Strong KNN Classifier | ProjectSyndicateStrong KNN Classifier reads the order flow underneath each bar and asks one question: is the current move more likely to CONTINUE or to REVERSE? Instead of guessing, it learns from the market's own history. Every bar is turned into a five-part order-flow fingerprint, stored with the outcome it actually produced, and the live bar is matched against the closest past situations using Lorentzian distance — the outlier-robust metric built for noisy market data. The nearest neighbours vote, that vote is calibrated into an honest probability, and only genuinely confident reads near structural zones are printed as signals. Every prediction that resolves is scored on a live hit-rate panel — winners and misses alike — so you see exactly how the logic behaves on the symbol and timeframe you trade, not a number typed into a description.
🧠 Lorentzian Core — the core idea, expressed as a lifecycle: OBSERVE ▸ LABEL ▸ LEARN ▸ CLASSIFY ▸ CALIBRATE. Each bar is reduced to five order-flow features and z-scored so they share one scale. A rolling library of past feature vectors is kept, and each one is labelled only after its outcome is fully known — a vector formed H bars ago is tagged CONTINUATION or REVERSAL using bars that have already printed, never future ones. The live bar is then compared to that resolved library with Lorentzian-distance k-nearest-neighbours, the closest matches vote, and the result is turned into a probability. Because every label is resolved from past bars and every signal confirms on the bar's close, the classifier does not repaint.
🔋 Feature Anatomy — the fingerprint is not one number; it is five breakout-native ingredients fused into a single distance. Net delta (reconstructed buy-minus-sell pressure), bid/ask imbalance (which side dominated the bar), absorption (heavy volume that produces little price progress — a stalled push), CVD slope (the direction and steepness of cumulative delta), and price location versus VWAP in ATR units (where the bar sits inside its structural range). Each feature is independently z-scored over a rolling window, so no single raw scale can dominate the match. Order-flow components are reconstructed from lower-timeframe intrabar data and are labelled as estimates, not exchange tick prints.
🎯 Calibrated Probability Engine — this is the part most "AI" scripts get wrong. A raw k-vote can only land on a handful of fractions, so it slams to 88% or 100% and lies about its own confidence. Strong KNN Classifier instead weights each neighbour by distance, collapses the vote into a signed margin, shrinks that margin toward a neutral 0.5 prior by how much trustworthy neighbour mass actually agreed, and passes it through a logistic curve. The output is a smooth, continuous Reversal Probability and a 0–100 Confidence read that reflect genuine neighbour agreement — high only when many close matches concur, honestly near the middle when they don't.
🧲 Structural-Zone Context — a classification matters most where decisions are made: at the levels where price interacts with resting structure. The engine only promotes a read to a signal when price is inside a structural zone — within an ATR band of VWAP or of the most recent confirmed swing pivot. Away from structure the model still reports its read on the dashboard, but it holds its fire, keeping orbs anchored to the moments that carry context rather than scattering them mid-range.
🎚️ Conviction Controls — a compact set of dials sets how serious a read must be before it prints: the Minimum Confidence to signal, the number of Neighbours (k) that vote, the Prior Strength that shrinks weak agreement toward neutral, the Probability Sharpness that scales the confidence spread, the Distance Temperature that softens or sharpens neighbour weighting, the minimum sample count before any signal is allowed, and a cooldown. Tighten them for fewer, cleaner classifications; loosen them for more activity. Together with the zone gate, this is your main control over conviction versus frequency.
🧭 No-Lookahead Discipline — a read is not allowed to cheat. Training labels are resolved purely from bars that have already closed, the live bar is only ever compared against fully-resolved neighbours, and signals confirm on the closed candle. Until the library holds enough resolved samples the panel shows TRAINING and stays silent, and a cooldown stops a single chaotic session from stacking overlapping orbs. The hit-rate tracker is held to the same standard — every prediction whose horizon resolves is counted, correct or not, with nothing dropped to flatter the number.
⭐ 0–100 Confidence Read — every classification carries a numeric Reversal Probability, a Confidence score, and the count of neighbours that actually contributed. Treat Confidence as a relative cleanliness and agreement read for ranking and thinning signals — it describes how textbook the current situation is versus the model's memory, not a guaranteed outcome. The confidence threshold restricts what is displayed and alerted, while the dashboard keeps reporting the live read in the background even when no signal fires.
📊 Live Statistics Dashboard — a non-intrusive panel tracks, in real time on your chart: model status (TRAINING or LIVE) with the current training-pool size, the order-flow source in use, each of the five features as a live z-score, the current Prediction (continuation / reversal / neutral), the Reversal Probability, the Confidence, the number of neighbours used, whether price is currently inside a structural zone, and a rolling Hit-Rate computed over every resolved prediction. The hit-rate counts winners and misses in full, so the number is built live from the real signals on your current symbol and timeframe — not printed here in advance.
🎨 Clean Themed Visuals — four coherent palettes (Aurora default, plus Neon, Plasma, and Solar) shade the probability ribbon, the class-tinted candles, the signal orbs and their chips, and the dashboard to one look, so direction and quality read at a glance on a dark chart. A background ribbon graduates between the continuation and reversal colours by probability; confirmed signals print a sized circular orb with a clean, non-overlapping chip showing direction, probability, and the neighbour/confidence read. Signal dot size is adjustable, and the chip offset scales with it so labels never collide with the orb.
🔔 Detailed Alerts — fires on a continuation signal, on a reversal signal, and on any high-confidence classification, formatted for manual or automated use. The confidence threshold and the in-zone gate restrict alerts to higher-conviction reads.
🔧 Fully Customizable — every component is exposed: the number of neighbours, the outcome horizon, the trend-reference length, the outcome threshold in ATR, and the maximum and minimum training-pool sizes; the distance temperature, prior strength, and probability sharpness that govern calibration; the normalisation window, CVD slope length, and absorption reference of the feature engine; the intrabar resolution and volume weighting of the order-flow reconstruction; the VWAP and swing-pivot zones, pivot length, and zone width; the confidence threshold, in-zone requirement, and cooldown; all four themes and every ribbon, candle-tint, orb, dot-size, neighbour-chip, and dashboard toggle, plus dashboard position and size.
🎯 Why this is different — most "machine learning" indicators are black boxes that restyle an oscillator and claim to call the top, and most kNN scripts publish a confidence that is really just a vote fraction in disguise. This one engineers inspectable order-flow features, matches them with an outlier-robust Lorentzian metric, and then does the part that is usually skipped: it calibrates the probability so confidence reflects real, distance-weighted neighbour agreement instead of a coarse vote artefact. It resolves every training label with no lookahead, gates signals to structural context, and reports a live, honest hit-rate that counts misses in full — so you judge it on your own current data rather than on a marketing figure.
🚀 Where to use it — the engine is symbol-agnostic and built on universal order-flow behaviour, so it can be applied to FX majors and crosses, metals, indices, and crypto on intraday timeframes. It is strongest where lower-timeframe order-flow reconstruction carries information — liquid intraday futures and crypto — and the normalisation and ATR scaling adapt to each instrument automatically. One practical check: if the five features all read 0σ, your chart's symbol is not exposing intrabar data and the model is running on the coarse bar-level fallback — set the Intrabar Resolution explicitly and confirm the features come alive before relying on signals. Because the classifier is symmetric, let the dashboard's hit-rate tell you whether the logic genuinely suits the pair and timeframe before you commit.
🎯 How to trade it
1 Apply it to a liquid symbol on an intraday timeframe and let the panel move from TRAINING to LIVE as the library fills. Read the live Hit-Rate for your symbol and timeframe first — if the logic doesn't suit that market, you'll see it.
2 Wait for an orb — it marks a confirmed close, above your confidence threshold, inside a structural zone, with the direction, probability, and neighbour/confidence read already labelled.
3 Read the dashboard alongside it: the Prediction, the Reversal Probability, the Confidence, and how many of the k neighbours actually agreed.
4 Use the confidence threshold, neighbour count, prior strength, and sharpness to set your tempo — stricter for fewer, cleaner reads; looser for more activity.
5 Combine the classification with your own structure and risk management — it is a read on the next move's character, not an entry-and-exit system on its own.
