ADXMomentumOtimizeHyperOpt

Strategy: ADXMomentumOtimizeHyperOpt
Downloaded: 20220421
Stoploss: -0.219

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Buys	Avg Prof%	Cum Prof%	Tot Profit%	Avg Duration	Win	Draw	Loss	Win%	DD%	Stake	Exchange	Sharpe	Month
523	-0.31	-163.25	-37.71	2:49:00	174	90	259	33.3	37.68	USDT	kucoin	-58.46	202305
524	-0.31	-160.02	-36.97	2:44:00	192	97	235	36.6	37.01	USDT	kucoin	-63.6	202304
488	-0.27	-130.73	-30.2	2:47:00	186	84	218	38.1	33.35	USDT	kucoin	-41.34	202303
453	-0.21	-93.39	-21.57	2:33:00	173	75	205	38.2	23.72	USDT	kucoin	-19.44	202302
482	-0.13	-63.19	-18.98	2:33:00	205	75	202	42.5	18.72	USDT	kucoin	-3.31	202301
408	-0.33	-133.67	-40.14	2:51:00	140	73	195	34.3	41.42	USDT	kucoin	-9.98	202212
313	-0.49	-153.93	-46.23	2:40:00	117	50	146	37.4	46.76	USDT	kucoin	-12.5	202211
408	-0.29	-117.92	-35.38	2:56:00	164	57	187	40.2	38.55	USDT	kucoin	-10.37	202210
361	-0.4	-142.71	-42.86	2:47:00	112	79	170	31	42.19	USDT	kucoin	-6.79	202209
461	-0.2	-91.2	-27.39	2:46:00	183	98	180	39.7	35.93	USDT	kucoin	-4.43	202208
419	-0.3	-126.56	-38.01	2:50:00	165	91	163	39.4	38.51	USDT	kucoin	-7.35	202207
271	-0.64	-172.85	-51.91	2:44:00	92	49	130	33.9	52.37	USDT	kucoin	-10.67	202206

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The ADXMomentumOtimizeHyperOpt strategy is designed to backtest trading signals based on several technical indicators. Here's a short description of what the strategy does: The strategy uses the following indicators: ADX (Average Directional Index): Measures the strength of a market trend. PLUS_DI and MINUS_DI: Positive and negative directional indicators that help determine the market direction.

SAR (Stop and Reverse): Identifies potential trend reversals and provides stop-loss levels.

MOM (Momentum): Measures the rate of price change over a specified period.

The strategy has predefined parameters for ROI (Return on Investment) targets and a stop-loss level. The ideal timeframe for this strategy is 5 minutes. The strategy requires at least 20 candles (time periods) before generating valid signals. The populate_indicators function calculates the values of the indicators mentioned above and adds them to the input dataframe. The populate_buy_trend function generates a "buy" signal when specific conditions are met, including high ADX, positive momentum, and the positive directional indicator (DI+) being higher than the negative directional indicator (DI-). The populate_sell_trend function generates a "sell" signal when specific conditions are met, including high ADX, negative momentum, and the negative directional indicator (DI-) being higher than the positive directional indicator (DI+). By backtesting this strategy using historical price data, you can evaluate its performance and determine its effectiveness in generating profitable trading signals.