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๐ŸŽ‰ Congratulations to all investors on updating the maximum historical profit! During recent corrections in crypto robot was gradually gaining profit, so, unfortunately, it didn't reach the minimum drawdown level allowing to increase risk according to my capital management strategy. And yesterday, after huge movement in Ethereum, caused by #ETHETFS news, historical profit was significantly updated. It's a pity that this didn't happen at a new risk level, but it's great anyway! ๐Ÿ’ฐ Current trading result is 125% of profit with 22% max drawdown within 88 days. Binance, for some reason that I don't understand, does not show any trading history older than 90 days on monitoring (may be because there are practically no accounts that live for such a long time...), so I saved a screenshot for the memory. After such a sharp market movement (and, accordingly, rapid increase in robot's profit) I expect, as it usually the case, consolidation, and, accordingly, a drawdown, which, hopefully, will allow to increase trading volume. I'll be withdrawing all the profit above 5000 from my account so that relative risk for investors would remain at the bottom level defined by the risk-management policies (0.6 lots for every 1000 of balance). #COPYTRADING #EthereumPower #ProfitUpdate #algotrading

๐ŸŽ‰ Congratulations to all investors on updating the maximum historical profit!

During recent corrections in crypto robot was gradually gaining profit, so, unfortunately, it didn't reach the minimum drawdown level allowing to increase risk according to my capital management strategy. And yesterday, after huge movement in Ethereum, caused by #ETHETFS news, historical profit was significantly updated. It's a pity that this didn't happen at a new risk level, but it's great anyway!

๐Ÿ’ฐ Current trading result is 125% of profit with 22% max drawdown within 88 days.

Binance, for some reason that I don't understand, does not show any trading history older than 90 days on monitoring (may be because there are practically no accounts that live for such a long time...), so I saved a screenshot for the memory.

After such a sharp market movement (and, accordingly, rapid increase in robot's profit) I expect, as it usually the case, consolidation, and, accordingly, a drawdown, which, hopefully, will allow to increase trading volume.

I'll be withdrawing all the profit above 5000 from my account so that relative risk for investors would remain at the bottom level defined by the risk-management policies (0.6 lots for every 1000 of balance).

#COPYTRADING #EthereumPower #ProfitUpdate #algotrading

Algo_Hedge
3 / 200
7D PNL
-1426.67
7D ROI
-21.05%
AUM
$14807.32
MDD
21.62%
Win Rate
20
Copy trading is high risk. Be careful and see Risk Warning.
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3. My algorithm โš ๏ธ Please read the following CAREFULLY before investing money in my strategy (especially the Risks section) โš ๏ธ Main properties of the expert: ๐ŸŸข Trend following system on $ETHUSDT futures. ๐ŸŸข No grid, no martingale, not holding loss positions for a long time. ๐ŸŸข Trades only 1 deal at a time. Next deal can be opened only after the 1st one was closed. ๐ŸŸข Each deal has a fixed SL that can only be shortened. ๐ŸŸข Uses trailing SL to maximize profit from big trends. โžก๏ธ On the one hand, it uses some adaptive conditions to enter the trend with a relatively short SL, follow that trend with trailing SL and exit or reverse at the end of the trend. These conditions adapt to the market situation. โžก๏ธ On the other, it has some filters to avoid multiple losses during the flat phase, which were tuned based on my original technique that I call "an optimization without optimization". It was inspired by several research articles ([1], [2], [3]) where I found the answer why most of the optimization techniques used in algotrading are fail. This approach allows to reduce an overfitting to minimum. Examples of deals: see screenshot. References: [1] D. Bailey, J. Borwein, M. Lรณpez de Prado and J. Zhu, The probability of backtest overfitting, 2013, working paper. [2] D. Bailey and M. Lรณpez de Prado, The Sharpe ratio efficient frontier, Journal of Risk 15(2) (2012), 3โ€“44. [3] Bailey, D., J. Borwein, M. Lยดopez de Prado and J. Zhu, โ€œPseudo-mathematics and financial charlatanism: The effects of backtest over fitting on out-of-sample performance,โ€ Notices of the AMS, 61 May (2014), 458โ€“471. #COPYTRADING #ethereum #algotrading #InvestingSafety #RiskManagement
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