⚠️ Important — this is a decision-support tool, not a standalone buy/sell system, and it makes no performance guarantees. The probability is calibrated to reflect genuine neighbour agreement, but the underlying edge varies by market, session, and configuration, and on some symbols and timeframes it will be close to neutral — the displayed probability and the dashboard hit-rate are historical and descriptive, not a forecast. Order-flow features are reconstructed from lower-timeframe data and are estimates, not true tick prints; where intrabar data is unavailable the model falls back to a coarser delta. Signals confirm on the closed bar, so always wait for the orb on a closed candle. Because the classifier is symmetric and contrarian-capable, a strong one-way trend or a regime shift can run straight through a high-confidence read — combine it with your own analysis, and test it on your market before trading it live. Indicator
Machine Learning RSI | AI Classification & Ranking (Zeiierman)█ Overview
The Machine Learning RSI | AI Classification & Ranking (Zeiierman) is an adaptive RSI intelligence system that combines momentum analysis, historical analog recognition, machine learning classification, confidence scoring, and dynamic trend management into a single framework.
Rather than interpreting RSI solely through traditional overbought and oversold thresholds, the indicator examines how similar RSI environments have behaved historically and uses those observations to classify current market conditions.
The script transforms RSI into a multi-dimensional feature space, stores historical market behavior, identifies the closest historical analogs, and allows those analogs to vote on future directional bias.
An adaptive feature-optimization engine then continuously learns which RSI characteristics provide the greatest predictive value under current market conditions.
The result is a hybrid system that blends:
• Multi-dimensional RSI analysis
• Historical analog matching
• Machine learning classification
• Adaptive feature weighting
• Rank & confidence scoring
• AI-driven trend management
█ Why is this one unique
This is not a normal RSI. It is a full analog classification engine built in Pine Script v6. It turns RSI behavior into an 8-feature market fingerprint, stores historical examples, labels them by future outcome, finds the closest past situations, lets those analogs vote, then converts the result into an adaptive ML RSI, rank/confidence scores, signals, and an ML-modulated Supertrend.
⚪ What it does
At a high level:
1. Builds 8 RSI-derived features
It does not only use the RSI value. It models:
RSI level, slope, acceleration, distance from 50, percentile rank, RSI volatility, fast/slow RSI spread, and smoothed RSI regime.
That means each bar becomes a multi-dimensional “state” of momentum, not just “RSI is 63.”
2. Creates a memory bank
Each confirmed bar is stored with its feature snapshot and a future outcome label. The label is based on whether price moved up or down after a fixed horizon, scaled by ATR. That is the learning dataset.
3. Uses K-nearest-neighbor analog matching
For the current bar, the script scans the historical bank and finds the closest past examples. It uses a Lorentzian-style compressed distance:
log(1 + abs(feature difference))
That is good because it reduces the impact of outliers. Huge feature mismatches do not completely dominate the model.
4. Lets analogs vote
Nearest neighbors vote bull or bear, weighted by distance. Closer matches matter more. The output becomes: analogScore, bias direction, agreement fraction, and gap tightness.
5. Auto-optimizes feature weights
This is one of the most sophisticated parts. The script uses a Fisher-discriminant-style calculation to determine which RSI features currently best separate bullish vs. bearish outcomes. Then it rescales those weights and smooths them over time.
So the model can learn that, for example, RSI slope matters more on one instrument, while RSI percentile or regime matters more on another.
6. Builds rank and confidence
Signals are not triggered just because the model flips bullish or bearish. They must pass a quality system:
Rank blends agreement, distance tightness, trend alignment, volatility health, regime fit, slope fit, smoothness, persistence, and penalties for chop or early flips.
Confidence focuses more on analog agreement, tightness, persistence, and slope fit.
This is much better than a simple buy/sell oscillator because it asks: “Is this setup actually supported?”
7. Adds adaptive Supertrend
The Supertrend is not static. Its band width changes based on ML conviction. High conviction tightens the trailing stop. Low conviction or chop widens it. That makes the trend system responsive without being blindly reactive.
⚪ Why it is good
The strongest part is that it combines machine learning logic, technical architecture, and trade-quality filtering into a single system.
Most PulseWire indicators are fixed formulas: RSI crosses 30, MACD crosses, Supertrend flips, moving average slope changes. This code differs because it creates a small local learning model directly in Pine.
The unique edge is the combination of:
• Feature engineering: RSI is transformed into 8 separate behavioral dimensions.
• Historical analog learning: Current market conditions are compared to past similar conditions.
• Distance-weighted voting: Closer historical examples have more influence.
• Auto feature weighting: The system adapts which features matter most.
• ATR-based outcome labeling: Learning is normalized by volatility, not just raw price movement.
• Quality scoring: Signals require both rank and confidence.
• Adaptive trend logic: The ML engine not only generates oscillator signals but also modifies Supertrend behavior.
That combination is rare in Pine Script. PulseWire supports advanced data structures such as arrays, matrices, and user-defined types, but many public scripts still use simpler procedural indicator logic. This script uses those advanced structures as a true modeling framework.
⚪ What makes it sophisticated
The code actually implements an AI-style classification workflow:
Input features → labeled memory → nearest-neighbor search → weighted classification → confidence scoring → adaptive output.
That is a real machine-learning pattern.
But this script goes further than a basic KNN signal tool because it adds:
• Auto-optimized feature weights using class separation.
• Rank/confidence gates instead of raw prediction signals.
• Chop, volatility, and trend filters to reduce bad market conditions.
• ML-driven Supertrend adaptivity rather than using ML only for arrows.
• Non-repainting signal discipline by firing on confirmed bars only.
⚪ Why It’s Marketable
Most RSI indicators treat every reading the same. This tool takes a different approach by analyzing how similar RSI conditions performed in the past and evaluating the current setup against those historical patterns. It only generates signals when multiple factors align, including confidence, trend direction, volatility, and market structure.
What makes it valuable is that it transforms RSI from a simple momentum oscillator into a context-aware decision framework. Rather than reacting to fixed overbought and oversold levels, it identifies recurring market behaviors, measures the similarity of current conditions to historical examples, and assigns a quality score to each opportunity. It then filters out low-probability environments and dynamically adjusts its trend management based on the strength of the model's conviction.
The result is a more selective, adaptive, and intelligent signal engine that helps traders focus on higher-quality setups instead of every RSI fluctuation. This moves well beyond the capabilities of a conventional PulseWire RSI indicator.
⚪ Main weakness
It is not deep learning, and it does not train a neural network. It is an online analog classifier. That is still legitimate AI-style logic. Also, because it learns from historical analogs inside the chart, performance depends heavily on market regime, symbol, timeframe, memory depth, and filters.
█ How It Works
⚪ Machine Learning Feature Engine
Most RSI indicators analyze a single value.
The Machine Learning RSI transforms RSI into a complete momentum fingerprint, consisting of eight independent characteristics that describe how momentum behaves beneath the surface.
The model analyzes:
• RSI Value
• RSI Slope
• RSI Acceleration
• Distance From Neutral (50)
• RSI Percentile Rank
• RSI Volatility
• Fast vs Slow RSI Spread
• RSI Regime Structure
Features cur = Features.new(
rOsc / 100.0,
scale01(rOsc - rOsc , winLen),
scale01(rOsc - rOsc -
(rOsc - rOsc ), winLen),
math.abs(rOsc - 50.0) / 50.0,
ta.percentrank(rOsc, winLen) / 100.0,
scale01(ta.stdev(rOsc, 14), winLen),
scale01(rOscF - rOscS, winLen),
scale01(ta.ema(rOsc, 20) - 50.0, winLen)
)
Together these features create a much richer representation of market behavior than traditional RSI calculations.
Instead of asking:
“Where is RSI?”
The model asks:
“What type of momentum behavior is currently occurring?”
⚪ Historical Analog Memory
The indicator continuously builds a memory bank of historical market behavior.
Every confirmed bar is stored together with its RSI fingerprint and the future outcome that followed.
row = array.from(
fVal, fSlp, fAcc, fMid,
fPct, fChn, fSpr, fReg,
float(outcome)
)
bank.add_row(0, row)
Over time the model accumulates hundreds or even thousands of historical observations.
Each observation becomes a real market example the system can reference later.
Rather than relying entirely on fixed formulas, the indicator learns from historical market behavior.
⚪ AI Classification Engine
Once the memory bank has been built, the Machine Learning RSI begins searching for historical situations that closely resemble the current market.
