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and we are back to 0.5xcorr + 2xmmc
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@BorisVSchmid
BorisVSchmid committed Dec 20, 2023
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66 changes: 36 additions & 30 deletions optimize_stake.R
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Expand Up @@ -31,8 +31,8 @@ options(scipen = 999)

## Vlad Input Data and Settings
#
NMR <- 142.6
model_df <- read_excel("Optimize-Me-30nov2023.xlsx") # Read in the list of model names and their start (and possibly end) round.
NMR <- 142.6 # sum(model_df$Notes) 142.6
colnames(model_df) <- c("name","start","notes")
current_round <- Rnumerai::get_current_round()
oldest_round <- min(model_df$start)
Expand All @@ -42,7 +42,7 @@ oldest_round <- min(model_df$start)
## Collect daily scores, and filter for models that have at least 60 data points (you shouldn't use Vlad for models with less data points)
# I have set the starting round at 339, as that is the first round of the daily tournament, but you are free to change that round back and forth.
#
daily_data <- build_RAW(model_df, MinfromRound = 339, corr_multiplier = 0.5, mmc_multiplier = 3)
daily_data <- build_RAW(model_df, MinfromRound = 339, corr_multiplier = 0.5, mmc_multiplier = 2)
daily_data <- daily_data[,colnames(daily_data) %in% colnames(daily_data)[colSums(!is.na(daily_data)) > 60]]


Expand Down Expand Up @@ -79,9 +79,9 @@ ggsave("model-performances.png",scale=1,width=15,height=15)
#
# numerai_perf <- left_join(model_df,dplyr::select(model_stats,name,mean,drawdown)) %>% na.omit()
# sum(numerai_perf$mean * numerai_perf$notes) / sum(numerai_perf$notes) # weighted mean
benchmark_mean <- 0.00653
benchmark_mean <- 0.00532
# sum(numerai_perf$drawdown * numerai_perf$notes) / sum(numerai_perf$notes) # weighted drawdown
benchmark_drawdown <- 0.841
benchmark_drawdown <- 0.755
#
# Tweak these thresholds as you feel is most appropriate
good_models <- model_stats %>% dplyr::filter(mean > benchmark_mean * 0.8, drawdown < benchmark_drawdown / 0.8)
Expand Down Expand Up @@ -137,35 +137,41 @@ condensed$stake <- round(condensed$stake)
#
kable(combined,digits=3)

# |name |weight |stake |mean |Cov |CVaR |VaR |samplesize |starting_round |
# |:-------------------|:------|:-----|:------|:------|:------|:------|:----------|:--------------|
# |INTEGRATION_TEST |0.479 |68 |0.0196 |0.0201 |0.0117 |0.0079 |279 |339 |
# |V42_LGBM_CLAUDIA20 |0.221 |32 | | | | | | |
# |V42_LGBM_TEAGER20 |0.076 |11 | | | | | | |
# |V42_LGBM_TEAGER60 |0.162 |23 | | | | | | |
# |V4_LGBM_VICTOR20 |0.061 |9 | | | | | | |
# | | | | | | | | | |
# |INTEGRATION_TEST |0.451 |64 |0.019 |0.0197 |0.0127 |0.0089 |279 |340 |
# |V41_LGBM_CAROLINE20 |0.049 |7 | | | | | | |
# |V42_LGBM_CLAUDIA20 |0.196 |28 | | | | | | |
# |V42_LGBM_TEAGER20 |0.072 |10 | | | | | | |
# |V42_LGBM_TEAGER60 |0.169 |24 | | | | | | |
# |V4_LGBM_VICTOR20 |0.064 |9 | | | | | | |
# | | | | | | | | | |
# |name |weight |stake |mean |Cov |CVaR |VaR |samplesize |starting_round |
# |:------------------|:------|:-----|:------|:------|:------|:------|:----------|:--------------|
# |INTEGRATION_TEST |0.439 |63 |0.014 |0.0157 |0.0118 |0.0074 |289 |339 |
# |V41_LGBM_XERXES20 |0.051 |7 | | | | | | |
# |V42_LGBM_CLAUDIA20 |0.182 |26 | | | | | | |
# |V42_LGBM_ROWAN20 |0.064 |9 | | | | | | |
# |V42_LGBM_TEAGER20 |0.1 |14 | | | | | | |
# |V42_LGBM_TEAGER60 |0.117 |17 | | | | | | |
# |V4_LGBM_VICTOR20 |0.046 |7 | | | | | | |
# | | | | | | | | | |
# |INTEGRATION_TEST |0.427 |61 |0.0142 |0.0163 |0.0129 |0.0084 |289 |340 |
# |V41_EXAMPLE_PREDS |0.051 |7 | | | | | | |
# |V42_LGBM_CLAUDIA20 |0.163 |23 | | | | | | |
# |V42_LGBM_ROWAN20 |0.063 |9 | | | | | | |
# |V42_LGBM_TEAGER20 |0.099 |14 | | | | | | |
# |V42_LGBM_TEAGER60 |0.152 |22 | | | | | | |
# |V4_LGBM_VICTOR20 |0.045 |6 | | | | | | |
# | | | | | | | | | |


