Excellent ADXThe Average Directional movement indeX (ADX) is an indicator that helps you determine the trend direction, pivot points, and much more else! But it looks not so easy as other famous indicators. It seems strange or even terrible, but don't be afraid. Let's understand how it works and get its power into your analysis tactics.
In the beginning, imagine a drunk man goes through a ladder: step by step. Up, up, down, up, down, down, up...
How can we understand which direction he goes? Exactly! We can count the number of steps in each direction. In the above example, in the upward – 4, in the downward – 3. So, it looks like he goes in an upward direction.
The ADX indicator counts the same steps, but for price. The size of each step equals 1 ATR for "DI Length" candles. On the indicator chart, we have the green and red lines. The green line represents a number of steps upward. The red line shows one downward. When the red line upper green, then the price goes below, then the trend is directed down. Later the green line comes above the red one, and then the trend changes the direction to upward. Wow? After that, you can easy detect the trend direction on the market!
But it is still not the end. On the chart, we also have the fat blue line. This is the ADX line, and it represents the power of the trend. It is calculated from a distance between the green and red curves. The ADX line value grows if the distance is increased. If the movement is really powerful, then a number of steps into a direction much more prominent than one in an opposed direction. Then the blue line grows faster. But if the growth has stopped and the blue line turns back or already had changed self-direction, then it is a signal that the trend has ended too. It's an excellent sign to close the position (but not always). Easy? Not quite. Thresholds help you there. The indicator has two additional parameters: upper and lower thresholds to evaluate the trend-over signal strength. An u-turn of the ADX line above the upper threshold sends a strong signal. If one occurs between both thresholds, it is a bit weak signal. But if the blue line goes below the lower threshold, it looks like there is no trend, and the price goes side. We can also say that the price goes side when the ADX value gradually falls down.
The Excellent ADX indicator helps you catch pivot/pullback signals based on green, red, and blue lines. Each such signal is highlighted as a green (buy) or red (sell) dot on the plot. The size of the dot represents the strength of the signal. You can also check the position of green and red lines from each other to determine the trend direction and the place where it has been changed. The Excellent ADX indicator helps you there too. It highlights the trend direction by the background-color, so you'll never miss it! The Excellent ADX good compliance with the Price Channel indicator built for the same length. You can use them together to be on a trend wave always!
Wyszukaj w skryptach "curve"
MACD ProMoving average convergence divergence pro.
Original MACD with new features, Including...
1. Three different modes.
Basic, Logarithmic, Percent (calculates difference of oscillator MAs in percent)
2. Additional moving averages for oscillator, signal and even histogram.
EMA, WMA (linearly weighted), LMA (logarithmically weighted), SMA
Volume Weighted RMA (I've been suggested to make a MACD with the VWEMA that I published recently but that was too fast, this almost 2 times slower because of using RMA instead of EMA)
VWRMA(s) (an alternative for VWRMA which uses candle formation to simulate the volume, can be useful when volume is not provided for the symbol or it is not proper)
And DEMA (Double Exponential MA)
3. Signal Displacement.
If you want to add some delay to signal, could help for extra confirmation of center crosses and removal of some falss ones.
4. Histogram Smoother.
For those who like the smooth curves. Can deliver a cleaner histogram even in volatile markets.
5. Bar color for more fun.
MAX TRENDS Spark 0.3.1.1This is a solid modification of Waves with extra volatility curves.
Very sophisticated for the day trading and forex swing.
XBT Contango Calculator v1.1
This indicator measures value of basis (or spread) of current Futures contracts compared to spot. The default settings are specifically for Bitmex XBTU19 and XBTZ19 futures contracts. These will need to be updated after expiration. Also, it seems that Tradingview does not keep charts of expired contracts. If anyone knows how to import data from previous expired contracts, please let me know. This historical data could be valuable for evaluating previous XBT futures curves.
Also, VERY important to understand is this indicator only works with Spot Bitcoin charts (XBTUSD, BTCUSD, etc). If you add this to any other asset chart, it would not be useful (unless you changed settings to evaluate a different Futures product).
Contango and Backwardation are important fundamental indicators to keep track of while trading Futures markets. For a better explanation, Ugly Old Goat had done several medium articles on this. Please check out link below for his latest article on the subject...
uglyoldgoat.com
Notes on chart above should explain most of what you need to know on to use this indicator. The zero line is the spot price on the chart, so a positive value means Futures are trading at a premium (or in Contango). You can set a value of extreme Contango which will give an alert as red background (default setting is +$500). Green background will appear when Futures are trading at a discount to spot (Backwardation).
Hope some people get some use out of this. This is my first attempt at coding anything, so any feedback would be greatly appreciated!
BTC Donations: 3CypEdvBcvVHbqzHUt1FDiUG53U7pYWviV
Moving AverageDisplay of simple moving average and exponential mobile average depending on period.
Simple moving average are for D, W, and M period.
Minutes and Hours periods display exponential curves.
4H HOD/LOD Checkpoint Analysis4H HOD/LOD Checkpoint Analysis - Detailed User Guide
OVERVIEW
This indicator is a data-driven probability framework for NQ Futures traders that predicts High-of-Day (HOD) and Low-of-Day (LOD) placement based on statistical analysis of 3,136+ trading days (2013-2025). Unlike traditional indicators that rely on technical signals, this tool uses checkpoint-based state analysis with zero forward-looking bias to provide real-time probabilities of whether the daily range is complete.
⚠️ IMPORTANT: This indicator is specifically designed for NQ FUTURES ONLY. All probabilities, patterns, and statistics were derived from a 10+ year historical dataset of NQ 1-minute bars. Using this on other instruments will produce inaccurate results.
CORE CONCEPT: CHECKPOINT METHODOLOGY
What is a Checkpoint?
A checkpoint occurs when a 4-hour candle closes. At this moment, the indicator "locks" the current market state and calculates probabilities for the remainder of the trading day. The key innovation is that state never changes after locking - probabilities remain constant throughout the session until the next checkpoint.
The Six 4-Hour Candles (EST):
6PM (18:00-22:00) - Evening/Globex open
10PM (22:00-02:00) - Asia session
2AM (02:00-06:00) - Early London
6AM (06:00-10:00) - Late London + NY Open
10AM (10:00-14:00) - NY Morning
2PM (14:00-17:00) - NY Afternoon (3 hours only)
Five Checkpoints:
10PM Checkpoint - After 6PM closes
2AM Checkpoint - After 10PM closes
6AM Checkpoint - After 2AM closes
10AM Checkpoint - After 6AM closes (most critical)
2PM Checkpoint - After 10AM closes (highest conviction fade signals)
HOW IT WORKS: THE THREE-FACTOR STATE SYSTEM
At each checkpoint, the indicator evaluates three critical factors to determine probability:
1. ELIMINATIONS (Quantity)
An "elimination" occurs when a candle trades beyond a previous candle's high or low, effectively removing that candle from contention for HOD/LOD.
Example at 10AM Checkpoint:
6PM high = 18,000
10PM high = 18,050 (eliminates 6PM high)
2AM high = 18,100 (eliminates 10PM high)
6AM high = 18,075 (does NOT eliminate 2AM high)
Result: 2 eliminations
The number of eliminations indicates trend strength:
0 eliminations = Range-bound, high probability extremes already set
1-2 eliminations = Moderate trend
3-4 eliminations = Strong trend day, range likely to extend
2. STRUCTURE (Pattern Type)
The indicator distinguishes between two elimination patterns:
Sequential: Eliminations occur in order (6pm → 10pm → 2am → 6am → 10am)
Indicates smooth, consistent trend
Example: 10pm eliminates 6pm, then 2am eliminates 10pm (sequential)
Skip: Eliminations skip candles
Indicates choppy/reversal behavior
Example: 2am eliminates 6pm but NOT 10pm (skip pattern)
Why it matters: Skip patterns show 2X probability differences compared to sequential patterns. At 10AM checkpoint with 2 eliminations, skip pattern shows 64% participation rate vs 36% for sequential pattern with previous survived.
3. PREVIOUS CANDLE STATUS
Did the immediately prior candle get eliminated?
Eliminated: Previous candle's high/low was taken out
Indicates relentless trend
Higher probability of continuation
Survived: Previous candle's high/low still intact
Indicates trend pause
Higher probability of mean reversion or range completion
Critical insight: High and low are tracked separately. At 2AM checkpoint, 10PM might have eliminated 6PM high (relentless uptrend) but NOT eliminated 6PM low (low survived). This creates different probabilities for HOD vs LOD.
VISUAL ELEMENTS
4-Hour Candle Boxes
Each 4H candle is displayed as a colored box showing its range:
Gray = 6PM (evening)
Blue = 10PM (Asia)
Purple = 2AM (early London)
Orange = 6AM (London + NY Open) - THE CURVE SESSION
Teal = 10AM (NY morning) - THE MONEY SESSION
Red = 2PM (NY afternoon) - THE FADE SESSION
HOD/LOD Lines
Black horizontal lines extend from current HOD/LOD with labels showing:
Which candle set the extreme
Current price level
THE CHECKPOINT TABLE EXPLAINED
Table Header:
Shows current checkpoint (e.g., "🎯 10AM CHECKPOINT") or "⏳ PRE-CHECKPOINT" if between checkpoints.
