule of this type: 'when the volatility is under 15% and the stock had fallen in the last 3 days, buy the put X (maturity, moneyness) and the underlying'. Could you suggest any technique?
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Nicolas Martin · External communityPost link
External answer — Data Science Stack Exchange
Author: Nicolas Martin
Original post: https://datascience.stackexchange.com/a/98309
License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/
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You have plenty of potential solutions, the easiest one is decision tree models like Random Forest.
Then, you can try more complex models based on LSTM or Reinforcement Learning.
Here are 2 code examples using RF:
https://python.plainenglish.io/how-to-predict-stock-prices-change-with-random-forest-in-python-f707e101d5c4
https://tcoil.info/predict-stock-price-trend-with-machine-learning-random-forest-scikit-python/
Here are 2 code examples using RL:
https://www.analyticsvidhya.com/blog/2020/10/reinforcement-learning-stock-price-prediction/
https://towardsdatascience.com/deep-reinforcement-learning-for-automated-stock-trading-f1dad0126a02
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Quoted from Forex.com.bd-Editorial External question — Data Science Stack Exchange Author: Alessio Martone Source score (net votes, not local likes): 1 Original post: https://datascience.stackexchange.com/questions/98294 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. For a given asset, I have simulations of the price and implied volatility for T periods in N scenarios. Furthermore, assuming that I know the value of the risk-free asset (and the dividend yield), I can calculate the price of the calls and puts options on different maturities and strikes (at least with Black-Scholes). I would like to build a program that autonomously selects a winning strategy on average in the N scenarios - combining the underlying, call and put so that -the expected profit in the different scenarios is maximized. The aim is to have a rule of this type: 'when the volatility is under 15% and the stock had fallen in the last 3 days, buy the put X (maturity, moneyness) and the underlying'. Could you suggest any technique?
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