Optimal Confidence Scalper [OCS]
Introduction
OCS : Optimal Confidence Scalpers, Utilise the computational approach towards finding confidence estimating in signal generating process, It helps u enter and exit the financial markets quickly, It buy and sell many times in a day with the objective of making consistent profits from incremental movements in the traded security's price. As we all know Lag is very undesirable because a trading system. Late trades can many times be worse than no trades at all, Main aim of the System is to find optimal Entry and Exit points for a successful trade
Mathematics behind the indicator
The indicator use two fundamentals pillars :
Estimation of a Confidence Interval
In frequentist statistics, a confidence interval (CI) is a range of estimates for an unknown parameter. A confidence interval is computed at a designated confidence level; the 95% confidence level is most common, but other levels, such as 90% or 99%, are sometimes used.
Desired properties are Validity, Optimality and Invariance
Polynomial Filters
The polynomial filters are based on the orthogonal polynomials of Legendre and Laguerre. Orthogonal polynomials are widely used in applied mathematics, physics and engineering, and the Legendre and Laguerre polynomials are only two of infinitely many sets, each of which has its own weight function.
They can be characterized in three equivalent ways:
1. They are the optimal lowpass filters that minimize the NRR, subject to additional constraints than the DC unity-gain condition
2. They are the optimal filters that minimize the NRR whose frequency response H(ω) satisfies certain flatness constraints at DC
3. They are the filters that optimally fit, in a least-squares sense, a set of data points to polynomials of different degrees.
The System uses Predictive Differentiation Filters, as subset to Polynomial Filters
Components of the System
Buy Signal and Sell Signals
=====================
=====================------ HOW TO USE IT
=====================
ENTRY and EXITS
Momentum Bands
Confidence Levels
Indicator Properties
Provision For Alerts
1. Buy Signal Alert
2. Sell Signal Alert
3. Exit Alert if in Buy Trade
4. Exit Alert if in Sell Trade
Some Examples
What TimeFrames To Use
U can use any Timeframe, The indicator is Adaptive in Nature,
I personally use timeframes such as : 1m, 5m 10m, 15m, ..... 1D, 1W
How to Access
U will need to privately message me.
use comment box for constructive comments
Thanks
OCS : Optimal Confidence Scalpers, Utilise the computational approach towards finding confidence estimating in signal generating process, It helps u enter and exit the financial markets quickly, It buy and sell many times in a day with the objective of making consistent profits from incremental movements in the traded security's price. As we all know Lag is very undesirable because a trading system. Late trades can many times be worse than no trades at all, Main aim of the System is to find optimal Entry and Exit points for a successful trade
Mathematics behind the indicator
The indicator use two fundamentals pillars :
Estimation of a Confidence Interval
In frequentist statistics, a confidence interval (CI) is a range of estimates for an unknown parameter. A confidence interval is computed at a designated confidence level; the 95% confidence level is most common, but other levels, such as 90% or 99%, are sometimes used.
Desired properties are Validity, Optimality and Invariance
Polynomial Filters
The polynomial filters are based on the orthogonal polynomials of Legendre and Laguerre. Orthogonal polynomials are widely used in applied mathematics, physics and engineering, and the Legendre and Laguerre polynomials are only two of infinitely many sets, each of which has its own weight function.
They can be characterized in three equivalent ways:
1. They are the optimal lowpass filters that minimize the NRR, subject to additional constraints than the DC unity-gain condition
2. They are the optimal filters that minimize the NRR whose frequency response H(ω) satisfies certain flatness constraints at DC
3. They are the filters that optimally fit, in a least-squares sense, a set of data points to polynomials of different degrees.
The System uses Predictive Differentiation Filters, as subset to Polynomial Filters
Components of the System
Buy Signal and Sell Signals
=====================
=====================------ HOW TO USE IT
=====================
ENTRY and EXITS
Momentum Bands
Confidence Levels
Indicator Properties
Provision For Alerts
1. Buy Signal Alert
2. Sell Signal Alert
3. Exit Alert if in Buy Trade
4. Exit Alert if in Sell Trade
Some Examples
What TimeFrames To Use
U can use any Timeframe, The indicator is Adaptive in Nature,
I personally use timeframes such as : 1m, 5m 10m, 15m, ..... 1D, 1W
How to Access
U will need to privately message me.
use comment box for constructive comments
Thanks
版本注释:
Some Intraday Example
版本注释:
Update : Adds The way to guide on the Targets
HISTORICAL PERFORMANCE
HISTORICAL PERFORMANCE