Logistic strategy

Script from: TradingViewSwingVolumeMomentum

The Logistic Strategy models price changes using a logistic function akin to those used for population growth. It leverages the z-score of net volume as the parameter influencing the exponential component, aiming to predict market movements with this unique approach. Ideal for traders seeking a novel perspective on utilizing volume data for trading analysis.

Top 68 Backtests of Logistic strategy

Explore the most profitable cryptocurrency and stock backtests with Logistic Strategy (on TradingView).

Filters

Premium users only

Premium users can access all backtests with a Risk/Reward Ratio > 3

@ 5 min

3.55

Risk Reward

22.53 %

Total ROI

Premium users only

Premium users can access all backtests with a Risk/Reward Ratio > 3

@ 1 h

3.13

Risk Reward

1,715.67 %

Total ROI

170

Bank of America Corporation (BAC)

+ Logistic strategy

@ 5 min

2.96

Risk Reward

25.10 %

Total ROI

Sandisk Corporation (SNDK)

+ Logistic strategy

@ 1 h

2.65

Risk Reward

1,375.96 %

Total ROI

Bloom Energy Corporation (BE)

+ Logistic strategy

@ Daily

2.44

Risk Reward

3,295.52 %

Total ROI

ChainLink / TetherUS (LINKUSDT)

+ Logistic strategy

@ 5 min

2.03

Risk Reward

17.56 %

Total ROI

Bloom Energy Corporation (BE)

+ Logistic strategy

@ 4 h

1.73

Risk Reward

8,654.67 %

Total ROI

126

Zcash / TetherUS (ZECUSDT)

+ Logistic strategy

@ 4 h

1.73

Risk Reward

15,283.41 %

Total ROI

342

Spotify Technology S.A. (SPOT)

+ Logistic strategy

@ Daily

1.61

Risk Reward

303.37 %

Total ROI

IonQ, Inc. (IONQ)

+ Logistic strategy

@ Daily

1.58

Risk Reward

663.92 %

Total ROI

Western Digital Corporation (WDC)

+ Logistic strategy

@ 1 h

1.52

Risk Reward

761.37 %

Total ROI

265

BITCOIN FUTURES (CONTINUOUS: CURRENT CONTRACT IN FRO...)

+ Logistic strategy

@ 4 h

1.51

Risk Reward

2,133.40 %

Total ROI

219

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Active Trades

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Logistic strategy backtest statistics

Aggregated across every validated backtest TradeSearcher has run on this strategy. Figures update automatically as new backtests are added — they are not edited by hand. Classified as swing by holding period.

Backtests run

1.3

Avg profit factor

+879%

Avg net profit

+34%

Avg annualized return

58%

Avg max drawdown

0.17

Avg Sharpe ratio

On average, backtests of this strategy beat a buy-and-hold baseline by 697% over the same window.

Performance by asset class

Market	Backtests	Avg profit factor	Avg net profit	Avg max drawdown	Avg Sharpe
Crypto	67	1.2	>1,000%	70%	0.18
Stocks	729	1.5	+721%	49%	0.16

Backtests use the validated-universe filter: non-repainting scripts, over 15 trades, non-excluded. Per-market rows appear only when at least one backtest exists for that asset class.

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