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INVITE-ONLY SCRIPT

Ocs Ai Trader

Por Ankit_1618
Atualizado
This script perform predictive analytics from a virtual trader perspective!

It acts as an AI Trade Assistant that helps you decide the optimal times to buy or sell securities, providing you with precise target prices and stop-loss level to optimise your gains and manage risk effectively.


System Components
The trading system is built on 4 fundamental layers :

  1. Time series Processing layer
  2. Signal Processing layer
  3. Machine Learning
  4. Virtual Trade Emulator


Time series Processing layer
This is first component responsible for handling and processing real-time and historical time series data.
In this layer Signals are extracted from
averages such as : volume price mean, adaptive moving average
Estimates such as : relative strength stochastics estimates on supertrend

Signal Processing layer
This second layer processes signals from previous layer using sensitivity filter comprising of an Probability Distribution Confidence Filter
The main purpose here is to predict the trend of the underlying, by converging price, volume signals and deltas over a dominant cycle as dimensions and generate signals of action.
Key terms
  • Dominant cycle is a time cycle that has a greater influence on the overall behaviour of a system than other cycles.
    The system uses Ehlers method to calculate Dominant Cycle/ Period.
    Dominant cycle is used to determine the influencing period for the underlying.
    Once the dominant cycle/ period is identified, it is treated as a dynamic length for considering further calculations

    Predictive Adaptive Filter to generate Signals and define Targets and Stops
    An adaptive filter is a system with a linear filter that has a transfer function controlled by variable parameters and a means to adjust those parameters according to an optimisation algorithm. Because of the complexity of the optimisation algorithms, almost all adaptive filters are digital filters. Thus Helping us classify our intent either long side or short side
    The indicator use Adaptive Least mean square algorithm, for convergence of the filtered signals into a category of intents, (either buy or sell)

    Machine Learning
    The third layer of the System performs classifications using KNN K-Nearest Neighbour is one of the simplest Machine Learning algorithms based on Supervised Learning technique.
    K-NN algorithm assumes the similarity between the new case/data and available cases and put the new case into the category that is most similar to the available categories.
    K-NN algorithm stores all the available data and classifies a new data point based on the similarity. This means when new data appears then it can be easily classified into a well suite category by using K- NN algorithm. K-NN algorithm can be used for Regression as well as for Classification but mostly it is used for the Classification problems.

    Virtual Trade Emulator
    In this last and fourth layer a trade assistant is coded using trade emulation techniques and the Lines and Labels for Buy / Sell Signals, Targets and Stop are forecasted!


    How to use
    The system generates Buy and Sell alerts and plots it on charts
    Buy signal snapshot
    Buy signal constitutes of three targets {namely T1, T2, T3} and one stop level

    Sell signal snapshot
    Sell signal constitutes of three targets {namely T1, T2, T3} and one stop level

    What Securities will it work upon ?Volume Informations must be present for the applied security
    The indicator works on every liquid security : stocks, future, forex, crypto, options, commodities

    What TimeFrames To Use ?
    You can use any Timeframe, The indicator is Adaptive in Nature,
    I personally use timeframes such as : 1m, 5m 10m, 15m, ..... 1D, 1W

    This Script Uses Tradingview Premium features for working on lower timeframesIn case if you are not a Tradingview premium subscriber you should tell the script that after applying on chart, this can be done by going to settings and unchecking "Is your Tradingview Subscription Premium or Above " Option
    snapshotHow To Get Access ?
    You will need to privately message me for access mentioning you want access to "Ocs Ai Trader" Use comment box only for constructive comments. Thanks !