Main Metrics (Side-by-Side Comparison):
The table displays HOD and LOD separately in two columns because they can have different patterns:
METRIC
HODLOD Eliminations
Number of candles eliminated so far for highs
Number of candles eliminated so far for lows
Structure
Sequential or Skip pattern for highs
Sequential or Skip pattern for lows
Prev Candle
Was previous candle's high eliminated or did it survive?
Was previous candle's low eliminated or did it survive?
Pattern
Combined interpretation: Relentless/Paused/Skip/Early
Combined interpretation: Relentless/Paused/Skip/Early
Color Coding:
Structure Row:
White = Sequential (smooth trend)
Orange = Skip (choppy/reversal)
Previous Candle Row:
Red = Eliminated (relentless trend continuing)
Blue = Survived (trend paused)
Pattern Row:
Red = Relentless (previous eliminated + sequential = strong trend)
Blue = Paused (previous survived + sequential = trend pause)
Orange = Skip/Chop (skip pattern = reversal likely)
Gray = Early (0-1 eliminations, too early to tell)
Probability Section:
Prob Already In: Percentage chance that HOD/LOD has already been set
Color coding:
Green (>75%) = High confidence extreme is in, FADE
Yellow (45-75%) = Moderate confidence
Red (<45%) = Low confidence extreme is in, CONTINUATION likely
Sample Size: Shows how many historical occurrences match this exact state (n=XXX)
Larger samples = higher confidence
Most common states have n=500-2,000+
Current: Which candle currently holds HOD/LOD
Pattern Guide Section:
Appears when you have 2+ eliminations. Provides interpretation:
📈 Paused: Trend has paused, 2pm more likely to set extreme
📈 Relentless: Breaking higher/lower, continuation expected
📈 Skip/Chop: Choppy pattern, next session likely
Same for lows with 📉 symbol.
PRACTICAL TRADING EXAMPLES
Example 1: High Conviction Fade Setup
State at 10AM Checkpoint:
Eliminations: 0 (both HOD/LOD)
Structure: None (no eliminations yet)
Prev Candle: Survived
Table shows:
HOD Prob Already In: 68.9% (n=582)
LOD Prob Already In: 73.6% (n=785)
Interpretation: Range is likely complete. Fade extremes. With 0 eliminations and 70%+ probability, this is a high-conviction mean reversion signal.
Example 2: Strong Continuation Signal
State at 10AM Checkpoint:
Eliminations: 3 (both HOD/LOD)
Structure: Sequential
Prev Candle: Eliminated (relentless)
Table shows:
HOD Prob Already In: 29.8% (n=1,758)
LOD Prob Already In: 34.6% (n=1,451)
Pattern: 📈 Relentless / 📉 Relentless
Interpretation: Strong trend day. Only 30-35% chance range is complete. Look for breakouts in direction of trend. 10AM and 2PM likely to extend range.
Example 3: Pattern Structure Edge
State at 10AM Checkpoint:
Eliminations: 2 (HOD)
Structure: Skip (orange background)
Prev Candle: Eliminated vs Alternative State:
Eliminations: 2 (HOD)
Structure: Sequential
Prev Candle: Survived
Result: Skip pattern shows 64% chance 10AM participates vs 36% for sequential+survived. Skip pattern = 2X more likely to see 10AM high. This structural edge is unique to this indicator.
Example 4: Different HOD vs LOD Patterns
State at 10AM Checkpoint:
HOD: 2 eliminations, Sequential, Previous Eliminated (Relentless) = 46.7% in
LOD: 2 eliminations, Skip, Previous Eliminated (Choppy) = 48.4% in
Interpretation: Highs show relentless uptrend but lows show choppy behavior. This divergence suggests potential for upside continuation but with volatility. Not a clean trend day.
KEY CHECKPOINT STATISTICS (DERIVED FROM 10-YEAR DATASET)
10PM Checkpoint (After 6PM):
Very early in day
13.5% HOD in, 21.3% LOD in
Most likely outcome: Range extends into 6AM/10AM
2AM Checkpoint (After 10PM):
Still early
With 0 elims: 22-31% in (balanced)
With 1 elim: 8-12% in (strong trend signal)
6AM Checkpoint (After 2AM) - Critical Decision Point:
With 0 elims: 40-47% in (balanced, could go either way)
With 2 elims: 18-22% in (strong trend into 6AM/10AM)
Most likely outcome: 10AM sets extremes (~38-40%)
10AM Checkpoint (After 6AM) - Highest Conviction:
With 0 elims: 69-74% in → FADE (high confidence)
With 3 elims: 30-35% in → BUY/SELL continuation
This is THE money checkpoint for high-probability setups
2PM Checkpoint (After 10AM) - Maximum Fade Conviction:
With 0-3 elims: 67-95% in → FADE strongly
With 4 elims: 49-61% in (monster trend, weaker fade)
2PM is primarily a mean reversion session
UNDERSTANDING THE UNDERLYING DATA
All probabilities are derived from analysis of:
Instrument: NQ Futures (E-mini NASDAQ-100)
Timeframe: 1-minute bars
Period: January 2013 - December 2025
Sample: 3,136+ complete trading days
Methodology: Real-time checkpoint analysis with zero forward-looking bias
Why NQ-Specific?
Each futures contract has unique:
Session characteristics (6AM in NQ shows 60-64% curve behavior, other sessions differ)
Timing patterns (NQ's 10AM session has 67-74% immediate takeouts)
Volatility profiles (NQ 2PM shows 56% bullish bias vs ES shows different bias)
Using this indicator on ES, RTY, or other instruments will produce inaccurate results because the probability tables are NQ-specific.
ORIGINALITY & INNOVATION
What Makes This Indicator Unique:
Zero Forward-Looking Bias: State locks at checkpoint moments. Traditional indicators recalculate continuously, introducing bias. This indicator freezes probabilities at the exact moment a 4H candle closes.
Three-Factor State System: Combines elimination count, structure pattern, and previous candle status. Most indicators only track one dimension. This multi-factor approach provides 2X+ probability differentials.
Separate HOD/LOD Tracking: Highs and lows can have different patterns simultaneously (relentless high with choppy low). This indicator tracks them separately for precision.
Pattern Structure Analysis: Distinguishes between sequential and skip patterns, a concept not found in standard indicators. Skip patterns show mean reversion while sequential shows continuation.
10+ Year Statistical Foundation: Every probability is backed by hundreds to thousands of historical occurrences (sample sizes shown in table). Not based on theories or assumptions.
Checkpoint-Specific Probabilities: Different checkpoints have different probability profiles. 10AM checkpoint with 0 eliminations = 70%+ fade. 6AM checkpoint with same state = 40%+ fade. Context matters.
HOW TO USE THIS INDICATOR
Step 1: Wait for Checkpoint
The table will show "⏳ PRE-CHECKPOINT" until a 4H candle closes. Probabilities are only valid at checkpoint moments.
Step 2: Read the State
Check the three factors:
How many eliminations?
Sequential or skip?
Previous candle eliminated or survived?
Step 3: Check Probability
Look at "Prob Already In" percentage:
>75% (Green) = High confidence extreme is set, fade
45-75% (Yellow) = Moderate confidence, use other confirmation
<45% (Red) = Low confidence extreme is set, continuation likely
Step 4: Check Sample Size
Larger sample (n=1,000+) = higher confidence
Smaller sample (n=50-200) = use caution, edge is real but less robust
Step 5: Consider Pattern
Read the pattern guide:
Relentless = trend continuing
Paused = trend stalled, mean reversion
Skip/Chop = reversal/range likely
Step 6: Compare HOD vs LOD
If both show similar patterns = cleaner signal
If divergent patterns = complex day, be cautious
BEST PRACTICES
Focus on 10AM and 2PM checkpoints - These have the highest conviction signals
Combine with price action - Don't fade blindly at 90% probability if price is breaking out strongly
Larger samples = better edges - Prioritize setups with n=500+
Watch for pattern divergence - When HOD and LOD show different patterns, expect complexity
Remember session characteristics:
6AM = THE CURVE SESSION (60-64% mean reversion when Q2 breaks Q1)
10AM = THE MONEY SESSION (67-74% immediate takeouts, highest conviction)
2PM = THE FADE SESSION (67-95% extremes already in)
SETTINGS
Show 4H Candle Boxes - Display colored boxes for each 4H candle
Show HOD/LOD Lines - Display horizontal lines at current extremes
Show Checkpoint Analysis - Display probability table
Table Position - Choose where to place the checkpoint table
Table Size - Tiny/Small/Normal
Colors - Customize box colors for each session
LIMITATIONS & DISCLAIMERS
NQ FUTURES ONLY - Do not use on other instruments
Not a standalone system - Use as confluence with your strategy
Historical data - Past performance doesn't guarantee future results
Sample size variance - Some states have smaller samples, use judgment
Requires understanding - Read this guide fully before trading with this tool
FINAL NOTES
This indicator represents 10+ years of NQ futures data distilled into actionable, real-time probabilities. The checkpoint methodology ensures zero forward-looking bias, while the three-factor state system provides granular edge that traditional indicators miss.
Remember: This tool provides probabilities, not certainties. Trade with proper risk management, and use this as one input in your decision-making process.