The comparison is performed across all eight RSI features simultaneously.
g = cur.gapTo(row, wts)
Similarity is measured using a weighted Lorentzian distance function.
compress(float d) =>
math.log(1.0 + math.abs(d))
Unlike traditional distance calculations, logarithmic compression reduces the influence of extreme outliers and prevents a single feature from dominating the comparison process.
This creates a more stable and robust analog matching system.
The objective is not to find identical charts.
The objective is to find historical momentum environments that behaved similarly.
⚪ Historical Analog Voting
After locating the closest historical matches, the system allows them to vote on the current market direction.
Closer analogs receive greater influence while weaker matches contribute less.
float w = 1.0 / (1.0 + n.gap)
v.score := v.score + n.cls * w
The weighted votes are combined into a final classification score.
eng.analogScore :=
vote.total > 0
? vote.score / vote.total
: 0.0
This process produces:
• Directional Bias
• Analog Agreement
• Classification Strength
• Similarity Quality
• Market Conviction
Rather than attempting to predict the future directly, the model asks:
“How did the most similar momentum environments behave when they occurred previously?”
⚪ Adaptive Feature Optimizer
Markets are constantly changing.
Features that are highly predictive in one environment may become less useful in another.
To solve this problem, the Machine Learning RSI includes an adaptive feature optimization engine.
The model continuously evaluates which RSI characteristics are doing the best job separating bullish outcomes from bearish outcomes.
float f =
math.pow(mB - mBe, 2)
/
(vB + vBe + 1e-6)
This process is based on Fisher Discriminant Analysis.
Features that consistently separate winning conditions from losing conditions receive larger weights.
Features that lose predictive power gradually receive less influence.
wts.value := wAuto.get(0)
wts.slope := wAuto.get(1)
wts.accel := wAuto.get(2)
wts.mid := wAuto.get(3)
This allows the model to adapt automatically to changing market conditions without requiring constant manual optimization.
⚪ Rank & Confidence Engine
Most indicators generate signals immediately after a condition is met.
The Machine Learning RSI goes several steps further. Every setup receives two independent evaluations.
• Rank → Measures setup quality.
• Confidence → Measures model conviction.
Rank evaluates:
• Historical agreement
• Analog quality
• Trend alignment
• Volatility conditions
• Regime structure
• Momentum consistency
• Market stability
Confidence evaluates:
• Historical consensus
• Analog clustering
• Directional consistency
• Signal persistence
• Structural confirmation
setup.rank := rankScore(…)
setup.conf := confScore(…)
Signals are only generated once both quality and confidence requirements have been satisfied.
This helps filter weaker market conditions while prioritizing stronger opportunities.
⚪ AI-Driven Learning System
The Machine Learning RSI does not simply memorize historical outcomes.
It learns what constitutes a meaningful outcome.
Each historical observation is classified based on future movement relative to current volatility.
outcome =
moveFwd > 2 * bandFwd ? 3 :
moveFwd > bandFwd ? 2 :
moveFwd > 0 ? 1 :
moveFwd < -2 * bandFwd ? -3 :
moveFwd < -bandFwd ? -2 :
moveFwd < 0 ? -1 : 0
• Large bullish moves receive stronger bullish labels.
• Large bearish moves receive stronger bearish labels.
• Small movements receive weaker classifications.
This allows the model to distinguish meaningful market behavior from ordinary noise.
⚪ ML Supertrend System
The indicator includes an adaptive Machine Learning Supertrend that responds to model conviction.
Unlike traditional Supertrends that rely on a fixed ATR multiplier, the ML Supertrend dynamically adjusts its sensitivity based on classification strength.
mlDrive =
math.abs(convSmoothed) * 0.5 +
eng.gapTight * 0.3 +
eng.agreeFrac * 0.2
As conviction increases:
• Bands tighten
• Trend changes become faster
• Stops become more responsive
As conviction decreases:
• Bands widen
• Noise tolerance increases
• Whipsaws are reduced
adaptMult =
stMultBase *
(1.0 + stMlResp * (1.0 - mlDrive))
This creates a trend-following system that adapts to the strength of the model’s conviction rather than relying solely on volatility.
█ How To Use
⚪ Reading The ML RSI
The Machine Learning RSI ranges from 0 to 100.
• Values above 50 suggest bullish momentum conditions dominate the market.
• Values below 50 suggest bearish momentum conditions dominate the market.
• Readings above 70 typically indicate strong bullish conditions, while readings below 30 suggest strong bearish pressure.
⚪ Reading The Signals
The Machine Learning RSI generates signals when the model detects a meaningful shift in market conditions and that shift passes both its quality and confidence requirements.
• Long signals indicate that the classification engine has identified a bullish market environment supported by historical analog agreement, trend structure, and market conditions.
• Short signals indicate that the classification engine has identified a bearish market environment supported by historical analog agreement, trend structure, and market conditions.
⚪ Using The ML Supertrend
The ML Supertrend acts as both a trend filter and a dynamic trailing stop.
• When the Supertrend flips bullish, the model considers the market to be operating in an uptrend regime.
• When the Supertrend flips bearish, the model considers the market to be operating in a downtrend regime.
█ Settings
Price Source: controls the price data used to build every RSI feature inside the learning engine.
Base RSI Length: controls the main RSI period used to create the ML RSI and its feature set.
Memory Depth: controls how many historical bars the model stores and searches when looking for similar market conditions.
Analog Count (k): controls how many closest historical matches are allowed to vote on the current market direction.
Show Signal Markers: toggles the Long and Short signal markers on the chart.
Candle Coloring: colors candles based on the current ML Supertrend regime.
Min Rank to Signal: controls the minimum setup-quality score required before a signal can appear.
Min Confidence to Signal: controls the minimum model conviction required before a signal can appear.
Trend Gate: requires signals to align with the ML Supertrend direction.
Volatility Band: filters signals so they only appear in healthier volatility conditions.
Min Vol Rank: controls the lower volatility threshold required for signals.
Chop Filter: blocks signals during choppy, range-bound market conditions.
Learning Sensitivity: controls how large a future move must be before the model treats it as a meaningful historical outcome.
Auto-Optimize Weights: allows the model to automatically learn which RSI features are most important.
Adaptation Speed: controls how quickly the learned feature weights adjust to changing market behavior.
Feature Weights: manually control the importance of each RSI feature when Auto-Optimize Weights is disabled.
Show ML Supertrend: toggles the adaptive ML Supertrend line, cloud, and trend visuals.
Supertrend Source: controls the price source used to build the ML Supertrend bands.
ATR Multiplier: controls the base distance of the ML Supertrend from price.
ML Band Adaptivity: controls how strongly model conviction adjusts the Supertrend band width.
RSI Signal Line Type: selects the moving average style displayed on the ML RSI.
RSI Signal Line Length: controls the smoothing length of the RSI signal line.
BB StdDev: controls the Bollinger Band width when using SMA + Bollinger Bands.
Colors: customize signal markers, candle coloring, ML RSI colors, Supertrend colors, cloud colors, and signal line visuals.
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Indicator
Machine Learning: VWAP [YinYangAlgorithms]Machine Learning: VWAP aims to use Machine Learning to Identify the best location to Anchor the VWAP at. Rather than using a traditional fixed length or simply adjusting based on a Date / Time; by applying Machine Learning we may hope to identify crucial areas which make sense to reset the VWAP and start anew. VWAP’s may act similar to a Bollinger Band in the sense that they help to identify both Overbought and Oversold Price locations based on previous movements and help to identify how far the price may move within the current Trend. However, unlike Bollinger Bands, VWAPs have the ability to parabolically get quite spaced out and also reset. For this reason, the price may never actually go from the Lower to the Upper and vice versa (when very spaced out; when the Upper and Lower zones are narrow, it may bounce between the two). The reason for this is due to how the anchor location is calculated and in this specific Indicator, how it changes anchors based on price movement calculated within Machine Learning.
This Indicator changes the anchor if the Low < Lowest Low of a length of X and likewise if the High > Highest High of a length of X. This logic is applied within a Machine Learning standpoint that likewise amplifies this Lookback Length by adding a Machine Learning Length to it and increasing the lookback length even further.
Due to how the anchor for this VWAP changes, you may notice that the Basis Line (Orange) may act as a Trend Identifier. When the Price is above the basis line, it may represent a bullish trend; and likewise it may represent a bearish trend when below it. You may also notice what may happen is when the trend occurs, it may push all the way to the Upper or Lower levels of this VWAP. It may then proceed to move horizontally until the VWAP expands more and it may gain more movement; or it may correct back to the Basis Line. If it corrects back to the basis line, what may happen is it either uses the Basis Line as a Support and continues in its current direction, or it will change the VWAP anchor and start anew.
