KAVAETH
Kava.io / Ethereum
crypto

It took a really long time to find a strategy that works without making any personal analysis - just the indicators doing the work for you.
**Abstract**
I conducted a series of trades operating between August and September (\~2 months). I decided with 200 trades to create a robust sample size for reliability.
Listed on BINANCE, 26 cryptocurrencies were selected as part of the 200 trades via simple random sampling, with some stratification variability.
* SHELLUSDT.P, STRKUSDT.P, NOTUSDT.P, MYROUSDT.P, BRETTUSDT.P, MOODENGUSDT.P, DEGENUSDT.P, SPELLUSDT.P, BIGTIMEUSDT.P, PIPPINUSDT.P, ACHUSDT.P, OPUSDT.P, KAVAUSDT.P, GALAUSDT.P, DOGSUSDT.P, TRUMPUSDT.P, WUSDT.P, VETUSDT.P, TURBOUSDT.P, AAVEUSDT.P, ROSEUSDT.P, PENGUUSDT.P, REZUSDT.P, DOGEUSDT.P, XRPUSDT.P, and ENAUSDT.P
I started with $100.00 as my initial capital.
* RRR = 1:1
* Leverage: 5x
Null hypothesis: The true win-rate is equal to random chance; = 0.50.
Alternative hypothesis: The true win-rate is greater than random chance; > 0.50.
**Results**
* Win-rate: 65.5% in 200 trades
* Net profit: +1552% (i.e., $100.00 -> $1552.41)
**Conclusion**
Test for statistical significance: one-sample proportion z-test
Level of significance: 5%
p-value ≈ 5.82 × 10⁻⁶
Interpretation: We reject the null hypothesis. The strategy's observed win-rate is statistically significantly higher than 50% at conventional significance levels (p = 0.00000582, 95% CI (0.587, 0.717)).
\------------------------------------------------------------------------------------------------------------------
Ok so now the abstract is done (just know that my results are not random). Below presents several charts that highlight interesting visuals.
Figure 1: Observed cumulative win-rate progression
[Note to viewers: 0.1 = 10% WR; 0.2 = 20% WR; etc](https://preview.redd.it/nq79xmbop4qf1.png?width=736&format=png&auto=webp&s=2448b223f7e6717b6b8444fc718af1e0f6045c8a)
Figure 2: Observed capital progression
[In short, net profit is 16 times the initial.](https://preview.redd.it/fvwzvffwp4qf1.png?width=735&format=png&auto=webp&s=649ccd0a438b7d0d42dafc7ffe9433f8b8bb46f7)
Figure 3: Distribution of overall win rate (bootstrapped)
[Note: 5000 refers to the number of stratified bootstrap iterations \(sampling with replacement within each crypto symbol\) \/\/ We can notice that distribution is relatively non-skewed.](https://preview.redd.it/ynz2dxa2q4qf1.png?width=651&format=png&auto=webp&s=f1ce68c93945425ae416090fcf1ebdec9699545c)
Figure 4: Distribution of capital (bootstrapped)
[The bootstrap distribution of final capital is skewed. The dashed lines represent the boundaries of 95% CI.](https://preview.redd.it/fq65dg9iq4qf1.png?width=693&format=png&auto=webp&s=95eb761c224285619f89e9851df42f4b2045f237)
Figure 5: Stratified per-symbol win-rate (bootstrapped)
[This one is interesting. It is important to note that ENAUSDT.P is only a sample size of 1, which happened to be a loss.](https://preview.redd.it/fsuvh4rsq4qf1.png?width=608&format=png&auto=webp&s=bbf4e68d9440766c716f8bb36de1cfd7bfe30ca2)
**Limitations**
Firstly, the indicators used are limited by TradingView's limitations. Firstly, the replay function is limited on a symbol-by-symbol basis. While unknown, some symbols can only be replayed a day before the present, while others multiple days before the present. This led to some symbols being traded more than others.
Secondly, this strategy was done by manual backtesting via TradingView's replay function. Although objectivity was attempted to be maintained at all costs, they may still be possibility of potential losses that were missed during the backtesting period. However, this is unlikely due to the statistically significant result as shown by the p-value.
**My notes**
I kinda wanted to do this for a while and share to the community that daytrading any cryptocurrency is possible. Thoughts and feedback are appreciated!

