Random Forest as best approach to this problem?
I am studying ML and want to practice building a model to predict stock market returns for the next day, for example based on price and volume of the preceding days.
The current values I have for each day:
M = [[Price at day-1, price at day 0, return at day+1]
[Volume at day-1, volume at day 0, return at day+1]]
I would like to find rules, that define the ranges of price at day-1 and price at day 0 to predict the price at day+1 in the following way:
If price is below 500 for day-1 AND price is above 200 at day 0
The average return at day+1 is 1.05 (5%)
or
If price is below 500 for day-1 AND price is above 200 at day 0
AND If volume is above 200 for day-1 AND volume is below 800 at day 0
The average return at day+1 is 1.09 (9%)
I am not looking for any solutions but just for the general strategy how to approach this problem.
Is ML useful here at all, or would it be better done using a for loop iterating through all values to find the rules? I am considering random forest, would that be a viable option?
machine-learning regression random-forest
edited Nov 18 '18 at 8:17
Anony-Mousse
57.6k796159
asked Nov 18 '18 at 5:47
Franc WeserFranc Weser
16417
add a comment |
I am studying ML and want to practice building a model to predict stock market returns for the next day, for example based on price and volume of the preceding days.
The current values I have for each day:
M = [[Price at day-1, price at day 0, return at day+1]
[Volume at day-1, volume at day 0, return at day+1]]
I would like to find rules, that define the ranges of price at day-1 and price at day 0 to predict the price at day+1 in the following way:
If price is below 500 for day-1 AND price is above 200 at day 0
The average return at day+1 is 1.05 (5%)
or
If price is below 500 for day-1 AND price is above 200 at day 0
AND If volume is above 200 for day-1 AND volume is below 800 at day 0
The average return at day+1 is 1.09 (9%)
I am not looking for any solutions but just for the general strategy how to approach this problem.
Is ML useful here at all, or would it be better done using a for loop iterating through all values to find the rules? I am considering random forest, would that be a viable option?
machine-learning regression random-forest
edited Nov 18 '18 at 8:17
Anony-Mousse
57.6k796159
asked Nov 18 '18 at 5:47
Franc WeserFranc Weser
16417
I would say stats.stackexchange.com is a better option to make your particular question.
– Franco Piccolo
Nov 18 '18 at 6:52
add a comment |
I am studying ML and want to practice building a model to predict stock market returns for the next day, for example based on price and volume of the preceding days.
The current values I have for each day:
M = [[Price at day-1, price at day 0, return at day+1]
[Volume at day-1, volume at day 0, return at day+1]]
I would like to find rules, that define the ranges of price at day-1 and price at day 0 to predict the price at day+1 in the following way:
If price is below 500 for day-1 AND price is above 200 at day 0
The average return at day+1 is 1.05 (5%)
or
If price is below 500 for day-1 AND price is above 200 at day 0
AND If volume is above 200 for day-1 AND volume is below 800 at day 0
The average return at day+1 is 1.09 (9%)
I am not looking for any solutions but just for the general strategy how to approach this problem.
Is ML useful here at all, or would it be better done using a for loop iterating through all values to find the rules? I am considering random forest, would that be a viable option?
machine-learning regression random-forest
edited Nov 18 '18 at 8:17
Anony-Mousse
57.6k796159
asked Nov 18 '18 at 5:47
Franc WeserFranc Weser
16417
I am studying ML and want to practice building a model to predict stock market returns for the next day, for example based on price and volume of the preceding days.
The current values I have for each day:
M = [[Price at day-1, price at day 0, return at day+1]
[Volume at day-1, volume at day 0, return at day+1]]
I would like to find rules, that define the ranges of price at day-1 and price at day 0 to predict the price at day+1 in the following way:
If price is below 500 for day-1 AND price is above 200 at day 0
The average return at day+1 is 1.05 (5%)
or
If price is below 500 for day-1 AND price is above 200 at day 0
AND If volume is above 200 for day-1 AND volume is below 800 at day 0
The average return at day+1 is 1.09 (9%)
I am not looking for any solutions but just for the general strategy how to approach this problem.
Is ML useful here at all, or would it be better done using a for loop iterating through all values to find the rules? I am considering random forest, would that be a viable option?
machine-learning regression random-forest
machine-learning regression random-forest
edited Nov 18 '18 at 8:17
Anony-Mousse
57.6k796159
asked Nov 18 '18 at 5:47
Franc WeserFranc Weser
16417
edited Nov 18 '18 at 8:17
Anony-Mousse
57.6k796159
asked Nov 18 '18 at 5:47
Franc WeserFranc Weser
16417
edited Nov 18 '18 at 8:17
Anony-Mousse
57.6k796159
edited Nov 18 '18 at 8:17
Anony-Mousse
57.6k796159
edited Nov 18 '18 at 8:17
Anony-Mousse
57.6k796159
57.6k796159
asked Nov 18 '18 at 5:47
Franc WeserFranc Weser
16417
asked Nov 18 '18 at 5:47
Franc WeserFranc Weser
16417
asked Nov 18 '18 at 5:47
Franc WeserFranc Weser
16417
16417
I would say stats.stackexchange.com is a better option to make your particular question.
– Franco Piccolo
Nov 18 '18 at 6:52
add a comment |
I would say stats.stackexchange.com is a better option to make your particular question.
– Franco Piccolo
Nov 18 '18 at 6:52
I would say stats.stackexchange.com is a better option to make your particular question.
– Franco Piccolo
Nov 18 '18 at 6:52
I would say stats.stackexchange.com is a better option to make your particular question.
– Franco Piccolo
Nov 18 '18 at 6:52
add a comment |
1 Answer
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Yes. Random forests can be used for regression.
They will have a tendency to predict the average though, because of the forest aggregation. Regular decision trees may be a bit more "decisive".
answered Nov 18 '18 at 8:17
Anony-MousseAnony-Mousse
57.6k796159
add a comment |
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Yes. Random forests can be used for regression.
They will have a tendency to predict the average though, because of the forest aggregation. Regular decision trees may be a bit more "decisive".
answered Nov 18 '18 at 8:17
Anony-MousseAnony-Mousse
57.6k796159
add a comment |
Yes. Random forests can be used for regression.
They will have a tendency to predict the average though, because of the forest aggregation. Regular decision trees may be a bit more "decisive".
answered Nov 18 '18 at 8:17
Anony-MousseAnony-Mousse
57.6k796159
add a comment |
Yes. Random forests can be used for regression.
They will have a tendency to predict the average though, because of the forest aggregation. Regular decision trees may be a bit more "decisive".
answered Nov 18 '18 at 8:17
Anony-MousseAnony-Mousse
57.6k796159
Yes. Random forests can be used for regression.
They will have a tendency to predict the average though, because of the forest aggregation. Regular decision trees may be a bit more "decisive".
answered Nov 18 '18 at 8:17
Anony-MousseAnony-Mousse
57.6k796159
answered Nov 18 '18 at 8:17
Anony-MousseAnony-Mousse
57.6k796159
answered Nov 18 '18 at 8:17
Anony-MousseAnony-Mousse
57.6k796159
answered Nov 18 '18 at 8:17
Anony-MousseAnony-Mousse
57.6k796159
57.6k796159
add a comment |
add a comment |
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I would say stats.stackexchange.com is a better option to make your particular question.
– Franco Piccolo
Nov 18 '18 at 6:52