Hyper Insight MA Strategy [Universal]Hyper Insight MA Strategy ** is a comprehensive trend-following engine designed for traders who require precision and flexibility. Unlike standard indicators that lock you into a single calculation method, this strategy serves as a "Universal Adapter," allowing you to **Mix & Match 13 different Moving Average types** for both the Fast and Slow trend lines independently.
Whether you need the smoothness of T3, the responsiveness of HMA, or the classic reliability of SMA, this script enables you to backtest thousands of combinations to find the perfect edge for your specific asset class.
---
🔬 Deep Dive: Calculation Logic of Included MAs
This strategy includes 13 distinct calculation methods. Understanding the math behind them will help you choose the right tool for your specific market conditions.
#### 1. Standard Averages
* **SMA (Simple Moving Average):** The unweighted mean of the previous $n$ data points.
* *Logic:* Treats every price point in the period with equal importance. Good for identifying long-term macro trends but reacts slowly to recent volatility.
* **WMA (Weighted Moving Average):** A linear weighted average.
* *Logic:* Assigns heavier weight to current data linearly (e.g., $1, 2, 3... n$). It reacts faster than SMA but is still relatively smooth.
* **SWMA (Symmetrically Weighted Moving Average):**
* *Logic:* Uses a fixed-length window (usually 4 bars) with symmetrical weights $ $. It prioritizes the center of the recent data window.
#### 2. Exponential & Lag-Reducing Averages
* **EMA (Exponential Moving Average):**
* *Logic:* Applies an exponential decay weighting factor. Recent prices have significantly more impact on the average than older prices, reducing lag compared to SMA.
* **RMA (Running Moving Average):** Also known as Wilder's Smoothing (used in RSI).
* *Logic:* It is essentially an EMA but with a slower alpha weight of $1/length$. It provides a very smooth, stable line that filters out noise effectively.
* **DEMA (Double Exponential Moving Average):**
* *Logic:* Calculated as $2 \times EMA - EMA(EMA)$. By subtracting the "lag" (the smoothed EMA) from the original EMA, DEMA provides a much faster reaction to price changes with less noise than a standard EMA.
* **TEMA (Triple Exponential Moving Average):**
* *Logic:* Calculated as $3 \times EMA - 3 \times EMA(EMA) + EMA(EMA(EMA))$. This effectively eliminates the lag inherent in single and double EMAs, making it an extremely fast-tracking indicator for scalping.
#### 3. Advanced & Adaptive Averages
* **HMA (Hull Moving Average):**
* *Logic:* A composite formula involving Weighted Moving Averages: ASX:WMA (2 \times Integer(n/2)) - WMA(n)$. The result is then smoothed by a $\sqrt{n}$ WMA.
* *Effect:* It eliminates lag almost entirely while managing to improve curve smoothness, solving the traditional trade-off between speed and noise.
* **ZLEMA (Zero Lag Exponential Moving Average):**
* *Logic:* This calculation attempts to remove lag by modifying the data source before smoothing. It calculates a "lag" value $(length-1)/2$ and applies an EMA to the data: $Source + (Source - Source )$. This creates a projection effect that tracks price tightly.
* **T3 (Tillson T3 Moving Average):**
* *Logic:* A complex smoothing technique that runs an EMA through a filter multiple times using a "Volume Factor" (set to 0.7 in this script).
* *Effect:* It produces a curve that is incredibly smooth and free of "overshoot," making it excellent for filtering out market chop.
* **ALMA (Arnaud Legoux Moving Average):**
* *Logic:* Uses a Gaussian distribution (bell curve) to assign weights. It allows the user to offset the moving average (moving the peak of the weight) to align it perfectly with the price, balancing smoothness and responsiveness.
* **LSMA (Least Squares Moving Average):**
* *Logic:* Calculates the endpoint of a Linear Regression line for the lookback period. It essentially guesses where the price "should" be based on the best-fit line of the recent trend.
* **VWMA (Volume Weighted Moving Average):**
* *Logic:* Weights the closing price by the volume of that bar.
* *Effect:* Prices on high volume days pull the MA harder than prices on low volume days. This is excellent for validating true trend strength (i.e., a breakout on high volume will move the VWMA significantly).
---
### 🛠 Features & Settings
* **Universal Switching:** Change the `Fast MA` and `Slow MA` types instantly via the settings menu.
* **Trend Cloud:** A dynamic background fill (Green/Red) highlights the crossover zone for immediate visual trend identification.
* **Strategy Mode:** Built-in Backtesting logic triggers `LONG` entries when Fast MA crosses over Slow MA, and `EXIT` when Fast MA crosses under.
### ⚠️ Disclaimer
This script is intended for educational and research purposes. The wide variety of MA combinations can produce vastly different results. Past performance is not indicative of future results. Please use proper risk management.
UM VIX30/VIX Regime & Volatility Roll Yield
SUMMARY
A front-of-the-curve volatility indicator that compares spot VIX to a synthetic 30-day VIX (VIX30) built from VX1/VX2 futures, revealing early volatility pressure, regime shifts, and roll-yield transitions. Ideal for timing long/short volatility trades in VXX, UVXY, SVIX, and VIX futures.
DESCRIPTION
This indicator compares spot VIX to a synthetic 30-day constant-maturity volatility estimate (“VIX30”) built from VX1 and VX2 futures. The VIX30/VIX Ratio reveals short-term volatility pressure and regime shifts that traditional VX1/VX2 roll-yield alone often misses.
VIX30 is constructed using true calendar-day interpolation between VX1 and VX2, with VX1% and VX2% showing the real-time weights behind the 30-day volatility anchor. The table displays the volatility regime, the VX1/VX2 weights, spot-term roll yield (VIX30/VIX), and futures-term roll yield (VX2/VX1), giving a complete, front-of-the-curve perspective on volatility dynamics.
Use this to spot early volatility expansions, collapsing contango, and regime transitions that influence VXX, UVXY, SVIX, VX options, and VIX futures.
HOW IT WORKS
The script calculates the exact calendar days to expiration for the front two VIX futures. It then applies linear interpolation to blend VX1 and VX2 into a 30-day constant-maturity synthetic volatility measure (“VIX30”). Comparing VIX30 to spot VIX produces the VIX30/VIX Ratio, which highlights short-term volatility pressure and regime direction. A full term-structure table summarizes regime, VX1%/VX2% weights, and both spot-term and futures-term roll yields.
DEFAULT SETTINGS
VX1! and VX2! are used by default for front-month and second-month futures. These may be manually overridden if TradingView rolls contracts early. The default timeframe is 30 minutes, and the VIX30/VIX Ratio uses a 21-period EMA for regime smoothing. The historical threshold is set to 1.08, reflecting the long-run average relationship between VIX30 and VIX.
SUGGESTED USES
• Identify early volatility expansions before they appear in VX1/VX2 roll yield.
• Confirm contango/backwardation shifts with front-of-curve context.
• Time long/short volatility trades in VXX, UVXY, SVIX, and VX options.
• Monitor regime transitions (Low → Cautionary → High) to anticipate trend inflections.
• Combine with price action, Nadaraya-Watson trends, or MA color-flip systems for higher-confidence entries.
• MA red → green flips may signal opportunities to short volatility or increase equity exposure.
• MA green → red flips may signal opportunities to go long volatility, reduce equity exposure, or take short-equity positions.
ALERTS
Alerts trigger when the ratio crosses above or below the historical threshold or when the moving-average slope flips direction. A green flip signals rising volatility pressure; a red flip signals fading or collapsing volatility. These alert conditions can be used to automate long/short volatility bias shifts or trade-entry notifications.
FURTHER HINTS
• Increasing orange/red in the table suggests an emerging higher-volatility environment.
• SVIX (inverse volatility ETF) can trend strongly when volatility decays; on a 6-hour chart, MA green flips often align with attractive short-volatility opportunities.
• For long-volatility trades, consider shrinking to a 30-minute chart and watching for MA green → red flips as early entry cues.
• Experiment with different timeframes and smoothing lengths to match your trading style.
• Higher VIX30/VIX and VX2/VX1 roll yields generally imply faster decay in VXX, UVXY, and UVIX — or stronger upside momentum in SVIX.
• The author likes the 6-hour chart for short vol, and the 30-minute chart for long vol. Long vol trades are fast and furious so you want to be quick.
NEURAL FLOW INDEX — Core Energy • Momentum Stream • Pulse SyncNeural Flow Index (NFI) — Advanced Triple-Layer Reversal Framework
The Neural Flow Index (NFI) is a next-generation market oscillator designed to reveal the hidden synchronization between trend energy, cyclical momentum, and internal pulse dynamics.
It merges three powerful analytical layers into a single, normalized view:
Core Energy Curve (based on RSO logic) — captures structural trend bias and volatility expansion.
Momentum Stream (WaveTrend algorithm) — visualizes cyclical motion of price waves.
Pulse Sync (Stochastic RSI adaptation) — measures short-term momentum rhythm and overextension.
Each layer feeds into a unified flow model that adapts to both trend-following and reversal conditions. The goal is not to chase every fluctuation, but to sense where momentum, direction, and volatility converge into true inflection points.
Conceptual Mechanics
The oscillator translates complex market behavior into an elegant, multi-phase signal system:
Core Energy Curve (RSO foundation):
A smoothed dynamic field representing the overall strength and direction of market pressure.
Green energy indicates expansion (bullish dominance); red energy reflects contraction (bearish decay).