Tutorial:
If we zoom in on the most recent VWAP we can see how it expands. Expansion may be caused by time but generally it may be caused by price movement and volume. Exponential Price movement causes the VWAP to expand, even if there are corrections to it. However, please note Volume adds a large weighted factor to the calculation; hence Volume Weighted Average Price (VWAP).
If you refer to the white circle in the example above; you’ll be able to see that the VWAP expanded even while the price was correcting to the Basis line. This happens due to exponential movement which holds high volume. If you look at the volume below the white circle, you’ll notice it was very large; however even though there was exponential price movement after the white circle, since the volume was low, the VWAP didn’t expand much more than it already had.
There may be times where both Volume and Price movement isn’t significant enough to cause much of an expansion. During this time it may be considered to be in a state of consolidation. While looking at this example, you may also notice the color switch from red to green to red. The color of the VWAP is related to the movement of the Basis line (Orange middle line). When the current basis is > the basis of the previous bar the color of the VWAP is green, and when the current basis is < the basis of the previous bar, the color of the VWAP is red. The color may help you gauge the current directional movement the price is facing within the VWAP.
You may have noticed there are signals within this Indicator. These signals are composed of Green and Red Triangles which represent potential Bullish and Bearish momentum changes. The Momentum changes happen when the Signal Type:
The High/Low or Close (You pick in settings)
Crosses one of the locations within the VWAP.
Bullish Momentum change signals occur when :
Signal Type crosses OVER the Basis
Signal Type crosses OVER the lower level
Bearish Momentum change signals occur when:
Signal Type crosses UNDER the Basis
Signal Type Crosses UNDER the upper level
These signals may represent locations where momentum may occur in the direction of these signals. For these reasons there are also alerts available to be set up for them.
If you refer to the two circles within the example above, you may see that when the close goes above the basis line, how it mat represents bullish momentum. Likewise if it corrects back to the basis and the basis acts as a support, it may continue its bullish momentum back to the upper levels again. However, if you refer to the red circle, you’ll see if the basis fails to act as a support, it may then start to correct all the way to the lower levels, or depending on how expanded the VWAP is, it may just reset its anchor due to such drastic movement.
You also have the ability to disable Machine Learning by setting ‘Machine Learning Type’ to ‘None’. If this is done, it will go off whether you have it set to:
Bullish
Bearish
Neutral
For the type of VWAP you want to see. In this example above we have it set to ‘Bullish’. Non Machine Learning VWAP are still calculated using the same logic of if low < lowest low over length of X and if high > highest high over length of X.
Non Machine Learning VWAP’s change much quicker but may also allow the price to correct from one side to the other without changing VWAP Anchor. They may be useful for breaking up a trend into smaller pieces after momentum may have changed.
Above is an example of how the Non Machine Learning VWAP looks like when in Bearish. As you can see based on if it is Bullish or Bearish is how it favors the trend to be and may likewise dictate when it changes the Anchor.
When set to neutral however, the Anchor may change quite quickly. This results in a still useful VWAP to help dictate possible zones that the price may move within, but they’re also much tighter zones that may not expand the same way.
We will conclude this Tutorial here, hopefully this gives you some insight as to why and how Machine Learning VWAPs may be useful; as well as how to use them.
Settings:
VWAP:
VWAP Type: Type of VWAP. You can favor specific direction changes or let it be Neutral where there is even weight to both. Please note, these do not apply to the Machine Learning VWAP.
Source: VWAP Source. By default VWAP usually uses HLC3; however OHLC4 may help by providing more data.
Lookback Length: The Length of this VWAP when it comes to seeing if the current High > Highest of this length; or if the current Low is < Lowest of this length.
Standard VWAP Multiplier: This multiplier is applied only to the Standard VWMA. This is when 'Machine Learning Type' is set to 'None'.
Machine Learning:
Use Rational Quadratics: Rationalizing our source may be beneficial for usage within ML calculations.
Signal Type: Bullish and Bearish Signals are when the price crosses over/under the basis, as well as the Upper and Lower levels. These may act as indicators to where price movement may occur.
Machine Learning Type: Are we using a Simple ML Average, KNN Mean Average, KNN Exponential Average or None?
KNN Distance Type: We need to check if distance is within the KNN Min/Max distance, which distance checks are we using.
Machine Learning Length: How far back is our Machine Learning going to keep data for.
k-Nearest Neighbour (KNN) Length: How many k-Nearest Neighbours will we account for?
Fast ML Data Length: What is our Fast ML Length? This is used with our Slow Length to create our KNN Distance.
Slow ML Data Length: What is our Slow ML Length? This is used with our Fast Length to create our KNN Distance.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING! Indicator
Machine Learning: Optimal RSI [YinYangAlgorithms]This Indicator, will rate multiple different lengths of RSIs to determine which RSI to RSI MA cross produced the highest profit within the lookback span. This ‘Optimal RSI’ is then passed back, and if toggled will then be thrown into a Machine Learning calculation. You have the option to Filter RSI and RSI MA’s within the Machine Learning calculation. What this does is, only other Optimal RSI’s which are in the same bullish or bearish direction (is the RSI above or below the RSI MA) will be added to the calculation.
You can either (by default) use a Simple Average; which is essentially just a Mean of all the Optimal RSI’s with a length of Machine Learning. Or, you can opt to use a k-Nearest Neighbour (KNN) calculation which takes a Fast and Slow Speed. We essentially turn the Optimal RSI into a MA with different lengths and then compare the distance between the two within our KNN Function.
RSI may very well be one of the most used Indicators for identifying crucial Overbought and Oversold locations. Not only that but when it crosses its Moving Average (MA) line it may also indicate good locations to Buy and Sell. Many traders simply use the RSI with the standard length (14), however, does that mean this is the best length?
By using the length of the top performing RSI and then applying some Machine Learning logic to it, we hope to create what may be a more accurate, smooth, optimal, RSI.
Tutorial:
This is a pretty zoomed out Perspective of what the Indicator looks like with its default settings (except with Bollinger Bands and Signals disabled). If you look at the Tables above, you’ll notice, currently the Top Performing RSI Length is 13 with an Optimal Profit % of: 1.00054973. On its default settings, what it does is Scan X amount of RSI Lengths and checks for when the RSI and RSI MA cross each other. It then records the profitability of each cross to identify which length produced the overall highest crossing profitability. Whichever length produces the highest profit is then the RSI length that is used in the plots, until another length takes its place. This may result in what we deem to be the ‘Optimal RSI’ as it is an adaptive RSI which changes based on performance.
In our next example, we changed the ‘Optimal RSI Type’ from ‘All Crossings’ to ‘Extremity Crossings’. If you compare the last two examples to each other, you’ll notice some similarities, but overall they’re quite different. The reason why is, the Optimal RSI is calculated differently. When using ‘All Crossings’ everytime the RSI and RSI MA cross, we evaluate it for profit (short and long). However, with ‘Extremity Crossings’, we only evaluate it when the RSI crosses over the RSI MA and RSI <= 40 or RSI crosses under the RSI MA and RSI >= 60. We conclude the crossing when it crosses back on its opposite of the extremity, and that is how it finds its Optimal RSI.
The way we determine the Optimal RSI is crucial to calculating which length is currently optimal.
In this next example we have zoomed in a bit, and have the full default settings on. Now we have signals (which you can set alerts for), for when the RSI and RSI MA cross (green is bullish and red is bearish). We also have our Optimal RSI Bollinger Bands enabled here too. These bands allow you to see where there may be Support and Resistance within the RSI at levels that aren’t static; such as 30 and 70. The length the RSI Bollinger Bands use is the Optimal RSI Length, allowing it to likewise change in correlation to the Optimal RSI.
In the example above, we’ve zoomed out as far as the Optimal RSI Bollinger Bands go. You’ll notice, the Bollinger Bands may act as Support and Resistance locations within and outside of the RSI Mid zone (30-70). In the next example we will highlight these areas so they may be easier to see.
Circled above, you may see how many times the Optimal RSI faced Support and Resistance locations on the Bollinger Bands. These Bollinger Bands may give a second location for Support and Resistance. The key Support and Resistance may still be the 30/50/70, however the Bollinger Bands allows us to have a more adaptive, moving form of Support and Resistance. This helps to show where it may ‘bounce’ if it surpasses any of the static levels (30/50/70).
