Gor Dragongor
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Weighted Fourier Transform: open-source DFT for Pine Script 6 with my custom tapering window
Weighted Fourier Transform: open-source DFT for Pine Script 6 with my custom tapering window
🙏🏻 This drop has 2 purposes: 1) to inform every1 who'd ever see it that Weighted Fourier Tranform does exist, while being available nowhere online, not even in papers, yet there's nothing incredibly complicated about it, and it can/should be used in certain cases; 2) to show TradingView users how they can use it now in dem endevours, to show em what spectral filtering is, and what can they do with all of it in diy mode. ... so we gonna have 2 sections in the description Section 1: Weighted …
[proprietary] Volatility Simulation & Analysis: stable volatility estimates via Gamma Noise -> Monte Carlo
[proprietary] Volatility Simulation & Analysis: stable volatility estimates via Gamma Noise -> Monte Carlo
🙏🏻 The main purpose of this tool is to define your stop-losses and take-profits, even tho it's really fast (time complexity O(n)), it does Monte Carlo simulations inside, providing you the Way higher info gain. This method is more advanced than using structural volatility analysis, such as stdev on raw data, in a sense that the outputs have lower variance but higher bias. However, in return for that, it provides means to know where to look for breakeven exits, smth you can't really do …
[proprietary] Market State Tracking: using 4 separate innovations streams to fit 4 independant PVA trackers
[proprietary] Market State Tracking: using 4 separate innovations streams to fit 4 independant PVA trackers
🙏🏻 This is MST (Market State Tracker), it’s main purpose is to tell whether it's better to take a predefined take-profit, or to expect a runner. Unlike widely-known alternatives, this model is made with top state-space and innovation modelling tech, and it takes the necessary info ‘itself’ (not the derivatives) from the right places. In fancy terms it’s not even a model, it’s an ensemble of several models. If you want to get familiar with other work of mine like this, check …
UAT: adaptive tracking using a kernel based on derivatives and anti-derivatives of Beta(2, 2) PDF
UAT: adaptive tracking using a kernel based on derivatives and anti-derivatives of Beta(2, 2) PDF
🙏🏻 Behold, this is UAT (Universal Adaptive Tracker), with less words imma proceed how it compares with alternatives: https://www.tradingview.com/x/CIB1rlrH/ ^^ comparison with non-adaptive quadratic regression (purple line), that has higher overshoots, less precision https://www.tradingview.com/x/j6yRwrBC/ ^^ comparison with JMA and its adaptive gain. JMA’s gain is heavily limited, while UAT’s negative and positive gains are soft-saturated with p-order Möbius transform This drop is inspired …
Multivariate Kalman Filter: Pine Script 6, open source, numerically safe & stable
Multivariate Kalman Filter: Pine Script 6, open source, numerically safe & stable
Volatility Signal-to-Noise Ratio: volatility regime detection @ O(1)
Volatility Signal-to-Noise Ratio: volatility regime detection @ O(1)
🙏🏻 this is VSNR: the most effective and simple volatility regime detector & automatic volatility threshold scaler that somehow no1 ever talks about. This is simply an inverse of the coefficient of variation of absolute returns, but properly constructed taking into account temporal information, and made online via recursive math with algocomplexity O(1) both in expanding and moving windows modes. How do the available alternatives differ (while some’re just worse)? Mainstream quant stat …
[Proprietary] oMBAD: multi-purpose HFT-ready tool, O(1) algocomplexity
[Proprietary] oMBAD: multi-purpose HFT-ready tool, O(1) algocomplexity
🙏🏻 oMBAD (online Moment-Based Adaptive Detection): adaptive anomaly || outlier || novelty detection, higher-order standardized moments; at O(1) time complexity For TradingView users: this entity would truly unleash its true potential for you ‘only’ if you work with tick-based & seconds-based resolutions, otherwise I recommend to keep using original non-online MBAD. Otherwise it may only help with a much faster backtesting & strategy development processes. ... Main features : O(1) time …
Stochastic Ensembling of Outputs: perturbing outputs with colored noise, O(1) algocomplexity
Stochastic Ensembling of Outputs: perturbing outputs with colored noise, O(1) algocomplexity
Stochastic Ensembling of Outputs 🙏🏻 This is a simple tool/method that would solve naturally many well known problems: “Price reversed 1 tick before the actual level, not executing my limit order” “I consider intraday trend change by checking whether price is above/below VWAP, but is 1 tick enough? What to do, price is now whipsawing around vwap...”. “I want to gradually accumulate a position around a chosen anchor. But where exactly should I put my orders? And I want to automate it …
QBAD: weighted adaptive box-plot based on quantile asymetry metrics, all at O(nlogn) cost
QBAD: weighted adaptive box-plot based on quantile asymetry metrics, all at O(nlogn) cost
🙏🏻 Dedicated to John Tukey. He invented the boxplot, and I finalized it. QBAD (Quantile-Based Adaptive Detection) is ‘the’ adaptive (also optionally weighted = ready for timeseries) boxplot with more senseful fences. Instead of hardcoded multipliers for outer fences, I base em on a set of quantile-based asymmetry metrics (you can view it as an ‘algorithmic’ counter part of central & standardized moments). So outer bands are Not hardcoded, not optimized, not cross-validated etc, simply …
Hybrid Adaptive Double Exponential Smoothing (HADES) : parameterless & adaptive hybrid exponential smoothing, @ O(1) algo complexity
Hybrid Adaptive Double Exponential Smoothing (HADES) : parameterless & adaptive hybrid exponential smoothing, @ O(1) algo complexity
Adaptive Window Size (AWS): leveraging fractal efficiency to gain optimal adaptive moving window length, O(1) on live updates
Adaptive Window Size (AWS): leveraging fractal efficiency to gain optimal adaptive moving window length, O(1) on live updates