Momentum Stream (WaveTrend):
The teal line functions like an electro-wave, oscillating through phases of expansion and exhaustion.
It provides the heartbeat of the market — smooth, rhythmic, and beautifully cyclic.
Pulse Sync (Stochastic RSI):
The purple line acts as the market’s nervous pulse, reacting to micro-momentum changes before the larger trend adjusts.
It identifies micro-tops and micro-bottoms that precede major trend shifts.
When these three forces align, they create high-probability reversal zones known as Neural Nodes — regions where energy, momentum, and rhythm converge.
Trading Logic
Potential Entry Zones:
When the purple Pulse Sync line crosses the green Momentum Stream near the lower or upper bounds of the oscillator, a potential turning point forms.
Yet, these crossovers are only validated when the Core Energy histogram (RSO) simultaneously supports the same direction — confirming that energy and rhythm are synchronized.
Histogram Confirmation:
The histogram is the “voice” of the oscillator.
Rising green volume within the histogram during a Pulse-Momentum crossover suggests a legitimate upward reversal.
Conversely, expanding red energy during an upper-band cross indicates momentum exhaustion and an early short-side opportunity.
Neutral Zones:
When all three layers flatten near the zero line, the market enters an equilibrium phase — no clear trend dominance, ideal for patience and re-entry planning.
| Layer | Representation | Color | Function |
| --------------------- | ------------------- | ----------------- | ------------------------------ |
| **Core Energy Curve** | Area / Histogram | Lime-Red gradient | Trend bias & volatility energy |
| **Momentum Stream** | WaveTrend line | Teal | Cyclical flow of price |
| **Pulse Sync** | Stochastic RSI line | Purple | Short-term momentum rhythm |
Interpretation Summary
Converging Waves: Trend, momentum, and pulse move together → strong continuation.
Diverging Waves: Pulse or Momentum decouple from Core Energy → early reversal warnings.
Histogram Expansion: Confirms direction and strength of the new wave.
Crossovers at Extremes: Potential entries, especially when confirmed by energy alignment.
🪶 Philosophy Behind NFI
The Neural Flow Index is not just a technical indicator — it’s a behavioral visualization system.
Instead of focusing on lagging confirmations, it captures the neural pattern of price motion:
how liquidity flows, contracts, and expands through time.
It bridges the gap between pure mathematics and market intuition — giving traders a cinematic, harmonic view of energy transition inside price structure.
Financial-Conditions Brake Index (FCBI) — US10Y brake on USIRYYFinancial-Conditions Brake Index (FCBI) – US10Y Brake on USIRYY
Concept
The Financial-Conditions Brake Index (FCBI) measures how U.S. long-term yields (US10Y) interact with the Federal Funds Rate (USINTR) and inflation (CPI YoY) to shape real-rate conditions (USIRYY).
It visualizes whether the bond market is tightening or loosening overall financial conditions relative to the Federal Reserve’s policy stance.
Formula
FCBI = (US10Y) − (USINTR) − (CPI YoY)
How It Works
The FCBI expresses the difference between the long-term yield curve and short-term policy rates, adjusted for inflation. It shows whether the long end of the curve is amplifying or counteracting the Fed’s stance.
FCBI > +2 → Strong brake → Long yields remain elevated despite easing → tight conditions → recession delayed.
FCBI +1 to +2 → Mild brake → Financial transmission slower; lag ≈ 12–18 months.
FCBI 0 to +1 → Neutral → Typical early post-cut environment.
FCBI < 0 → Accelerator → Long yields and inflation expectations falling → liquidity flows freely → recession often follows within 6–14 months.
How to Read the Chart
Blue line (FCBI) shows the strength of the financial brake.
Red line (USIRYY) represents the real yield baseline.
Recession shading (gray) marks NBER recessions for comparison.
FCBI < USIRYY → Brake engaged → financial conditions tighter than real-rate baseline.
FCBI > USIRYY → Brake released → long end easing faster than policy → liquidity surge → late-cycle setup.
Historically, U.S. recessions begin on average about 14 months after the first Fed rate cut, and a decline of the FCBI below zero often precedes that window.
Practical Use
Use the FCBI to identify when policy transmission is blocked (brake engaged) or flowing (brake released).
Cross-check with yield-curve inversions, Fed policy shifts, and inflation expectations to estimate macro timing windows.
Current Example (Oct 2025)
FCBI ≈ −3.1, USIRYY ≈ +3.0 → Brake still engaged.
Once FCBI rises above USIRYY and crosses positive, it signals the “brake released” phase — historically the final liquidity surge before a U.S. recession.
Summary
FCBI shows how tight the brake is.
USIRYY shows how fast the car is moving.
When FCBI rises above USIRYY, the brake is released — liquidity accelerates and the historical recession countdown begins.
Chart Fusion Line SND Detection by TitikSona🧭 Overview
Fusion Line Momentum Analyzer is a momentum visualization tool that introduces a unified model of oscillator fusion.
It blends Fast and Slow Stochastics with RSI into one adaptive curve, designed to eliminate conflicting signals between different momentum sources.
Instead of reading three separate oscillators, the Fusion Line provides a consolidated view of strength and exhaustion zones in a single framework.
This approach helps analysts detect aligned momentum shifts with greater clarity and less noise, without repainting or lagging methods.
⚙️ Core Concept
Traditional oscillators often provide conflicting readings when volatility changes.
To solve this, the Fusion Line averages three normalized components:
Fast Stochastic (12,3,3) — reacts quickly to short-term momentum spikes.
Slow Stochastic (100,8,8) — filters long-term momentum context.
RSI (26) — measures internal strength between buying and selling pressure.
Each is rescaled to a 0–100 range, then averaged into a single curve called the Fusion Line.
A secondary Signal Line (SMA 9) is added to visualize directional confirmation.
This combination aims to preserve responsiveness from the fast components while maintaining structural stability from the slow and RSI layers.
🌈 Features
Unified momentum curve combining stochastic and RSI dynamics.
Automatic bias shading to highlight dominant trend direction.
Real-time percentage strength meter (visual intensity).
Configurable alert triggers on key momentum zones (20/80).
Clean chart display without unnecessary elements or overlays.
📘 Interpretation
Rising Fusion Line → indicates strengthening bullish momentum.
Falling Fusion Line → indicates strengthening bearish pressure.
Fusion values below 20 → potential oversold recovery.
Fusion values above 80 → possible exhaustion or reversal zone.
Mid-zone movement → reflects equilibrium or sideways momentum.
These readings should always be combined with higher timeframe structure or volume confirmation for context.
⚙️ Default Parameters
Fast Stochastic (12,3,3)
Slow Stochastic (100,8,8)
RSI Length (26)
Signal Line Smoothing (9)
All values can be adjusted to adapt to asset volatility or timeframe conditions.
⚠️ Disclaimer
This indicator is a research and visualization tool, not a signal generator.
It does not predict price movement or guarantee performance.
Use for analytical purposes only and combine with your own trading framework.
👨💻 Developer
Created by TitikSona — Research & Fusion Concept Designer
Built using Pine Script v6
Type: Open-source educational script
💬 Short Description
Fusion-based momentum visualization combining Double Stochastic and RSI into one adaptive line for clearer, noise-free momentum analysis.
Keltner Channel Enhanced [DCAUT]█ Keltner Channel Enhanced
📊 ORIGINALITY & INNOVATION
The Keltner Channel Enhanced represents an important advancement over standard Keltner Channel implementations by introducing dual flexibility in moving average selection for both the middle band and ATR calculation. While traditional Keltner Channels typically use EMA for the middle band and RMA (Wilder's smoothing) for ATR, this enhanced version provides access to 25+ moving average algorithms for both components, enabling traders to fine-tune the indicator's behavior to match specific market characteristics and trading approaches.
Key Advancements:
Dual MA Algorithm Flexibility: Independent selection of moving average types for middle band (25+ options) and ATR smoothing (25+ options), allowing optimization of both trend identification and volatility measurement separately
Enhanced Trend Sensitivity: Ability to use faster algorithms (HMA, T3) for middle band while maintaining stable volatility measurement with traditional ATR smoothing, or vice versa for different trading strategies
Adaptive Volatility Measurement: Choice of ATR smoothing algorithm affects channel responsiveness to volatility changes, from highly reactive (SMA, EMA) to smoothly adaptive (RMA, TEMA)
Comprehensive Alert System: Five distinct alert conditions covering breakouts, trend changes, and volatility expansion, enabling automated monitoring without constant chart observation
Multi-Timeframe Compatibility: Works effectively across all timeframes from intraday scalping to long-term position trading, with independent optimization of trend and volatility components
This implementation addresses key limitations of standard Keltner Channels: fixed EMA/RMA combination may not suit all market conditions or trading styles. By decoupling the trend component from volatility measurement and allowing independent algorithm selection, traders can create highly customized configurations for specific instruments and market phases.
📐 MATHEMATICAL FOUNDATION
Keltner Channel Enhanced uses a three-component calculation system that combines a flexible moving average middle band with ATR-based (Average True Range) upper and lower channels, creating volatility-adjusted trend-following bands.
