Hi all,
Just a general question on how quickly you guy scale up your bets.
I've finally created something which is really awesome. I'm currently using 30p stakes and it brings in an average of 10£ a day (been running it for days now). There's a mixture of other things in there too which I'm experimenting with, but really quite consistent with low losses but high returns and it can be very scalable.
I'm thinking keep it on 30ps for a month and then add another 30p.
what do you guys recommend ?
Also a little thanks to James, as he was kind enough to helped me with few learning curves!
Scaling up
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eatyourgreens
- Posts: 275
- Joined: Sun Feb 18, 2018 12:53 am
hi,
you could try using a % of your bank and scale up automatically, ie, start with a £30 bank and use 1% for staking, as your bank grows your stake grows
......just a thought!
good luck
you could try using a % of your bank and scale up automatically, ie, start with a £30 bank and use 1% for staking, as your bank grows your stake grows
......just a thought!
good luck
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ShaunWhite
- Posts: 10352
- Joined: Sat Sep 03, 2016 3:42 am
Depends on your confidence, give GPT your results and ask it to calculate these 3 statistics or diy in Excel.... I look for a t-stat above 6 and a p-value < 0.01. Those will give you a guide about how hard to push it.
How much of the variance in the data is explained by the model
R² Value Interpretation
≥ 0.90 Very strong relationship —the model explains almost all variance
0.75 – 0.90 Strong relationship —good explanatory power
0.50 – 0.75 Moderate relationship —some noise, but still useful
0.25 – 0.50 Weak relationship —model only explains part of the variance
≤ 0.25 Very weak or no relationship —mostly random noise
How far the observed mean is from zero, scaled by variance and sample size.
t-Statistic Interpretation
≥ 10 Extremely strong evidence —almost certain the effect is real
6 – 10 Very strong evidence —high confidence that this isn’t noise
3 – 6 Strong evidence —significant, but not bulletproof
2 – 3 Moderate evidence —likely real, but worth further validation
1 – 2 Weak evidence —could be noise or require more data
≤ 1 No real evidence —likely just random fluctuations
The probability of seeing this result if the true effect was zero.
p-value Interpretation
≤ 0.001 Extremely strong evidence against randomness —highly significant
0.001 – 0.01 Very strong evidence —unlikely due to chance
0.01 – 0.05 Strong evidence —statistically significant
0.05 – 0.10 Some evidence, but weaker —possibly real, but less convincing
0.10 – 0.20 Weak evidence —could be noise, needs more data
≥ 0.20 No real evidence of a meaningful effect —likely random
How much of the variance in the data is explained by the model
R² Value Interpretation
≥ 0.90 Very strong relationship —the model explains almost all variance
0.75 – 0.90 Strong relationship —good explanatory power
0.50 – 0.75 Moderate relationship —some noise, but still useful
0.25 – 0.50 Weak relationship —model only explains part of the variance
≤ 0.25 Very weak or no relationship —mostly random noise
How far the observed mean is from zero, scaled by variance and sample size.
t-Statistic Interpretation
≥ 10 Extremely strong evidence —almost certain the effect is real
6 – 10 Very strong evidence —high confidence that this isn’t noise
3 – 6 Strong evidence —significant, but not bulletproof
2 – 3 Moderate evidence —likely real, but worth further validation
1 – 2 Weak evidence —could be noise or require more data
≤ 1 No real evidence —likely just random fluctuations
The probability of seeing this result if the true effect was zero.
p-value Interpretation
≤ 0.001 Extremely strong evidence against randomness —highly significant
0.001 – 0.01 Very strong evidence —unlikely due to chance
0.01 – 0.05 Strong evidence —statistically significant
0.05 – 0.10 Some evidence, but weaker —possibly real, but less convincing
0.10 – 0.20 Weak evidence —could be noise, needs more data
≥ 0.20 No real evidence of a meaningful effect —likely random
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ShaunWhite
- Posts: 10352
- Joined: Sat Sep 03, 2016 3:42 am