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 71 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 profit factor above 3

@ 5 min

3.55

Profit Factor

22.53 %

Total ROI

Bank of America Corporation (BAC)

+ Logistic strategy

@ 5 min

2.96

Profit Factor

25.10 %

Total ROI

Hewlett Packard Enterprise Company (HPE)

+ Logistic strategy

@ 1 h

2.43

Profit Factor

1,484.10 %

Total ROI

174

Sandisk Corporation (SNDK)

+ Logistic strategy

@ 1 h

2.30

Profit Factor

1,249.05 %

Total ROI

E-MINI S&P 500 FUTURES (CONTINUOUS: CURRENT CONTRACT...)

+ Logistic strategy

@ DailyTradeSearcher backtest

2.16

Profit Factor

166.82 %

Total ROI

ChainLink / TetherUS (LINKUSDT)

+ Logistic strategy

@ 5 min

2.03

Profit Factor

17.56 %

Total ROI

Bloom Energy Corporation (BE)

+ Logistic strategy

@ Daily

1.87

Profit Factor

2,606.27 %

Total ROI

Bloom Energy Corporation (BE)

+ Logistic strategy

@ 4 h

1.73

Profit Factor

8,654.67 %

Total ROI

126

Spotify Technology S.A. (SPOT)

+ Logistic strategy

@ Daily

1.66

Profit Factor

327.15 %

Total ROI

Zcash / TetherUS (ZECUSDT)

+ Logistic strategy

@ 4 h

1.61

Profit Factor

14,008.75 %

Total ROI

345

IonQ, Inc. (IONQ)

+ Logistic strategy

@ Daily

1.58

Profit Factor

666.43 %

Total ROI

Australian Dollar/U.S. Dollar (AUDUSD)

+ Logistic strategy

@ DailyTradeSearcher backtest

1.57

Profit Factor

21.96 %

Total ROI

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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

+794%

Avg net profit

+30%

Avg annualized return

57%

Avg max drawdown

0.16

Avg Sharpe ratio

On average, backtests of this strategy beat a buy-and-hold baseline by 663% 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	+976%	70%	0.18
Stocks	729	1.4	+677%	49%	0.15
Forex	66	1.4	+16%	18%	0.05

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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