Core Calculation Process:
1. Middle Band (Basis) Calculation:
The basis line is calculated using the selected moving average algorithm applied to the price source over the specified period:
basis = ma(source, length, maType)
Supported algorithms include EMA (standard choice, trend-biased), SMA (balanced and symmetric), HMA (reduced lag), WMA, VWMA, TEMA, T3, KAMA, and 17+ others.
2. Average True Range (ATR) Calculation:
ATR measures market volatility by calculating the average of true ranges over the specified period:
trueRange = max(high - low, abs(high - close ), abs(low - close ))
atrValue = ma(trueRange, atrLength, atrMaType)
ATR smoothing algorithm significantly affects channel behavior, with options including RMA (standard, very smooth), SMA (moderate smoothness), EMA (fast adaptation), TEMA (smooth yet responsive), and others.
3. Channel Calculation:
Upper and lower channels are positioned at specified multiples of ATR from the basis:
upperChannel = basis + (multiplier × atrValue)
lowerChannel = basis - (multiplier × atrValue)
Standard multiplier is 2.0, providing channels that dynamically adjust width based on market volatility.
Keltner Channel vs. Bollinger Bands - Key Differences:
While both indicators create volatility-based channels, they use fundamentally different volatility measures:
Keltner Channel (ATR-based):
Uses Average True Range to measure actual price movement volatility
Incorporates gaps and limit moves through true range calculation
More stable in trending markets, less prone to extreme compression
Better reflects intraday volatility and trading range
Typically fewer band touches, making touches more significant
More suitable for trend-following strategies
Bollinger Bands (Standard Deviation-based):
Uses statistical standard deviation to measure price dispersion
Based on closing prices only, doesn't account for intraday range
Can compress significantly during consolidation (squeeze patterns)
More touches in ranging markets
Better suited for mean-reversion strategies
Provides statistical probability framework (95% within 2 standard deviations)
Algorithm Combination Effects:
The interaction between middle band MA type and ATR MA type creates different indicator characteristics:
Trend-Focused Configuration (Fast MA + Slow ATR): Middle band uses HMA/EMA/T3, ATR uses RMA/TEMA, quick trend changes with stable channel width, suitable for trend-following
Volatility-Focused Configuration (Slow MA + Fast ATR): Middle band uses SMA/WMA, ATR uses EMA/SMA, stable trend with dynamic channel width, suitable for volatility trading
Balanced Configuration (Standard EMA/RMA): Classic Keltner Channel behavior, time-tested combination, suitable for general-purpose trend following
Adaptive Configuration (KAMA + KAMA): Self-adjusting indicator responding to efficiency ratio, suitable for markets with varying trend strength and volatility regimes
📊 COMPREHENSIVE SIGNAL ANALYSIS
Keltner Channel Enhanced provides multiple signal categories optimized for trend-following and breakout strategies.
Channel Position Signals:
Upper Channel Interaction:
Price Touching Upper Channel: Strong bullish momentum, price moving more than typical volatility range suggests, potential continuation signal in established uptrends
Price Breaking Above Upper Channel: Exceptional strength, price exceeding normal volatility expectations, consider adding to long positions or tightening trailing stops
Price Riding Upper Channel: Sustained strong uptrend, characteristic of powerful bull moves, stay with trend and avoid premature profit-taking
Price Rejection at Upper Channel: Momentum exhaustion signal, consider profit-taking on longs or waiting for pullback to middle band for reentry
Lower Channel Interaction:
Price Touching Lower Channel: Strong bearish momentum, price moving more than typical volatility range suggests, potential continuation signal in established downtrends
Price Breaking Below Lower Channel: Exceptional weakness, price exceeding normal volatility expectations, consider adding to short positions or protecting against further downside
Price Riding Lower Channel: Sustained strong downtrend, characteristic of powerful bear moves, stay with trend and avoid premature covering
Price Rejection at Lower Channel: Momentum exhaustion signal, consider covering shorts or waiting for bounce to middle band for reentry
Middle Band (Basis) Signals:
Trend Direction Confirmation:
Price Above Basis: Bullish trend bias, middle band acts as dynamic support in uptrends, consider long positions or holding existing longs
Price Below Basis: Bearish trend bias, middle band acts as dynamic resistance in downtrends, consider short positions or avoiding longs
Price Crossing Above Basis: Potential trend change from bearish to bullish, early signal to establish long positions
Price Crossing Below Basis: Potential trend change from bullish to bearish, early signal to establish short positions or exit longs
Pullback Trading Strategy:
Uptrend Pullback: Price pulls back from upper channel to middle band, finds support, and resumes upward, ideal long entry point
Downtrend Bounce: Price bounces from lower channel to middle band, meets resistance, and resumes downward, ideal short entry point
Basis Test: Strong trends often show price respecting the middle band as support/resistance on pullbacks
Failed Test: Price breaking through middle band against trend direction signals potential reversal
Volatility-Based Signals:
Narrow Channels (Low Volatility):
Consolidation Phase: Channels contract during periods of reduced volatility and directionless price action
Breakout Preparation: Narrow channels often precede significant directional moves as volatility cycles
Trading Approach: Reduce position sizes, wait for breakout confirmation, avoid range-bound strategies within channels
Breakout Direction: Monitor for price breaking decisively outside channel range with expanding width
Wide Channels (High Volatility):
Trending Phase: Channels expand during strong directional moves and increased volatility
Momentum Confirmation: Wide channels confirm genuine trend with substantial volatility backing
Trading Approach: Trend-following strategies excel, wider stops necessary, mean-reversion strategies risky
Exhaustion Signs: Extreme channel width (historical highs) may signal approaching consolidation or reversal
Advanced Pattern Recognition:
Channel Walking Pattern:
Upper Channel Walk: Price consistently touches or exceeds upper channel while staying above basis, very strong uptrend signal, hold longs aggressively
Lower Channel Walk: Price consistently touches or exceeds lower channel while staying below basis, very strong downtrend signal, hold shorts aggressively
Basis Support/Resistance: During channel walks, price typically uses middle band as support/resistance on minor pullbacks
Pattern Break: Price crossing basis during channel walk signals potential trend exhaustion
Squeeze and Release Pattern:
Squeeze Phase: Channels narrow significantly, price consolidates near middle band, volatility contracts
Direction Clues: Watch for price positioning relative to basis during squeeze (above = bullish bias, below = bearish bias)
Release Trigger: Price breaking outside narrow channel range with expanding width confirms breakout
Follow-Through: Measure squeeze height and project from breakout point for initial profit targets
Channel Expansion Pattern:
Breakout Confirmation: Rapid channel widening confirms volatility increase and genuine trend establishment
Entry Timing: Enter positions early in expansion phase before trend becomes overextended
Risk Management: Use channel width to size stops appropriately, wider channels require wider stops
Basis Bounce Pattern:
Clean Bounce: Price touches middle band and immediately reverses, confirms trend strength and entry opportunity
Multiple Bounces: Repeated basis bounces indicate strong, sustainable trend
Bounce Failure: Price penetrating basis signals weakening trend and potential reversal
Divergence Analysis:
Price/Channel Divergence: Price makes new high/low while staying within channel (not reaching outer band), suggests momentum weakening
Width/Price Divergence: Price breaks to new extremes but channel width contracts, suggests move lacks conviction
Reversal Signal: Divergences often precede trend reversals or significant consolidation periods
Multi-Timeframe Analysis:
Keltner Channels work particularly well in multi-timeframe trend-following approaches:
Three-Timeframe Alignment:
Higher Timeframe (Weekly/Daily): Identify major trend direction, note price position relative to basis and channels
Intermediate Timeframe (Daily/4H): Identify pullback opportunities within higher timeframe trend
Lower Timeframe (4H/1H): Time precise entries when price touches middle band or lower channel (in uptrends) with rejection
Optimal Entry Conditions:
Best Long Entries: Higher timeframe in uptrend (price above basis), intermediate timeframe pulls back to basis, lower timeframe shows rejection at middle band or lower channel
Best Short Entries: Higher timeframe in downtrend (price below basis), intermediate timeframe bounces to basis, lower timeframe shows rejection at middle band or upper channel
Risk Management: Use higher timeframe channel width to set position sizing, stops below/above higher timeframe channels
🎯 STRATEGIC APPLICATIONS
Keltner Channel Enhanced excels in trend-following and breakout strategies across different market conditions.