Due to the fact that this Indicator may take a long time to execute and it can throw errors for such, we have added a Setting called: Adjust Optimal RSI Lookback and RSI Count. This settings will automatically modify the Optimal RSI Lookback Length and the RSI Count based on the Time Frame you are on and the Bar Indexes that are within. For instance, if we switch to the 1 Hour Time Frame, it will adjust the length from 200->90 and RSI Count from 30->20. If this wasn’t adjusted, the Indicator would Timeout.
You may however, change the Setting ‘Adjust Optimal RSI Lookback and RSI Count’ to ‘Manual’ from ‘Auto’. This will give you control over the ‘Optimal RSI Lookback Length’ and ‘RSI Count’ within the Settings. Please note, it will likely take some “fine tuning” to find working settings without the Indicator timing out, but there are definitely times you can find better settings than our ‘Auto’ will create; especially on higher Time Frames. The Minimum our ‘Auto’ will create is:
Optimal RSI Lookback Length: 90
RSI Count: 20
The Maximum it will create is:
Optimal RSI Lookback Length: 200
RSI Count: 30
If there isn’t much bar index history, for instance, if you’re on the 1 Day and the pair is BTC/USDT you’ll get < 4000 Bar Indexes worth of data. For this reason it is possible to manually increase the settings to say:
Optimal RSI Lookback Length: 500
RSI Count: 50
But, please note, if you make it too high, it may also lead to inaccuracies.
We will conclude our Tutorial here, hopefully this has given you some insight as to how calculating our Optimal RSI and then using it within Machine Learning may create a more adaptive RSI.
Settings:
Optimal RSI:
Show Crossing Signals: Display signals where the RSI and RSI Cross.
Show Tables: Display Information Tables to show information like, Optimal RSI Length, Best Profit, New Optimal RSI Lookback Length and New RSI Count.
Show Bollinger Bands: Show RSI Bollinger Bands. These bands work like the TDI Indicator, except its length changes as it uses the current RSI Optimal Length.
Optimal RSI Type: This is how we calculate our Optimal RSI. Do we use all RSI and RSI MA Crossings or just when it crosses within the Extremities.
Adjust Optimal RSI Lookback and RSI Count: Auto means the script will automatically adjust the Optimal RSI Lookback Length and RSI Count based on the current Time Frame and Bar Index's on chart. This will attempt to stop the script from 'Taking too long to Execute'. Manual means you have full control of the Optimal RSI Lookback Length and RSI Count.
Optimal RSI Lookback Length: How far back are we looking to see which RSI length is optimal? Please note the more bars the lower this needs to be. For instance with BTC/USDT you can use 500 here on 1D but only 200 for 15 Minutes; otherwise it will timeout.
RSI Count: How many lengths are we checking? For instance, if our 'RSI Minimum Length' is 4 and this is 30, the valid RSI lengths we check is 4-34.
RSI Minimum Length: What is the RSI length we start our scans at? We are capped with RSI Count otherwise it will cause the Indicator to timeout, so we don't want to waste any processing power on irrelevant lengths.
RSI MA Length: What length are we using to calculate the optimal RSI cross' and likewise plot our RSI MA with?
Extremity Crossings RSI Backup Length: When there is no Optimal RSI (if using Extremity Crossings), which RSI should we use instead?
Machine Learning:
Use Rational Quadratics: Rationalizing our Close may be beneficial for usage within ML calculations.
Filter RSI and RSI MA: Should we filter the RSI's before usage in ML calculations? Essentially should we only use RSI data that are of the same type as our Optimal RSI? For instance if our Optimal RSI is Bullish (RSI > RSI MA), should we only use ML RSI's that are likewise bullish?
Machine Learning Type: Are we using a Simple ML Average, KNN Mean Average, KNN Exponential Average or None?
KNN Distance Type: We need to check if distance is within the KNN Min/Max distance, which distance checks are we using.
Machine Learning Length: How far back is our Machine Learning going to keep data for.
k-Nearest Neighbour (KNN) Length: How many k-Nearest Neighbours will we account for?
Fast ML Data Length: What is our Fast ML Length? This is used with our Slow Length to create our KNN Distance.
Slow ML Data Length: What is our Slow ML Length? This is used with our Fast Length to create our KNN Distance.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING! Indicator
Machine Learning: SuperTrend Strategy TP/SL [YinYangAlgorithms]The SuperTrend is a very useful Indicator to display when trends have shifted based on the Average True Range (ATR). Its underlying ideology is to calculate the ATR using a fixed length and then multiply it by a factor to calculate the SuperTrend +/-. When the close crosses the SuperTrend it changes direction.
This Strategy features the Traditional SuperTrend Calculations with Machine Learning (ML) and Take Profit / Stop Loss applied to it. Using ML on the SuperTrend allows for the ability to sort data from previous SuperTrend calculations. We can filter the data so only previous SuperTrends that follow the same direction and are within the distance bounds of our k-Nearest Neighbour (KNN) will be added and then averaged. This average can either be achieved using a Mean or with an Exponential calculation which puts added weight on the initial source. Take Profits and Stop Losses are then added to the ML SuperTrend so it may capitalize on Momentum changes meanwhile remaining in the Trend during consolidation.
By applying Machine Learning logic and adding a Take Profit and Stop Loss to the Traditional SuperTrend, we may enhance its underlying calculations with potential to withhold the trend better. The main purpose of this Strategy is to minimize losses and false trend changes while maximizing gains. This may be achieved by quick reversals of trends where strategic small losses are taken before a large trend occurs with hopes of potentially occurring large gain. Due to this logic, the Win/Loss ratio of this Strategy may be quite poor as it may take many small marginal losses where there is consolidation. However, it may also take large gains and capitalize on strong momentum movements.
Tutorial:
In this example above, we can get an idea of what the default settings may achieve when there is momentum. It focuses on attempting to hit the Trailing Take Profit which moves in accord with the SuperTrend just with a multiplier added. When momentum occurs it helps push the SuperTrend within it, which on its own may act as a smaller Trailing Take Profit of its own accord.
We’ve highlighted some key points from the last example to better emphasize how it works. As you can see, the White Circle is where profit was taken from the ML SuperTrend simply from it attempting to switch to a Bullish (Buy) Trend. However, that was rejected almost immediately and we went back to our Bearish (Sell) Trend that ended up resulting in our Take Profit being hit (Yellow Circle). This Strategy aims to not only capitalize on the small profits from SuperTrend to SuperTrend but to also capitalize when the Momentum is so strong that the price moves X% away from the SuperTrend and is able to hit the Take Profit location. This Take Profit addition to this Strategy is crucial as momentum may change state shortly after such drastic price movements; and if we were to simply wait for it to come back to the SuperTrend, we may lose out on lots of potential profit.
If you refer to the Yellow Circle in this example, you’ll notice what was talked about in the Summary/Overview above. During periods of consolidation when there is little momentum and price movement and we don’t have any Stop Loss activated, you may see ‘Signal Flashing’. Signal Flashing is when there are Buy and Sell signals that keep switching back and forth. During this time you may be taking small losses. This is a normal part of this Strategy. When a signal has finally been confirmed by Momentum, is when this Strategy shines and may produce the profit you desire.
You may be wondering, what causes these jagged like patterns in the SuperTrend? It's due to the ML logic, and it may be a little confusing, but essentially what is happening is the Fast Moving SuperTrend and the Slow Moving SuperTrend are creating KNN Min and Max distances that are extreme due to (usually) parabolic movement. This causes fewer values to be added to and averaged within the ML and causes less smooth and more exponential drastic movements. This is completely normal, and one of the perks of using k-Nearest Neighbor for ML calculations. If you don’t know, the Min and Max Distance allowed is derived from the most recent(0 index of data array) to KNN Length. So only SuperTrend values that exhibit distances within these Min/Max will be allowed into the average.
Since the KNN ML logic can cause these exponential movements in the SuperTrend, they likewise affect its Take Profit. The Take Profit may benefit from this movement like displayed in the example above which helped it claim profit before then exhibiting upwards movement.
By default our Stop Loss Multiplier is kept quite low at 0.0000025. Keeping it low may help to reduce some Signal Flashing while not taking extra losses more so than not using it at all. However, if we increase it even more to say 0.005 like is shown in the example above. It can really help the trend keep momentum. Please note, although previous results don’t imply future results, at 0.0000025 Stop Loss we are currently exhibiting 69.27% profit while at 0.005 Stop Loss we are exhibiting 33.54% profit. This just goes to show that although there may be less Signal Flashing, it may not result in more profit.
