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Ethereum Algotrader
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šŸ“£šŸ“£ SvarÄ«gs paziņojums investoriem! IzņemÅ”ana no mana potenciālā pirkuma konta ir sasniegusi 800Ā $, kas ir pirmais optimālais ieejas punkts, kas ieteicams palielināt depozÄ«tu, lai sasniegtu maksimālo veiktspēju [according to your investing strategy](https://www.binance.com/en/square/post/8453380496338). Es palielināju savu depozÄ«tu no 4200$ lÄ«dz 9000$ un pielāgoju savu risku jaunajam depozÄ«ta lielumam (risks = 5,4 lotes). RelatÄ«vais risks kopiju kontiem paliek nemainÄ«gs. #COPYTRADING #buythedip #ethereum #RiskManagement #InvestingSafety

šŸ“£šŸ“£ SvarÄ«gs paziņojums investoriem!

IzņemÅ”ana no mana potenciālā pirkuma konta ir sasniegusi 800Ā $, kas ir pirmais optimālais ieejas punkts, kas ieteicams palielināt depozÄ«tu, lai sasniegtu maksimālo veiktspēju according to your investing strategy.

Es palielināju savu depozītu no 4200$ līdz 9000$ un pielāgoju savu risku jaunajam depozīta lielumam (risks = 5,4 lotes). Relatīvais risks kopiju kontiem paliek nemainīgs.

#COPYTRADING #buythedip #ethereum #RiskManagement #InvestingSafety

Algo_Hedge
1 / 200
7Ā dienu PZA
-1372.34
7Ā dienu ROI
-15.29%
Pārvaldībā esoŔie aktīvi
$15183.33
MDD
15.29%
Ieguvuma rādītājs
9.0909
AtdarinoŔā tirdzniecÄ«ba ir saistÄ«ta ar augstu risku. Ievēro piesardzÄ«bu un izlasi BrÄ«dinājumu par riskiem.
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5. Risks āš ļø Please read the following CAREFULLY before investing money in my strategy (especially the Risks section - that's why we start from it) āš ļø All historical tests were done with the risk 0.5 lots for every 1000 of balance (fix.lot=4). A more aggressive option is now being used in trading. It trades from 0.6 to 1 lot for every 1000 of balance. But the lot is increased only in drawdowns which decreases risk significantly. (technical information, not crucial) If you use absolutely passive investing strategy and enter the trading at the high of the profit curve your risk in the case of repeat of the maximal historical drawdown is ā—ļø55%ā—ļøof your deposit (but this drawdown can be exceeded; there are 5 drawdowns of the similar size within 6 years of history). That's why I highly recommend using partial investing strategy. šŸŸ£ Conservative strategy (the one that I use) šŸŸ£ Divide your bot budget into 3 equal parts: āž”ļøDeposit 1st part immediately. āž”ļøDeposit 2nd part when the drawdown on my deposit will reach 800$ (see screenshot below how to find it on Monitoring). āž”ļøDeposit 3rd part when the drawdown on my deposit will reach 2440$. In this case repeat of the maximal historical drawdown will result in a ā—ļø43%ā—ļø drawdown on your account. Your account will recover faster. šŸ”“ Aggressive strategy šŸ”“ Divide your bot budget into 2 equal parts: āž”ļøDeposit 1st part immediately. āž”ļøDeposit 2nd part when the drawdown on my deposit will reach 800$ (see screenshot below how to find it on Monitoring. In this case repeat of the maximal historical drawdown will result in a ā—ļø51%ā—ļø drawdown on your account. Your account will recover faster. (see drawdowns($) in 6 years (blue line is month number) for the lead trader account below) #COPYTRADING #RiskManagement #algotrading #InvestingSafety #BuytheDips
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3. My algorithm āš ļø Please read the following CAREFULLY before investing money in my strategy (especially the Risks section) āš ļø Main properties of the expert: šŸŸ¢ Trend following system on $ETHUSDT futures. šŸŸ¢ No grid, no martingale, not holding loss positions for a long time. šŸŸ¢ Trades only 1 deal at a time. Next deal can be opened only after the 1st one was closed. šŸŸ¢ Each deal has a fixed SL that can only be shortened. šŸŸ¢ Uses trailing SL to maximize profit from big trends. āž”ļø 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. āž”ļø 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. LĆ³pez de Prado and J. Zhu, The probability of backtest overfitting, 2013, working paper. [2] D. Bailey and M. LĆ³pez de Prado, The Sharpe ratio efficient frontier, Journal of Risk 15(2) (2012), 3ā€“44. [3] Bailey, D., J. Borwein, M. LĀ“opez de Prado and J. Zhu, ā€œPseudo-mathematics and financial charlatanism: The effects of backtest over fitting on out-of-sample performance,ā€ Notices of the AMS, 61 May (2014), 458ā€“471. #COPYTRADING #ethereum #algotrading #InvestingSafety #RiskManagement
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