Trend Following Strategy:
Setup Requirements:
Identify established trend with price consistently on one side of basis line
Wait for pullback to middle band (basis) or brief penetration through it
Confirm trend resumption with price rejection at basis and move back toward outer channel
Enter in trend direction with stop beyond basis line
Entry Rules:
Uptrend Entry:
Price pulls back from upper channel to middle band, shows support at basis (bullish candlestick, momentum divergence)
Enter long on rejection/bounce from basis with stop 1-2 ATR below basis
Aggressive: Enter on first touch; Conservative: Wait for confirmation candle
Downtrend Entry:
Price bounces from lower channel to middle band, shows resistance at basis (bearish candlestick, momentum divergence)
Enter short on rejection/reversal from basis with stop 1-2 ATR above basis
Aggressive: Enter on first touch; Conservative: Wait for confirmation candle
Trend Management:
Trailing Stop: Use basis line as dynamic trailing stop, exit if price closes beyond basis against position
Profit Taking: Take partial profits at opposite channel, move stops to basis
Position Additions: Add to winners on subsequent basis bounces if trend intact
Breakout Strategy:
Setup Requirements:
Identify consolidation period with contracting channel width
Monitor price action near middle band with reduced volatility
Wait for decisive breakout beyond channel range with expanding width
Enter in breakout direction after confirmation
Breakout Confirmation:
Price breaks clearly outside channel (upper for longs, lower for shorts), channel width begins expanding from contracted state
Volume increases significantly on breakout (if using volume analysis)
Price sustains outside channel for multiple bars without immediate reversal
Entry Approaches:
Aggressive: Enter on initial break with stop at opposite channel or basis, use smaller position size
Conservative: Wait for pullback to broken channel level, enter on rejection and resumption, tighter stop
Volatility-Based Position Sizing:
Adjust position sizing based on channel width (ATR-based volatility):
Wide Channels (High ATR): Reduce position size as stops must be wider, calculate position size using ATR-based risk calculation: Risk / (Stop Distance in ATR × ATR Value)
Narrow Channels (Low ATR): Increase position size as stops can be tighter, be cautious of impending volatility expansion
ATR-Based Risk Management: Use ATR-based risk calculations, position size = 0.01 × Capital / (2 × ATR), use multiples of ATR (1-2 ATR) for adaptive stops
Algorithm Selection Guidelines:
Different market conditions benefit from different algorithm combinations:
Strong Trending Markets: Middle band use EMA or HMA, ATR use RMA, capture trends quickly while maintaining stable channel width
Choppy/Ranging Markets: Middle band use SMA or WMA, ATR use SMA or WMA, avoid false trend signals while identifying genuine reversals
Volatile Markets: Middle band and ATR both use KAMA or FRAMA, self-adjusting to changing market conditions reduces manual optimization
Breakout Trading: Middle band use SMA, ATR use EMA or SMA, stable trend with dynamic channels highlights volatility expansion early
Scalping/Day Trading: Middle band use HMA or T3, ATR use EMA or TEMA, both components respond quickly
Position Trading: Middle band use EMA/TEMA/T3, ATR use RMA or TEMA, filter out noise for long-term trend-following
📋 DETAILED PARAMETER CONFIGURATION
Understanding and optimizing parameters is essential for adapting Keltner Channel Enhanced to specific trading approaches.
Source Parameter:
Close (Most Common): Uses closing price, reflects daily settlement, best for end-of-day analysis and position trading, standard choice
HL2 (Median Price): Smooths out closing bias, better represents full daily range in volatile markets, good for swing trading
HLC3 (Typical Price): Gives more weight to close while including full range, popular for intraday applications, slightly more responsive than HL2
OHLC4 (Average Price): Most comprehensive price representation, smoothest option, good for gap-prone markets or highly volatile instruments
Length Parameter:
Controls the lookback period for middle band (basis) calculation:
Short Periods (10-15): Very responsive to price changes, suitable for day trading and scalping, higher false signal rate
Standard Period (20 - Default): Represents approximately one month of trading, good balance between responsiveness and stability, suitable for swing and position trading
Medium Periods (30-50): Smoother trend identification, fewer false signals, better for position trading and longer holding periods
Long Periods (50+): Very smooth, identifies major trends only, minimal false signals but significant lag, suitable for long-term investment
Optimization by Timeframe: 1-15 minute charts use 10-20 period, 30-60 minute charts use 20-30 period, 4-hour to daily charts use 20-40 period, weekly charts use 20-30 weeks.
ATR Length Parameter:
Controls the lookback period for Average True Range calculation, affecting channel width:
Short ATR Periods (5-10): Very responsive to recent volatility changes, standard is 10 (Keltner's original specification), may be too reactive in whipsaw conditions
Standard ATR Period (10 - Default): Chester Keltner's original specification, good balance between responsiveness and stability, most widely used
Medium ATR Periods (14-20): Smoother channel width, ATR 14 aligns with Wilder's original ATR specification, good for position trading
Long ATR Periods (20+): Very smooth channel width, suitable for long-term trend-following
Length vs. ATR Length Relationship: Equal values (20/20) provide balanced responsiveness, longer ATR (20/14) gives more stable channel width, shorter ATR (20/10) is standard configuration, much shorter ATR (20/5) creates very dynamic channels.
Multiplier Parameter:
Controls channel width by setting ATR multiples:
Lower Values (1.0-1.5): Tighter channels with frequent price touches, more trading signals, higher false signal rate, better for range-bound and mean-reversion strategies
Standard Value (2.0 - Default): Chester Keltner's recommended setting, good balance between signal frequency and reliability, suitable for both trending and ranging strategies
Higher Values (2.5-3.0): Wider channels with less frequent touches, fewer but potentially higher-quality signals, better for strong trending markets
Market-Specific Optimization: High volatility markets (crypto, small-caps) use 2.5-3.0 multiplier, medium volatility markets (major forex, large-caps) use 2.0 multiplier, low volatility markets (bonds, utilities) use 1.5-2.0 multiplier.
MA Type Parameter (Middle Band):
Critical selection that determines trend identification characteristics:
EMA (Exponential Moving Average - Default): Standard Keltner Channel choice, Chester Keltner's original specification, emphasizes recent prices, faster response to trend changes, suitable for all timeframes
SMA (Simple Moving Average): Equal weighting of all data points, no directional bias, slower than EMA, better for ranging markets and mean-reversion
HMA (Hull Moving Average): Minimal lag with smooth output, excellent for fast trend identification, best for day trading and scalping
TEMA (Triple Exponential Moving Average): Advanced smoothing with reduced lag, responsive to trends while filtering noise, suitable for volatile markets
T3 (Tillson T3): Very smooth with minimal lag, excellent for established trend identification, suitable for position trading
KAMA (Kaufman Adaptive Moving Average): Automatically adjusts speed based on market efficiency, slow in ranging markets, fast in trends, suitable for markets with varying conditions
ATR MA Type Parameter:
Determines how Average True Range is smoothed, affecting channel width stability:
RMA (Wilder's Smoothing - Default): J. Welles Wilder's original ATR smoothing method, very smooth, slow to adapt to volatility changes, provides stable channel width
SMA (Simple Moving Average): Equal weighting, moderate smoothness, faster response to volatility changes than RMA, more dynamic channel width
EMA (Exponential Moving Average): Emphasizes recent volatility, quick adaptation to new volatility regimes, very responsive channel width changes
TEMA (Triple Exponential Moving Average): Smooth yet responsive, good balance for varying volatility, suitable for most trading styles
Parameter Combination Strategies:
Conservative Trend-Following: Length 30/ATR Length 20/Multiplier 2.5, MA Type EMA or TEMA/ATR MA Type RMA, smooth trend with stable wide channels, suitable for position trading
Standard Balanced Approach: Length 20/ATR Length 10/Multiplier 2.0, MA Type EMA/ATR MA Type RMA, classic Keltner Channel configuration, suitable for general purpose swing trading
Aggressive Day Trading: Length 10-15/ATR Length 5-7/Multiplier 1.5-2.0, MA Type HMA or EMA/ATR MA Type EMA or SMA, fast trend with dynamic channels, suitable for scalping and day trading
Breakout Specialist: Length 20-30/ATR Length 5-10/Multiplier 2.0, MA Type SMA or WMA/ATR MA Type EMA or SMA, stable trend with responsive channel width
Adaptive All-Conditions: Length 20/ATR Length 10/Multiplier 2.0, MA Type KAMA or FRAMA/ATR MA Type KAMA or TEMA, self-adjusting to market conditions
Offset Parameter:
Controls horizontal positioning of channels on chart. Positive values shift channels to the right (future) for visual projection, negative values shift left (past) for historical analysis, zero (default) aligns with current price bars for real-time signal analysis. Offset affects only visual display, not alert conditions or actual calculations.
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Keltner Channel Enhanced provides improvements over standard implementations while maintaining proven effectiveness.
Response Characteristics:
Standard EMA/RMA Configuration: Moderate trend lag (approximately 0.4 × length periods), smooth and stable channel width from RMA smoothing, good balance for most market conditions
Fast HMA/EMA Configuration: Approximately 60% reduction in trend lag compared to EMA, responsive channel width from EMA ATR smoothing, suitable for quick trend changes and breakouts
Adaptive KAMA/KAMA Configuration: Variable lag based on market efficiency, automatic adjustment to trending vs. ranging conditions, self-optimizing behavior reduces manual intervention
Comparison with Traditional Keltner Channels:
Enhanced Version Advantages:
Dual Algorithm Flexibility: Independent MA selection for trend and volatility vs. fixed EMA/RMA, separate tuning of trend responsiveness and channel stability
Market Adaptation: Choose configurations optimized for specific instruments and conditions, customize for scalping, swing, or position trading preferences
Comprehensive Alerts: Enhanced alert system including channel expansion detection
Traditional Version Advantages:
Simplicity: Fewer parameters, easier to understand and implement
Standardization: Fixed EMA/RMA combination ensures consistency across users
Research Base: Decades of backtesting and research on standard configuration
When to Use Enhanced Version: Trading multiple instruments with different characteristics, switching between trending and ranging markets, employing different strategies, algorithm-based trading systems requiring customization, seeking optimization for specific trading style and timeframe.
When to Use Standard Version: Beginning traders learning Keltner Channel concepts, following published research or trading systems, preferring simplicity and standardization, wanting to avoid optimization and curve-fitting risks.