Notas de Lançamento
Updates

  • Adds Regime Filter and Volatility Probability Scalper
  • Adds Quadratic Regression Filters
  • Adds Gaussian Lorentiz Normalisation and Daten Scaling
  • Adds Volume Float based Support and Resistance
  • Removes Dependencies from second based data unless necessarily forced by user!
Notas de Lançamento
some links updated
Notas de Lançamento
Adds Alert Passcode
Adds Marubozu Abnormality Detection
Adds Probability Average and Probability variable Filtering
Adds Historical Buy Sell Positions by Algo
Notas de Lançamento
Adds Trimean based Quantitative Test
Adds Hyperbolic Trend Tests
Adds Obv based Learnings
Adds Marubozu extremes
Adds Dynamic Target 4 and Target 5
Adds Signal Improvements
Notas de Lançamento
Minor update for Stop Taking
Notas de Lançamento
removes target text bugs
adds triple impulse of balance volume
Notas de Lançamento
Adds Pivotal points and Lines
Fixes issues in target booking
Notas de Lançamento
Adds Fisher Transform to Result in a variable with an approximately normally distributed variance, stable across different values of the sample correlation coefficient.
Notas de Lançamento
Does Minor Fixes in replay mode
Notas de Lançamento
Adds Fine tuning surrounding false volume negatives
Notas de Lançamento
Adds Tolerance to Fisher Transforms and * Minor Bug Fixes
Notas de Lançamento
Adds cumulative vol delta Regime filtering !
Notas de Lançamento
Adds Volatility Derivative Filters, inspired by elder's method!
Notas de Lançamento
*minor bug fixes -in -<datum plane of volatility reference>
Notas de Lançamento
Adds Filters based on Impulsive ATR's in order to Identify/Filter periods of increased market activity
Notas de Lançamento
Adds TRIX Filter's Strength and Weakness Analyser
Notas de Lançamento
Adds Smoothness and Marubozu Considerations in TRIX Regime Based Signals
Notas de Lançamento
Adds Work around over Exponential Probability Squeeze for managing random fuzzyness
Notas de Lançamento
minor FLT plot bugs updates
Notas de Lançamento
Adds Refined Retrained Weights for machine learning models
Notas de Lançamento
Update Description : Ocs Ai Trader ML pro.v312

1. Gaussian Structure
We’ve introduced a Gaussian-based framework to enhance signal smoothness and reduce market noise. A Gaussian approach applies weights along a bell-curve distribution, helping the strategy respond more precisely to recent price data while still smoothing out erratic price movements. This leads to more reliable trend detection and fewer false signals—key for better entry and exit decisions.

2. Backtesting Compiler
To improve the accuracy of performance metrics and streamline your strategy development, a backtesting compiler has been added. This tool runs systematic tests on historical data, helping you evaluate how well your strategy would have performed in past market conditions. It also makes optimization faster and more reliable, so you can identify winning parameter sets and adapt to changing market dynamics with confidence.

3. TRIX-Based System
A TRIX (Triple Exponential) component has been incorporated to further reduce lag and highlight momentum shifts. TRIX is known for quickly detecting trend changes while filtering out minor price fluctuations. Including TRIX in the script provides an additional momentum perspective, helping confirm signals from other indicators and refine overall entry/exit timing.

4. Derivative Filters
Lastly, we’ve added derivative-based filters to help isolate significant price moves from market noise. By focusing on higher-order changes in the price action (the “derivative” aspect), these filters can detect early shifts in momentum and volatility. This leads to more accurate alerts and reduces the likelihood of chasing sudden, short-lived price spikes.
Notas de Lançamento
Adds CVD-Based Divergence Signal Structure
CVD (Cumulative Volume Delta) tracks the net difference between buying and selling volume over time, providing insights into whether buyers or sellers are dominating the market. When price action and CVD diverge, it can signal an impending shift in momentum—often before traditional price-based indicators react. Including a CVD-based Divergence Signal Structure helps traders spot subtle imbalances in market participation, enabling earlier and more informed entry or exit decisions.
We have updated the script structure to lookout for these divergences
ai_trade_assistantforecastingocs_ai_traderPivot points and levelsvolumedelta

Script sob convite

O acesso a este script é restrito aos usuários autorizados pelo autor e pode requerir pagamento. Você pode adicioná-lo a seus favoritos, mas só poderá utilizá-lo após solicitar permissão e obtê-la do autor. Contate Ankit_1618 para mais informações, ou siga as instruções do autor abaixo.

O TradingView não sugere pagar por um script e usá-lo, a menos que você confie 100% no seu autor e entenda como o script funciona. Em muitos casos, você pode encontrar uma boa alternativa de código aberto gratuita nos nossos Scripts da Comunidade.

Instruções do autor

″You will need to privately message me for access mentioning you want access to "Ocs Ai Trader" You can connect us here ! https://ocstrader.com/ Use comment box only for constructive comments and criticism.

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Aviso: por favor leia antes de solicitar acesso.
Ankit_1618
Get Ocs Ai Trader, Your personal Ai Trade Assistant here
→ ocstrader.com
→ bit.ly/ocs_ai_trader

About me
AlgoTrading Certification, (University of Oxford, Säid Business School)
PGP Research Analysis, (NISM)
Electronics Engineer
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