It took a really long time to find a strategy that works without making any personal analysis - just the indicators doing the work for you.
**Abstract**
I conducted a series of trades operating between August and September (\~2 months). I decided with 200 trades to create a robust sample size for reliability.
Listed on BINANCE, 26 cryptocurrencies were selected as part of the 200 trades via simple random sampling, with some stratification variability.
* SHELLUSDT.P, STRKUSDT.P, NOTUSDT.P, MYROUSDT.P, BRETTUSDT.P, MOODENGUSDT.P, DEGENUSDT.P, SPELLUSDT.P, BIGTIMEUSDT.P, PIPPINUSDT.P, ACHUSDT.P, OPUSDT.P, KAVAUSDT.P, GALAUSDT.P, DOGSUSDT.P, TRUMPUSDT.P, WUSDT.P, VETUSDT.P, TURBOUSDT.P, AAVEUSDT.P, ROSEUSDT.P, PENGUUSDT.P, REZUSDT.P, DOGEUSDT.P, XRPUSDT.P, and ENAUSDT.P
I started with $100.00 as my initial capital.
* RRR = 1:1
* Leverage: 5x
Null hypothesis: The true win-rate is equal to random chance; = 0.50.
Alternative hypothesis: The true win-rate is greater than random chance; > 0.50.
**Results**
* Win-rate: 65.5% in 200 trades
* Net profit: +1552% (i.e., $100.00 -> $1552.41)
**Conclusion**
Test for statistical significance: one-sample proportion z-test
Level of significance: 5%
p-value ≈ 5.82 × 10⁻⁶
Interpretation: We reject the null hypothesis. The strategy's observed win-rate is statistically significantly higher than 50% at conventional significance levels (p = 0.00000582, 95% CI (0.587, 0.717)).
\------------------------------------------------------------------------------------------------------------------
Ok so now the abstract is done (just know that my results are not random). Below presents several charts that highlight interesting visuals.
Figure 1: Observed cumulative win-rate progression
[Note to viewers: 0.1 = 10% WR; 0.2 = 20% WR; etc](https://preview.redd.it/nq79xmbop4qf1.png?width=736&format=png&auto=webp&s=2448b223f7e6717b6b8444fc718af1e0f6045c8a)
Figure 2: Observed capital progression
[In short, net profit is 16 times the initial.](https://preview.redd.it/fvwzvffwp4qf1.png?width=735&format=png&auto=webp&s=649ccd0a438b7d0d42dafc7ffe9433f8b8bb46f7)
Figure 3: Distribution of overall win rate (bootstrapped)
[Note: 5000 refers to the number of stratified bootstrap iterations \(sampling with replacement within each crypto symbol\) \/\/ We can notice that distribution is relatively non-skewed.](https://preview.redd.it/ynz2dxa2q4qf1.png?width=651&format=png&auto=webp&s=f1ce68c93945425ae416090fcf1ebdec9699545c)
Figure 4: Distribution of capital (bootstrapped)
[The bootstrap distribution of final capital is skewed. The dashed lines represent the boundaries of 95% CI.](https://preview.redd.it/fq65dg9iq4qf1.png?width=693&format=png&auto=webp&s=95eb761c224285619f89e9851df42f4b2045f237)
Figure 5: Stratified per-symbol win-rate (bootstrapped)
[This one is interesting. It is important to note that ENAUSDT.P is only a sample size of 1, which happened to be a loss.](https://preview.redd.it/fsuvh4rsq4qf1.png?width=608&format=png&auto=webp&s=bbf4e68d9440766c716f8bb36de1cfd7bfe30ca2)
**Limitations**
Firstly, the indicators used are limited by TradingView's limitations. Firstly, the replay function is limited on a symbol-by-symbol basis. While unknown, some symbols can only be replayed a day before the present, while others multiple days before the present. This led to some symbols being traded more than others.
Secondly, this strategy was done by manual backtesting via TradingView's replay function. Although objectivity was attempted to be maintained at all costs, they may still be possibility of potential losses that were missed during the backtesting period. However, this is unlikely due to the statistically significant result as shown by the p-value.
**My notes**
I kinda wanted to do this for a while and share to the community that daytrading any cryptocurrency is possible. Thoughts and feedback are appreciated!