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Ethereum Algotrader
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Recent big downside movements in $BTC and $ETH were used by my algorithm to gain some extra profit. Currently it is trading at the maximum of the equity curve. In the nearest future I expect good volatility in the market in both sides which means further gains. #algotrading #EthereumDown #ProfitUpdate #COPYTRADING

Recent big downside movements in $BTC and $ETH were used by my algorithm to gain some extra profit. Currently it is trading at the maximum of the equity curve. In the nearest future I expect good volatility in the market in both sides which means further gains.

#algotrading #EthereumDown #ProfitUpdate #COPYTRADING

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 ⚠️ 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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