Performance Across Market Conditions:
Strong Trending Markets: EMA or HMA basis with RMA or TEMA ATR smoothing provides quicker trend identification, pullbacks to basis offer excellent entry opportunities
Choppy/Ranging Markets: SMA or WMA basis with RMA ATR smoothing and lower multipliers, channel bounce strategies work well, avoid false breakouts
Volatile Markets: KAMA or FRAMA with EMA or TEMA, adaptive algorithms excel by automatic adjustment, wider multipliers (2.5-3.0) accommodate large price swings
Low Volatility/Consolidation: Channels narrow significantly indicating consolidation, algorithm choice less impactful, focus on detecting channel width contraction for breakout preparation
Keltner Channel vs. Bollinger Bands - Usage Comparison:
Favor Keltner Channels When: Trend-following is primary strategy, trading volatile instruments with gaps, want ATR-based volatility measurement, prefer fewer higher-quality channel touches, seeking stable channel width during trends.
Favor Bollinger Bands When: Mean-reversion is primary strategy, trading instruments with limited gaps, want statistical framework based on standard deviation, need squeeze patterns for breakout identification, prefer more frequent trading opportunities.
Use Both Together: Bollinger Band squeeze + Keltner Channel breakout is powerful combination, price outside Bollinger Bands but inside Keltner Channels indicates moderate signal, price outside both indicates very strong signal, Bollinger Bands for entries and Keltner Channels for trend confirmation.
Limitations and Considerations:
General Limitations:
Lagging Indicator: All moving averages lag price, even with reduced-lag algorithms
Trend-Dependent: Works best in trending markets, less effective in choppy conditions
No Direction Prediction: Indicates volatility and deviation, not future direction, requires confirmation
Enhanced Version Specific Considerations:
Optimization Risk: More parameters increase risk of curve-fitting historical data
Complexity: Additional choices may overwhelm beginning traders
Backtesting Challenges: Different algorithms produce different historical results
Mitigation Strategies:
Use Confirmation: Combine with momentum indicators (RSI, MACD), volume, or price action
Test Parameter Robustness: Ensure parameters work across range of values, not just optimized ones
Multi-Timeframe Analysis: Confirm signals across different timeframes
Proper Risk Management: Use appropriate position sizing and stops
Start Simple: Begin with standard EMA/RMA before exploring alternatives
Optimal Usage Recommendations:
For Maximum Effectiveness:
Start with standard EMA/RMA configuration to understand classic behavior
Experiment with alternatives on demo account or paper trading
Match algorithm combination to market condition and trading style
Use channel width analysis to identify market phases
Combine with complementary indicators for confirmation
Implement strict risk management using ATR-based position sizing
Focus on high-quality setups rather than trading every signal
Respect the trend: trade with basis direction for higher probability
Complementary Indicators:
RSI or Stochastic: Confirm momentum at channel extremes
MACD: Confirm trend direction and momentum shifts
Volume: Validate breakouts and trend strength
ADX: Measure trend strength, avoid Keltner signals in weak trends
Support/Resistance: Combine with traditional levels for high-probability setups
Bollinger Bands: Use together for enhanced breakout and volatility analysis
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. Keltner Channel Enhanced has limitations and should not be used as the sole basis for trading decisions. While the flexible moving average selection for both trend and volatility components provides valuable adaptability across different market conditions, algorithm performance varies with market conditions, and past characteristics do not guarantee future results.
Key considerations:
Always use multiple forms of analysis and confirmation before entering trades
Backtest any parameter combination thoroughly before live trading
Be aware that optimization can lead to curve-fitting if not done carefully
Start with standard EMA/RMA settings and adjust only when specific conditions warrant
Understand that no moving average algorithm can eliminate lag entirely
Consider market regime (trending, ranging, volatile) when selecting parameters
Use ATR-based position sizing and risk management on every trade
Keltner Channels work best in trending markets, less effective in choppy conditions
Respect the trend direction indicated by price position relative to basis line
The enhanced flexibility of dual algorithm selection provides powerful tools for adaptation but requires responsible use, thorough understanding of how different algorithms behave under various market conditions, and disciplined risk management.
Auto-Fit Growth Trendline# **Theoretical Algorithmic Principles of the Auto-Fit Growth Trendline (AFGT)**
## **🎯 What Does This Algorithm Do?**
The Auto-Fit Growth Trendline is an advanced technical analysis system that **automates the identification of long-term growth trends** and **projects future price levels** based on historical cyclical patterns.
### **Primary Functionality:**
- **Automatically detects** the most significant lows in regular periods (monthly, quarterly, semi-annually, annually)
- **Constructs a dynamic trendline** that connects these historical lows
- **Projects the trend into the future** with high mathematical precision
- **Generates Fibonacci bands** that act as dynamic support and resistance levels
- **Automatically adapts** to different timeframes and market conditions
### **Strategic Purpose:**
The algorithm is designed to identify **fundamental value zones** where price has historically found support, enabling traders to:
- Identify optimal entry points for long positions
- Establish realistic price targets based on mathematical projections
- Recognize dynamic support and resistance levels
- Anticipate long-term price movements
---
## **🧮 Core Mathematical Foundations**
### **Adaptive Temporal Segmentation Theory**
The algorithm is based on **dynamic temporal partition theory**, where time is divided into mathematically coherent uniform intervals. It uses modular transformations to create bijective mappings between continuous timestamps and discrete periods, ensuring each temporal point belongs uniquely to a specific period.
**What does this achieve?** It allows the algorithm to automatically identify natural market cycles (annual, quarterly, etc.) without manual intervention, adapting to the inherent periodicity of each asset.
The temporal mapping function implements a **discrete affine transformation** that normalizes different frequencies (monthly, quarterly, semi-annual, annual) to a space of unique identifiers, enabling consistent cross-temporal comparative analysis.
---
## **📊 Local Extrema Detection Theory**
### **Multi-Point Retrospective Validation Principle**
Local minima detection is founded on **relative extrema theory with sliding window**. Instead of using a simple minimum finder, it implements a cross-validation system that examines the persistence of the extremum across multiple historical periods.
**What problem does this solve?** It eliminates false minima caused by temporal volatility, identifying only those points that represent true historical support levels with statistical significance.
This approach is based on the **statistical confirmation principle**, where a minimum is only considered valid if it maintains its extremum condition during a defined observation period, significantly reducing false positives caused by transitory volatility.
---
## **🔬 Robust Interpolation Theory with Outlier Control**
### **Contextual Adaptive Interpolation Model**
The mathematical core uses **piecewise linear interpolation with adaptive outlier correction**. The key innovation lies in implementing a **contextual anomaly detector** that identifies not only absolute extreme values, but relative deviations to the local context.
**Why is this important?** Financial markets contain extreme events (crashes, bubbles) that can distort projections. This system identifies and appropriately weights them without completely eliminating them, preserving directional information while attenuating distortions.
### **Implicit Bayesian Smoothing Algorithm**
When an outlier is detected (deviation >300% of local average), the system applies a **simplified Kalman filter** that combines the current observation with a local trend estimation, using a weight factor that preserves directional information while attenuating extreme fluctuations.
---
## **📈 Stabilized Extrapolation Theory**
### **Exponential Growth Model with Dampening**
Extrapolation is based on a **modified exponential growth model with progressive dampening**. It uses multiple historical points to calculate local growth ratios, implements statistical filtering to eliminate outliers, and applies a dampening factor that increases with extrapolation distance.
**What advantage does this offer?** Long-term projections in finance tend to be exponentially unrealistic. This system maintains short-to-medium term accuracy while converging toward realistic long-term projections, avoiding the typical "exponential explosions" of other methods.
### **Asymptotic Convergence Principle**
For long-term projections, the algorithm implements **controlled asymptotic convergence**, where growth ratios gradually converge toward pre-established limits, avoiding unrealistic exponential projections while preserving short-to-medium term accuracy.
---
## **🌟 Dynamic Fibonacci Projection Theory**
### **Continuous Proportional Scaling Model**
Fibonacci bands are constructed through **uniform proportional scaling** of the base curve, where each level represents a linear transformation of the main curve by a constant factor derived from the Fibonacci sequence.
**What is its practical utility?** It provides dynamic resistance and support levels that move with the trend, offering price targets and profit-taking points that automatically adapt to market evolution.
### **Topological Preservation Principle**
The system maintains the **topological properties** of the base curve in all Fibonacci projections, ensuring that spatial and temporal relationships are consistently preserved across all resistance/support levels.
---
## **⚡ Adaptive Computational Optimization**
### **Multi-Scale Resolution Theory**
It implements **automatic multi-resolution analysis** where data granularity is dynamically adjusted according to the analysis timeframe. It uses the **adaptive Nyquist principle** to optimize the signal-to-noise ratio according to the temporal observation scale.
**Why is this necessary?** Different timeframes require different levels of detail. A 1-minute chart needs more granularity than a monthly one. This system automatically optimizes resolution for each case.
### **Adaptive Density Algorithm**
Calculation point density is optimized through **adaptive sampling theory**, where calculation frequency is adjusted according to local trend curvature and analysis timeframe, balancing visual precision with computational efficiency.