We will conclude our Tutorial here. Hopefully this has given you some insight as to how Machine Learning, combined with Trailing Take Profit and Stop Loss may have positive effects on the SuperTrend when turned into a Strategy.
Settings:
SuperTrend:
ATR Length: ATR Length used to create the Original Supertrend.
Factor: Multiplier used to create the Original Supertrend.
Stop Loss Multiplier: 0 = Don't use Stop Loss. Stop loss can be useful for helping to prevent false signals but also may result in more loss when hit and less profit when switching trends.
Take Profit Multiplier: Take Profits can be useful within the Supertrend Strategy to stop the price reverting all the way to the Stop Loss once it's been profitable.
Machine Learning:
Only Factor Same Trend Direction: Very useful for ensuring that data used in KNN is not manipulated by different SuperTrend Directional data. Please note, it doesn't affect KNN Exponential.
Rationalized Source Type: Should we Rationalize only a specific source, All or None?
Machine Learning Type: Are we using a Simple ML Average, KNN Mean Average, KNN Exponential Average or None?
Machine Learning Smoothing Type: How should we smooth our Fast and Slow ML Datas to be used in our KNN Distance calculation? SMA, EMA or VWMA?
KNN Distance Type: We need to check if distance is within the KNN Min/Max distance, which distance checks are we using.
Machine Learning Length: How far back is our Machine Learning going to keep data for.
k-Nearest Neighbour (KNN) Length: How many k-Nearest Neighbours will we account for?
Fast ML Data Length: What is our Fast ML Length?? This is used with our Slow Length to create our KNN Distance.
Slow ML Data Length: What is our Slow ML Length?? This is used with our Fast Length to create our KNN Distance.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING! Strategy
Machine Learning: Trend Lines [YinYangAlgorithms]Trend lines have always been a key indicator that may help predict many different types of price movements. They have been well known to create different types of formations such as: Pennants, Channels, Flags and Wedges. The type of formation they create is based on how the formation was created and the angle it was created. For instance, if there was a strong price increase and then there is a Wedge where both end points meet, this is considered a Bull Pennant. The formations Trend Lines create may be powerful tools that can help predict current Support and Resistance and also Future Momentum changes. However, not all Trend Lines will create formations, and alone they may stand as strong Support and Resistance locations on the Vertical.
The purpose of this Indicator is to apply Machine Learning logic to a Traditional Trend Line Calculation, and therefore allowing a new approach to a modern indicator of high usage. The results of such are quite interesting and goes to show the impacts a simple KNN Machine Learning model can have on Traditional Indicators.
Tutorial:
There are a few different settings within this Indicator. Many will greatly impact the results and if any are changed, lots will need ‘Fine Tuning’. So let's discuss the main toggles that have great effects and what they do before discussing the lengths. Currently in this example above we have the Indicator at its Default Settings. In this example, you can see how the Trend Lines act as key Support and Resistance locations. Due note, Support and Resistance are a relative term, as is their color. What starts off as Support or Resistance may change when the price crosses over / under them.
In the example above we have zoomed in and circled locations that exhibited markers of Support and Resistance along the Trend Lines. These Trend Lines are all created using the Default Settings. As you can see from the example above; just because it is a Green Upwards Trend Line, doesn’t mean it’s a Support Line. Support and Resistance is always shifting on Trend Lines based on the prices location relative to them.
We won’t go through all the Formations Trend Lines make, but the example above, we can see the Trend Lines formed a Downward Channel. Channels are when there are two parallel downwards Trend Lines that are at a relatively similar angle. This means that they won’t ever meet. What may happen when the price is within these channels, is it may bounce between the upper and lower bounds. These Channels may drive the price upwards or downwards, depending on if it is in an Upwards or Downwards Channel.
If you refer to the example above, you’ll notice that the Trend Lines are formed like traditional Trend Lines. They don’t stem from current Highs and Lows but rather Machine Learning Highs and Lows. More often than not, the Machine Learning approach to Trend Lines cause their start point and angle to be quite different than a Traditional Trend Line. Due to this, it may help predict Support and Resistance locations at are more uncommon and therefore can be quite useful.
In the example above we have turned off the toggle in Settings ‘Use Exponential Data Average’. This Settings uses a custom Exponential Data Average of the KNN rather than simply averaging the KNN. By Default it is enabled, but as you can see when it is disabled it may create some pretty strong lasting Trend Lines. This is why we advise you ZOOM OUT AS FAR AS YOU CAN. Trend Lines are only displayed when you’ve zoomed out far enough that their Start Point is visible.
As you can see in this example above, there were 3 major Upward Trend Lines created in 2020 that have had a major impact on Support and Resistance Locations within the last year. Lets zoom in and get a closer look.
We have zoomed in for this example above, and circled some of the major Support and Resistance locations that these Upward Trend Lines may have had a major impact on.
Please note, these Machine Learning Trend Lines aren’t a ‘One Size Fits All’ kind of thing. They are completely customizable within the Settings, so that you can get a tailored experience based on what Pair and Time Frame you are trading on.
When any values are changed within the Settings, you’ll likely need to ‘Fine Tune’ the rest of the settings until your desired result is met. By default the modifiable lengths within the Settings are:
Machine Learning Length: 50
KNN Length:5
Fast ML Data Length: 5
Slow ML Data Length: 30
For example, let's toggle ‘Use Exponential Data Averages’ back on and change ‘Fast ML Data Length’ from 5 to 20 and ‘Slow ML Data Length’ from 30 to 50.
As you can in the example above, all of the lines have changed. Although there are still some strong Support Locations created by the Upwards Trend Lines.
We will conclude our Tutorial here. Hopefully you’ve learned how to use Machine Learning Trend Lines and will be able to now see some more unorthodox Support and Resistance locations on the Vertical.
Settings:
Use Machine Learning Sources: If disabled Traditional Trend line sources (High and Low) will be used rather than Rational Quadratics.
Use KNN Distance Sorting: You can disable this if you wish to not have the Machine Learning Data sorted using KNN. If disabled trend line logic will be Traditional.
Use Exponential Data Average: This Settings uses a custom Exponential Data Average of the KNN rather than simply averaging the KNN.
Machine Learning Length: How strong is our Machine Learning Memory? Please note, when this value is too high the data is almost 'too' much and can lead to poor results.
K-Nearest Neighbour (KNN) Length: How many K-Nearest Neighbours are allowed with our Distance Clustering? Please note, too high or too low may lead to poor results.
Fast ML Data Length: Fast and Slow speed needs to be adjusted properly to see results. 3/5/7 all seem to work well for Fast.
Slow ML Data Length: Fast and Slow speed needs to be adjusted properly to see results. 20 - 50 all seem to work well for Slow.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING! Indicator
Machine Learning : Cosine Similarity & Euclidean DistanceIntroduction:
This script implements a comprehensive trading strategy that adheres to the established rules and guidelines of housing trading. It leverages advanced machine learning techniques and incorporates customised moving averages, including the Conceptive Price Moving Average (CPMA), to provide accurate signals for informed trading decisions in the housing market. Additionally, signal processing techniques such as Lorentzian, Euclidean distance, Cosine similarity, Know sure thing, Rational Quadratic, and sigmoid transformation are utilised to enhance the signal quality and improve trading accuracy.
Features:
Market Analysis: The script utilizes advanced machine learning methods such as Lorentzian, Euclidean distance, and Cosine similarity to analyse market conditions. These techniques measure the similarity and distance between data points, enabling more precise signal identification and enhancing trading decisions.
Cosine similarity:
Cosine similarity is a measure used to determine the similarity between two vectors, typically in a high-dimensional space. It calculates the cosine of the angle between the vectors, indicating the degree of similarity or dissimilarity.
In the context of trading or signal processing, cosine similarity can be employed to compare the similarity between different data points or signals. The vectors in this case represent the numerical representations of the data points or signals.
Cosine similarity ranges from -1 to 1, with 1 indicating perfect similarity, 0 indicating no similarity, and -1 indicating perfect dissimilarity. A higher cosine similarity value suggests a closer match between the vectors, implying that the signals or data points share similar characteristics.