I’m posting it here because for an unknown reason TradingView banned this publication, I asked why and asked to reconsider, but given the…
N-Degree Moment-Based Adaptive Detection (NDMBAD): generalization of MBAD, extending the MBAD logic to higher-degree polynomial regression
N-Degree Moment-Based Adaptive Detection (NDMBAD): generalization of MBAD, extending the MBAD logic to higher-degree polynomial regression
Moment-Based Adaptive Detection (MBAD): using standardized moments to construct adaptive thresholds @ O(n)
Moment-Based Adaptive Detection (MBAD): using standardized moments to construct adaptive thresholds @ O(n)
MBAD (Moment-Based Adaptive Detection): a method applicable to a wide range of purposes, like outlier or novelty detection, that requires building a sensible interval/set of thresholds. Unlike other methods that are static and rely on optimizations that inevitably lead to underfitting/overfitting, it dynamically adapts to your data distribution without any optimizations, MLE, or stuff, and provides a set of data-driven adaptive thresholds, based on closed-form solution with O(n) algo …
[Upd] Weighted percentile nearest rank: algo complexity improved from O(n²) to O(n log n)
[Upd] Weighted percentile nearest rank: algo complexity improved from O(n²) to O(n log n)
Yo, posting it for the whole internet, took the whole day to find / to design the actual working solution for weighted percentile 'nearest rank' algorithm, almost no reliable info online and a lot of library-style/textbook-style solutions that don't provide on real world production level. The principle: 0) initial data data = 22, 33, 11, 44, 55 weights = 5 , 3 , 2 , 1 , 4 array(s) size = 5 1) sort data array, apply the sorting pattern to the weights array, resulting: data = …
Hybrid Triple Exponential Smoothing (HTES): applying exponential smoothing to residuals & errors
Hybrid Triple Exponential Smoothing (HTES): applying exponential smoothing to residuals & errors
O(1) on updates /w adaptive thresholds, better than SnR in spotting consistent behavior in time series
O(1) on updates /w adaptive thresholds, better than SnR in spotting consistent behavior in time series
🙏🏻 Hey TV, this is QSFS, following: https://www.tradingview.com/script/kVuhL76w-Quick-scan-for-drift/ ^^ Quick scan for drift (QSFD) https://www.tradingview.com/script/hApR4O9d-Quick-scan-for-cycles/ ^^ Quick scan for cycles (QSFC) As mentioned before, ML trading is all about spotting any kind of non-randomness, and this metric (along with 2 previously posted) gonna help ya'll do it fast. This one will show you whether your time series possibly exhibits mean-reverting / consistent / noisy …
o(n) algo to check if data has any kind of periodic component
o(n) algo to check if data has any kind of periodic component
🙏🏻 The followup for https://www.tradingview.com/v/kVuhL76w/ As I told before, ML based algorading is all about detecting any kind of non-randomness & exploiting it (cuz allegedly u cant trade randomness), and cycles are legit patterns that can be leveraged But bro would u really apply Fourier / Wavelets / 'whatever else heavy' on every update of thousands of datasets, esp in real time on HFT / nearly HFT data? That's why this metric. It works much faster & eats hell of a less electicity, …
Weighted Percentile Nearest Rank algo (still first in Google Search)
Weighted Percentile Nearest Rank algo (still first in Google Search)
Yo, posting it for the whole internet, took the whole day to find / to design the actual working solution for weighted percentile 'nearest rank' algorithm, almost no reliable info online and a lot of library-style/textbook-style solutions that don't provide on real world production level. The principle: 0) initial data data = 22, 33, 11, 44, 55 weights = 5 , 3 , 2 , 1 , 4 array(s) size = 5 1) sort data array, apply the sorting pattern to the weights array, resulting: data = …
[New] First real implementation of composite Market Profile on TradingView, with proper tick-based binning algorithms
[New] First real implementation of composite Market Profile on TradingView, with proper tick-based binning algorithms
Sup TV, 2 important points. 1) surprisingly, it's the first MP Visible Range script on TV; 2) This one doesn't use any bagging/binning*, instead each row represents the time spent on the actual minimal price steps (aka ticks). The script will be further extended with usual market profile related functionally in future updates. At this point we have: - Profile itself (each row represents how many bars touch the given price); - Mode of the profile (called POC)**; * Still it will be introduced …
Weighted Standard Deviation Bands on C++ for SierraChart
Weighted Standard Deviation Bands on C++ for SierraChart
ACSIL code. Contribute to gorx1/sierra_gorx1 development by creating an account on GitHub.
Weighted Standard Deviation Bands on C# for NinjaTrader
Weighted Standard Deviation Bands on C# for NinjaTrader
[Updated] N-degree Weighted Moving Regression
[Updated] N-degree Weighted Moving Regression
[R&D] How I designed "Relative Slope" metric
[R&D] How I designed "Relative Slope" metric
[New] Fixed REST & WS APIs clients for FTX, C++ & python
[New] Fixed REST & WS APIs clients for FTX, C++ & python
[New] First one to introduce Linearly Weighted Least Squares on TradingView
[New] First one to introduce Linearly Weighted Least Squares on TradingView
[Old] Award-winning script "Spread by" written for TradingView platform
[Old] Award-winning script "Spread by" written for TradingView platform
[Old] Parts of the python code of an algo with ~60% return/5y written for QuantConnect platform
[Old] Parts of the python code of an algo with ~60% return/5y written for QuantConnect platform
[Old] My first ML project in python, predicting whether an employee will leave a company
[Old] My first ML project in python, predicting whether an employee will leave a company
[Old] My first C++ with basic stats functions
[Old] My first C++ with basic stats functions
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