## Printout combined portfolio across starting rounds (equal weight for each starting points for now)
# For Numer.ai's models, the two timepoints (starting_round 339 and 340) don't matter much, so merging them changes little.
#
kable(condensed,digits=3)

# name | weight| stake|mean |Cov |CVaR |VaR |samplesize |
# |:-------------------|------:|-----:|:------|:------|:-----|:------|:----------|
# |INTEGRATION_TEST | 0.465| 66|0.0193 |0.0199 |0.012 |0.0089 |279 |
# |V41_LGBM_CAROLINE20 | 0.024| 4| | | | | |
# |V42_LGBM_CLAUDIA20 | 0.209| 30| | | | | |
# |V42_LGBM_TEAGER20 | 0.074| 10| | | | | |
# |V42_LGBM_TEAGER60 | 0.166| 24| | | | | |
# |V4_LGBM_VICTOR20 | 0.062| 9| | | | | |
# |name | weight| stake|mean |Cov |CVaR |VaR |samplesize |
# |:------------------|------:|-----:|:------|:-----|:------|:------|:----------|
# |INTEGRATION_TEST | 0.433| 62|0.0141 |0.016 |0.0123 |0.0078 |289 |
# |V41_EXAMPLE_PREDS | 0.026| 4| | | | | |
# |V41_LGBM_XERXES20 | 0.026| 4| | | | | |
# |V42_LGBM_CLAUDIA20 | 0.173| 25| | | | | |
# |V42_LGBM_ROWAN20 | 0.064| 9| | | | | |
# |V42_LGBM_TEAGER20 | 0.100| 14| | | | | |
# |V42_LGBM_TEAGER60 | 0.135| 20| | | | | |
# |V4_LGBM_VICTOR20 | 0.046| 7| | | | | |


# This is numer.ai's current stake distribution for models with a samplesize > 100. (nov 2023)
Expand All @@ -177,8 +183,8 @@ numerai_stake$weight <- numerai_stake$stake / sum(numerai_stake$stake)
# You can calculate its return by feeding virtual_returns the daily_data and a dataframe of c(name, weight)
kable(virtual_returns(daily_data,numerai_stake), digits=4)

# | mean| Cov| CVaR| VaR| samplesize|
# |------:|------:|------:|-----:|----------:|
# | 0.0066| 0.0257| 0.0486| 0.038| 278|
# | mean| Cov| CVaR| VaR| samplesize|
# |------:|----:|-----:|------:|----------:|
# | 0.0054| 0.02| 0.038| 0.0298| 288|

# So, using Vlad suggests a higher account-wide return is possible against a lower CVaR and VaR. Time for stake management? :-).

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