Lorentzian Classification:
Lorentzian classification is a machine learning algorithm used for classification tasks. It is based on the Lorentzian distance metric, which measures the similarity or dissimilarity between two data points. The Lorentzian distance takes into account the shape of the data distribution and can handle outliers better than other distance metrics.
Euclidean Distance:
Euclidean distance is a distance metric widely used in mathematics and machine learning. It calculates the straight-line distance between two points in Euclidean space. In two-dimensional space, the Euclidean distance between two points (x1, y1) and (x2, y2) is calculated using the formula sqrt((x2 - x1)^2 + (y2 - y1)^2).
Dynamic Time Windows: The script incorporates a dynamic time window function that allows users to define specific time ranges for trading. It checks if the current time falls within the specified window to execute the relevant trading signals.
Custom Moving Averages: The script includes the CPMA, a powerful moving average calculation. Unlike traditional moving averages, the CPMA provides improved support and resistance levels by considering multiple price types and employing a combination of Exponential Moving Averages (EMAs) and Simple Moving Averages (SMAs). Its adaptive nature ensures responsiveness to changes in price trends.
Signal Processing Techniques: The script applies signal processing techniques such as Know sure thing, Rational Quadratic, and sigmoid transformation to enhance the quality of the generated signals. These techniques improve the accuracy and reliability of the trading signals, aiding in making well-informed trading decisions.
Trade Statistics and Metrics: The script provides comprehensive trade statistics and metrics, including total wins, losses, win rate, win-loss ratio, and early signal flips. These metrics offer valuable insights into the performance and effectiveness of the trading strategy.
Usage:
Configuring Time Windows: Users can customize the time windows by specifying the start and finish time ranges according to their trading preferences and local market conditions.
Signal Interpretation: The script generates long and short signals based on the analysis, custom moving averages, and signal processing techniques. Users should pay attention to these signals and take appropriate action, such as entering or exiting trades, depending on their trading strategies.
Trade Statistics: The script continuously tracks and updates trade statistics, providing users with a clear overview of their trading performance. These statistics help users assess the effectiveness of the strategy and make informed decisions.
Conclusion:
With its adherence to housing trading rules, advanced machine learning methods, customized moving averages like the CPMA, and signal processing techniques such as Lorentzian, Euclidean distance, Cosine similarity, Know sure thing, Rational Quadratic, and sigmoid transformation, this script offers users a powerful tool for housing market analysis and trading. By leveraging the provided signals, time windows, and trade statistics, users can enhance their trading strategies and improve their overall trading performance.
Disclaimer:
Please note that while this script incorporates established pulsewire housing rules, advanced machine learning techniques, customized moving averages, and signal processing techniques, it should be used for informational purposes only. Users are advised to conduct their own analysis and exercise caution when making trading decisions. The script's performance may vary based on market conditions, user settings, and the accuracy of the machine learning methods and signal processing techniques. The trading platform and developers are not responsible for any financial losses incurred while using this script.
By publishing this script on the platform, traders can benefit from its professional presentation, clear instructions, and the utilisation of advanced machine learning techniques, customised moving averages, and signal processing techniques for enhanced trading signals and accuracy.
I extend my gratitude to PulseWire, LUX ALGO, and JDEHORTY for their invaluable contributions to the trading community. Their innovative scripts, meticulous coding patterns, and insightful ideas have profoundly enriched traders' strategies, including my own. Indicator
Lorentzian Classification Strategy Based in the model of Machine learning: Lorentzian Classification by @jdehorty, you will be able to get into trending moves and get interesting entries in the market with this strategy. I also put some new features for better backtesting results!
Backtesting context: 2022-07-19 to 2023-04-14 of US500 1H by PEPPERSTONE. Commissions: 0.03% for each entry, 0.03% for each exit. Risk per trade: 2.5% of the total account
For this strategy, 3 indicators are used:
Machine learning: Lorentzian Classification by @jdehorty
One Ema of 200 periods for identifying the trend
Supertrend indicator as a filter for some exits
Atr stop loss from Gatherio
Trade conditions:
For longs:
Close price is above 200 Ema
Lorentzian Classification indicates a buying signal
This gives us our long signal. Stop loss will be determined by atr stop loss (white point), break even(blue point) by a risk/reward ratio of 1:1 and take profit of 3:1 where half position will be closed. This will be showed as buy.
The other half will be closed when the model indicates a selling signal or Supertrend indicator gives a bearish signal. This will be showed as cl buy.
For shorts:
Close price is under 200 Ema
Lorentzian Classification indicates a selling signal
This gives us our short signal. Stop loss will be determined by atr stop loss (white point), break even(blue point) by a risk/reward ratio of 1:1 and take profit of 3:1 where half position will be closed. This will be showed as sell.
The other half will be closed when the model indicates a buying signal or Supertrend indicator gives a bullish signal. This will be showed as cl sell.
Risk management
To calculate the amount of the position you will use just a small percent of your initial capital for the strategy and you will use the atr stop loss or last swing for this.
Example: You have 1000 usd and you just want to risk 2,5% of your account, there is a buy signal at price of 4,000 usd. The stop loss price from atr stop loss or last swing is 3,900. You calculate the distance in percent between 4,000 and 3,900. In this case, that distance would be of 2.50%. Then, you calculate your position by this way: (initial or current capital * risk per trade of your account) / (stop loss distance).
Using these values on the formula: (1000*2,5%)/(2,5%) = 1000usd. It means, you have to use 1000 usd for risking 2.5% of your account.
We will use this risk management for applying compound interest.
> In settings, with position amount calculator, you can enter the amount in usd of your account and the amount in percentage for risking per trade of the account. You will see this value in green color in the upper left corner that shows the amount in usd to use for risking the specific percentage of your account.
> You can also choose a fixed amount, so you will have to activate fixed amount in risk management for trades and set the fixed amount for backtesting.
Script functions
Inside of settings, you will find some utilities for display atr stop loss, break evens, positions, signals, indicators, a table of some stats from backtesting, etc.
You will find the settings for risk management at the end of the script if you want to change something or trying new values for other assets for backtesting.
If you want to change the initial capital for backtest the strategy, go to properties, and also enter the commisions of your exchange and slippage for more realistic results.
In risk managment you can find an option called "Use leverage ?", activate this if you want to backtest using leverage, which means that in case of not having enough money for risking the % determined by you of your account using your initial capital, you will use leverage for using the enough amount for risking that % of your acount in a buy position. Otherwise, the amount will be limited by your initial/current capital
I also added a function for backtesting if you had added or withdrawn money frequently:
Adding money: You can choose how often you want to add money (Monthly, yearly, daily or weekly). Then a fixed amount of money and activate or deactivate this function
Withdraw money: You can choose if you want to withdraw a fixed amount or a percentage of earnings. Then you can choose a fixed amount of money, the period of time and activate or deactivate this function. Also, the percentage of earnings if you choosed this option.
Some other assets where strategy has worked
BTCUSD 4H, 1D
ETHUSD 4H, 1D
BNBUSD 4H
SPX 1D
BANKNIFTY 4H, 15 min
Some things to consider
USE UNDER YOUR OWN RISK. PAST RESULTS DO NOT REPRESENT THE FUTURE.
DEPENDING OF % ACCOUNT RISK PER TRADE, YOU COULD REQUIRE LEVERAGE FOR OPEN SOME POSITIONS, SO PLEASE, BE CAREFULL AND USE CORRECTLY THE RISK MANAGEMENT
Do not forget to change commissions and other parameters related with back testing results!. If you have problems loading the script reduce max bars back number in general settings
Strategies for trending markets use to have more looses than wins and it takes a long time to get profits, so do not forget to be patient and consistent !
Please, visit the post from @jdehorty called Machine Learning: Lorentzian Classification for a better understanding of his script!
Any support and boosts will be well received. If you have any question, do not doubt to ask! Strategy
Machine Learning: Lorentzian Classification█ OVERVIEW
A Lorentzian Distance Classifier (LDC) is a Machine Learning classification algorithm capable of categorizing historical data from a multi-dimensional feature space. This indicator demonstrates how Lorentzian Classification can also be used to predict the direction of future price movements when used as the distance metric for a novel implementation of an Approximate Nearest Neighbors (ANN) algorithm.
█ BACKGROUND
In physics, Lorentzian space is perhaps best known for its role in describing the curvature of space-time in Einstein's theory of General Relativity (2). Interestingly, however, this abstract concept from theoretical physics also has tangible real-world applications in trading.
