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Ethereum Algotrader
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📣📣 Anunț important pentru investitori! Retragerea din contul meu potențial a ajuns la 800 USD, care este primul punct de intrare optim recomandat pentru a vă crește depozitul pentru a obține performanța maximă [according to your investing strategy](https://www.binance.com/en/square/post/8453380496338). Îmi măresc depozitul de la 4200$ la 9000$ și îmi adaptez riscul la noua dimensiune a depozitului (risc=5,4 loturi). Riscul relativ pentru conturile de copiere rămâne același. #COPYTRADING #buythedip #ethereum #RiskManagement #InvestingSafety

📣📣 Anunț important pentru investitori!

Retragerea din contul meu potențial a ajuns la 800 USD, care este primul punct de intrare optim recomandat pentru a vă crește depozitul pentru a obține performanța maximă according to your investing strategy.

Îmi măresc depozitul de la 4200$ la 9000$ și îmi adaptez riscul la noua dimensiune a depozitului (risc=5,4 loturi). Riscul relativ pentru conturile de copiere rămâne același.

#COPYTRADING #buythedip #ethereum #RiskManagement #InvestingSafety

Algo_Hedge
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5. Risks ⚠️ Please read the following CAREFULLY before investing money in my strategy (especially the Risks section - that's why we start from it) ⚠️ 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 ❗️55%❗️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. 🟣 Conservative strategy (the one that I use) 🟣 Divide your bot budget into 3 equal parts: ➡️Deposit 1st part immediately. ➡️Deposit 2nd part when the drawdown on my deposit will reach 800$ (see screenshot below how to find it on Monitoring). ➡️Deposit 3rd part when the drawdown on my deposit will reach 2440$. In this case repeat of the maximal historical drawdown will result in a ❗️43%❗️ drawdown on your account. Your account will recover faster. 🔴 Aggressive strategy 🔴 Divide your bot budget into 2 equal parts: ➡️Deposit 1st part immediately. ➡️Deposit 2nd part when the drawdown on my deposit will reach 800$ (see screenshot below how to find it on Monitoring. In this case repeat of the maximal historical drawdown will result in a ❗️51%❗️ 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 ⚠️ Please read the following CAREFULLY before investing money in my strategy (especially the Risks section) ⚠️ Main properties of the expert: 🟢 Trend following system on $ETHUSDT futures. 🟢 No grid, no martingale, not holding loss positions for a long time. 🟢 Trades only 1 deal at a time. Next deal can be opened only after the 1st one was closed. 🟢 Each deal has a fixed SL that can only be shortened. 🟢 Uses trailing SL to maximize profit from big trends. ➡️ 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. ➡️ 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. López de Prado and J. Zhu, The probability of backtest overfitting, 2013, working paper. [2] D. Bailey and M. López de Prado, The Sharpe ratio efficient frontier, Journal of Risk 15(2) (2012), 3–44. [3] Bailey, D., J. Borwein, M. L´opez de Prado and J. Zhu, “Pseudo-mathematics and financial charlatanism: The effects of backtest over fitting on out-of-sample performance,” Notices of the AMS, 61 May (2014), 458–471. #COPYTRADING #ethereum #algotrading #InvestingSafety #RiskManagement
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