Recently, it was hypothesized that Lorentzian space was also well-suited for analyzing time-series data (4), (5). This hypothesis has been supported by several empirical studies that demonstrate that Lorentzian distance is more robust to outliers and noise than the more commonly used Euclidean distance (1), (3), (6). Furthermore, Lorentzian distance was also shown to outperform dozens of other highly regarded distance metrics, including Manhattan distance, Bhattacharyya similarity, and Cosine similarity (1), (3). Outside of Dynamic Time Warping based approaches, which are unfortunately too computationally intensive for PineScript at this time, the Lorentzian Distance metric consistently scores the highest mean accuracy over a wide variety of time series data sets (1).
Euclidean distance is commonly used as the default distance metric for NN-based search algorithms, but it may not always be the best choice when dealing with financial market data. This is because financial market data can be significantly impacted by proximity to major world events such as FOMC Meetings and Black Swan events. This event-based distortion of market data can be framed as similar to the gravitational warping caused by a massive object on the space-time continuum. For financial markets, the analogous continuum that experiences warping can be referred to as "price-time".
Below is a side-by-side comparison of how neighborhoods of similar historical points appear in three-dimensional Euclidean Space and Lorentzian Space:
This figure demonstrates how Lorentzian space can better accommodate the warping of price-time since the Lorentzian distance function compresses the Euclidean neighborhood in such a way that the new neighborhood distribution in Lorentzian space tends to cluster around each of the major feature axes in addition to the origin itself. This means that, even though some nearest neighbors will be the same regardless of the distance metric used, Lorentzian space will also allow for the consideration of historical points that would otherwise never be considered with a Euclidean distance metric.
Intuitively, the advantage inherent in the Lorentzian distance metric makes sense. For example, it is logical that the price action that occurs in the hours after Chairman Powell finishes delivering a speech would resemble at least some of the previous times when he finished delivering a speech. This may be true regardless of other factors, such as whether or not the market was overbought or oversold at the time or if the macro conditions were more bullish or bearish overall. These historical reference points are extremely valuable for predictive models, yet the Euclidean distance metric would miss these neighbors entirely, often in favor of irrelevant data points from the day before the event. By using Lorentzian distance as a metric, the ML model is instead able to consider the warping of price-time caused by the event and, ultimately, transcend the temporal bias imposed on it by the time series.
For more information on the implementation details of the Approximate Nearest Neighbors (ANN) algorithm used in this indicator, please refer to the detailed comments in the source code.
█ HOW TO USE
Below is an explanatory breakdown of the different parts of this indicator as it appears in the interface:
Below is an explanation of the different settings for this indicator:
General Settings:
Source - This has a default value of "hlc3" and is used to control the input data source.
Neighbors Count - This has a default value of 8, a minimum value of 1, a maximum value of 100, and a step of 1. It is used to control the number of neighbors to consider.
Max Bars Back - This has a default value of 2000.
Feature Count - This has a default value of 5, a minimum value of 2, and a maximum value of 5. It controls the number of features to use for ML predictions.
Color Compression - This has a default value of 1, a minimum value of 1, and a maximum value of 10. It is used to control the compression factor for adjusting the intensity of the color scale.
Show Exits - This has a default value of false. It controls whether to show the exit threshold on the chart.
Use Dynamic Exits - This has a default value of false. It is used to control whether to attempt to let profits ride by dynamically adjusting the exit threshold based on kernel regression.
Feature Engineering Settings:
Note: The Feature Engineering section is for fine-tuning the features used for ML predictions. The default values are optimized for the 4H to 12H timeframes for most charts, but they should also work reasonably well for other timeframes. By default, the model can support features that accept two parameters (Parameter A and Parameter B, respectively). Even though there are only 4 features provided by default, the same feature with different settings counts as two separate features. If the feature only accepts one parameter, then the second parameter will default to EMA-based smoothing with a default value of 1. These features represent the most effective combination I have encountered in my testing, but additional features may be added as additional options in the future.
Feature 1 - This has a default value of "RSI" and options are: "RSI", "WT", "CCI", "ADX".
Feature 2 - This has a default value of "WT" and options are: "RSI", "WT", "CCI", "ADX".
Feature 3 - This has a default value of "CCI" and options are: "RSI", "WT", "CCI", "ADX".
Feature 4 - This has a default value of "ADX" and options are: "RSI", "WT", "CCI", "ADX".
Feature 5 - This has a default value of "RSI" and options are: "RSI", "WT", "CCI", "ADX".
Filters Settings:
Use Volatility Filter - This has a default value of true. It is used to control whether to use the volatility filter.
Use Regime Filter - This has a default value of true. It is used to control whether to use the trend detection filter.
Use ADX Filter - This has a default value of false. It is used to control whether to use the ADX filter.
Regime Threshold - This has a default value of -0.1, a minimum value of -10, a maximum value of 10, and a step of 0.1. It is used to control the Regime Detection filter for detecting Trending/Ranging markets.
ADX Threshold - This has a default value of 20, a minimum value of 0, a maximum value of 100, and a step of 1. It is used to control the threshold for detecting Trending/Ranging markets.
Kernel Regression Settings:
Trade with Kernel - This has a default value of true. It is used to control whether to trade with the kernel.
Show Kernel Estimate - This has a default value of true. It is used to control whether to show the kernel estimate.
Lookback Window - This has a default value of 8 and a minimum value of 3. It is used to control the number of bars used for the estimation. Recommended range: 3-50
Relative Weighting - This has a default value of 8 and a step size of 0.25. It is used to control the relative weighting of time frames. Recommended range: 0.25-25
Start Regression at Bar - This has a default value of 25. It is used to control the bar index on which to start regression. Recommended range: 0-25
Display Settings:
Show Bar Colors - This has a default value of true. It is used to control whether to show the bar colors.
Show Bar Prediction Values - This has a default value of true. It controls whether to show the ML model's evaluation of each bar as an integer.
Use ATR Offset - This has a default value of false. It controls whether to use the ATR offset instead of the bar prediction offset.
Bar Prediction Offset - This has a default value of 0 and a minimum value of 0. It is used to control the offset of the bar predictions as a percentage from the bar high or close.
Backtesting Settings:
Show Backtest Results - This has a default value of true. It is used to control whether to display the win rate of the given configuration.
█ WORKS CITED
(1) R. Giusti and G. E. A. P. A. Batista, "An Empirical Comparison of Dissimilarity Measures for Time Series Classification," 2013 Brazilian Conference on Intelligent Systems, Oct. 2013, DOI: 10.1109/bracis.2013.22.
(2) Y. Kerimbekov, H. Ş. Bilge, and H. H. Uğurlu, "The use of Lorentzian distance metric in classification problems," Pattern Recognition Letters, vol. 84, 170–176, Dec. 2016, DOI: 10.1016/j.patrec.2016.09.006.
(3) A. Bagnall, A. Bostrom, J. Large, and J. Lines, "The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms." ResearchGate, Feb. 04, 2016.
(4) H. Ş. Bilge, Yerzhan Kerimbekov, and Hasan Hüseyin Uğurlu, "A new classification method by using Lorentzian distance metric," ResearchGate, Sep. 02, 2015.
(5) Y. Kerimbekov and H. Şakir Bilge, "Lorentzian Distance Classifier for Multiple Features," Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, 2017, DOI: 10.5220/0006197004930501.
(6) V. Surya Prasath et al., "Effects of Distance Measure Choice on KNN Classifier Performance - A Review." .
█ ACKNOWLEDGEMENTS
@veryfid - For many invaluable insights, discussions, and advice that helped to shape this project.
@capissimo - For open sourcing his interesting ideas regarding various KNN implementations in PineScript, several of which helped inspire my original undertaking of this project.
@RikkiTavi - For many invaluable physics-related conversations and for his helping me develop a mechanism for visualizing various distance algorithms in 3D using JavaScript
@jlaurel - For invaluable literature recommendations that helped me to understand the underlying subject matter of this project.
@annutara - For help in beta-testing this indicator and for sharing many helpful ideas and insights early on in its development.
@jasontaylor7 - For helping to beta-test this indicator and for many helpful conversations that helped to shape my backtesting workflow
@meddymarkusvanhala - For helping to beta-test this indicator
@dlbnext - For incredibly detailed backtesting testing of this indicator and for sharing numerous ideas on how the user experience could be improved. Indicator