---
## **🛡️ Robustness and Fault Tolerance**
### **Graceful Degradation Theory**
The system implements **multi-level graceful degradation**, where under error conditions or insufficient data, the algorithm progressively falls back to simpler but reliable methods, maintaining basic functionality under any condition.
**What does this guarantee?** That the indicator functions consistently even with incomplete data, new symbols with limited history, or extreme market conditions.
### **State Consistency Principle**
It uses **mathematical invariants** to guarantee that the algorithm's internal state remains consistent between executions, implementing consistency checks that validate data structure integrity in each iteration.
---
## **🔍 Key Theoretical Innovations**
### **A. Contextual vs. Absolute Outlier Detection**
It revolutionizes traditional outlier detection by considering not only the absolute magnitude of deviations, but their relative significance within the local context of the time series.
**Practical impact:** It distinguishes between legitimate market movements and technical anomalies, preserving important events like breakouts while filtering noise.
### **B. Extrapolation with Weighted Historical Memory**
It implements a memory system that weights different historical periods according to their relevance for current prediction, creating projections more adaptable to market regime changes.
**Competitive advantage:** It automatically adapts to fundamental changes in asset dynamics without requiring manual recalibration.
### **C. Automatic Multi-Timeframe Adaptation**
It develops an automatic temporal resolution selection system that optimizes signal extraction according to the intrinsic characteristics of the analysis timeframe.
**Result:** A single indicator that functions optimally from 1-minute to monthly charts without manual adjustments.
### **D. Intelligent Asymptotic Convergence**
It introduces the concept of controlled asymptotic convergence in financial extrapolations, where long-term projections converge toward realistic limits based on historical fundamentals.
**Added value:** Mathematically sound long-term projections that avoid the unrealistic extremes typical of other extrapolation methods.
---
## **📊 Complexity and Scalability Theory**
### **Optimized Linear Complexity Model**
The algorithm maintains **linear computational complexity** O(n) in the number of historical data points, guaranteeing scalability for extensive time series analysis without performance degradation.
### **Temporal Locality Principle**
It implements **temporal locality**, where the most expensive operations are concentrated in the most relevant temporal regions (recent periods and near projections), optimizing computational resource usage.
---
## **🎯 Convergence and Stability**
### **Probabilistic Convergence Theory**
The system guarantees **probabilistic convergence** toward the real underlying trend, where projection accuracy increases with the amount of available historical data, following **law of large numbers** principles.
**Practical implication:** The more history an asset has, the more accurate the algorithm's projections will be.
### **Guaranteed Numerical Stability**
It implements **intrinsic numerical stability** through the use of robust floating-point arithmetic and validations that prevent overflow, underflow, and numerical error propagation.
**Result:** Reliable operation even with extreme-priced assets (from satoshis to thousand-dollar stocks).
---
## **💼 Comprehensive Practical Application**
**The algorithm functions as a "financial GPS"** that:
1. **Identifies where we've been** (significant historical lows)
2. **Determines where we are** (current position relative to the trend)
3. **Projects where we're going** (future trend with specific price levels)
4. **Provides alternative routes** (Fibonacci bands as alternative targets)
This theoretical framework represents an innovative synthesis of time series analysis, approximation theory, and computational optimization, specifically designed for long-term financial trend analysis with robust and mathematically grounded projections.
Bitcoin Polynomial Regression ModelThis is the main version of the script. Click here for the Oscillator part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines. The Oscillator version can be found here.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
Bitcoin Polynomial Regression OscillatorThis is the oscillator version of the script. Click here for the other part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
DeepSignalFilterHelpersLibrary "DeepSignalFilterHelpers"
filter_intraday_intensity(useIiiFilter)
Parameters:
useIiiFilter (bool)
filter_vwma(src, length, useVwmaFilter)
Parameters:
src (float)
length (int)
useVwmaFilter (bool)
filter_nvi(useNviFilter)
Parameters:
useNviFilter (bool)
filter_emv(length, emvThreshold, useEmvFilter, useMovingAvg)
EMV filter for filtering signals based on Ease of Movement
Parameters:
length (int) : The length of the EMV calculation
emvThreshold (float) : The EMV threshold
useEmvFilter (bool) : Whether to apply the EMV filter
useMovingAvg (bool) : Whether to use moving average as threshold
Returns: Filtered result indicating whether the signal should be used
filter_adi(length, threshold, useAdiFilter, useMovingAvg)
ADI filter for filtering signals based on Accumulation/Distribution Index
Parameters:
length (int) : The length of the ADI moving average calculation
threshold (float) : The ADI threshold
useAdiFilter (bool) : Whether to apply the ADI filter
useMovingAvg (bool) : Whether to use moving average as threshold
Returns: Filtered result indicating whether the signal should be used
filter_mfi(length, mfiThreshold, useMfiFilter, useMovingAvg)
MFI filter for filtering signals based on Money Flow Index
Parameters:
length (int) : The length of the MFI calculation
mfiThreshold (float) : The MFI threshold
useMfiFilter (bool) : Whether to apply the MFI filter
useMovingAvg (bool) : Whether to use moving average as threshold
Returns: Filtered result indicating whether the signal should be used
detect_obv_states(obvThresholdStrong, obvThresholdModerate, lookbackPeriod, obvMode)
detect_obv_states: Identify OBV states with three levels (Strong, Moderate, Weak) over a configurable period
Parameters:
obvThresholdStrong (float) : Threshold for strong OBV movements
obvThresholdModerate (float) : Threshold for moderate OBV movements
lookbackPeriod (int) : Number of periods to analyze OBV trends
obvMode (string) : OBV mode to filter ("Strong", "Moderate", "Weak")
Returns: OBV state ("Strong Up", "Moderate Up", "Weak Up", "Positive Divergence", "Negative Divergence", "Consolidation", "Weak Down", "Moderate Down", "Strong Down")
filter_obv(src, length, obvMode, threshold, useObvFilter, useMovingAvg)
filter_obv: Filter signals based on OBV states
Parameters:
src (float) : The source series (default: close)
length (int) : The length of the OBV moving average calculation
obvMode (string) : OBV mode to filter ("Strong", "Moderate", "Weak")
threshold (float) : Optional threshold for additional filtering
useObvFilter (bool) : Whether to apply the OBV filter
useMovingAvg (bool) : Whether to use moving average as threshold
Returns: Filtered result indicating whether the signal should be used
filter_cmf(length, cmfThreshold, useCmfFilter, useMovingAvg)
CMF filter for filtering signals based on Chaikin Money Flow
Parameters:
length (int) : The length of the CMF calculation
cmfThreshold (float) : The CMF threshold
useCmfFilter (bool) : Whether to apply the CMF filter
useMovingAvg (bool) : Whether to use moving average as threshold
Returns: Filtered result indicating whether the signal should be used
filter_vwap(useVwapFilter)
VWAP filter for filtering signals based on Volume-Weighted Average Price
Parameters:
useVwapFilter (bool) : Whether to apply the VWAP filter
Returns: Filtered result indicating whether the signal should be used
filter_pvt(length, pvtThreshold, usePvtFilter, useMovingAvg)
PVT filter for filtering signals based on Price Volume Trend
Parameters:
length (int) : The length of the PVT moving average calculation
pvtThreshold (float) : The PVT threshold
usePvtFilter (bool) : Whether to apply the PVT filter
useMovingAvg (bool) : Whether to use moving average as threshold
Returns: Filtered result indicating whether the signal should be used
filter_vo(shortLength, longLength, voThreshold, useVoFilter, useMovingAvg)
VO filter for filtering signals based on Volume Oscillator
Parameters:
shortLength (int) : The length of the short-term volume moving average
longLength (int) : The length of the long-term volume moving average
voThreshold (float) : The Volume Oscillator threshold
useVoFilter (bool) : Whether to apply the VO filter
useMovingAvg (bool) : Whether to use moving average as threshold
Returns: Filtered result indicating whether the signal should be used
filter_cho(shortLength, longLength, choThreshold, useChoFilter, useMovingAvg)
CHO filter for filtering signals based on Chaikin Oscillator
Parameters:
shortLength (int) : The length of the short-term ADI moving average
longLength (int) : The length of the long-term ADI moving average
choThreshold (float) : The Chaikin Oscillator threshold
useChoFilter (bool) : Whether to apply the CHO filter
useMovingAvg (bool) : Whether to use moving average as threshold
Returns: Filtered result indicating whether the signal should be used
filter_fi(length, fiThreshold, useFiFilter, useMovingAvg)
FI filter for filtering signals based on Force Index
Parameters:
length (int) : The length of the FI calculation
fiThreshold (float) : The Force Index threshold
useFiFilter (bool) : Whether to apply the FI filter
useMovingAvg (bool) : Whether to use moving average as threshold
Returns: Filtered result indicating whether the signal should be used
filter_garman_klass_volatility(length, useGkFilter)
Parameters:
length (int)
useGkFilter (bool)
filter_frama(src, length, useFramaFilter)
Parameters:
src (float)
length (int)
useFramaFilter (bool)
filter_bollinger_bands(src, length, stdDev, useBollingerFilter)
Parameters:
src (float)
length (int)
stdDev (float)
useBollingerFilter (bool)
filter_keltner_channel(src, length, atrMult, useKeltnerFilter)
Parameters:
src (float)
length (simple int)
atrMult (float)
useKeltnerFilter (bool)
regime_filter(src, threshold, useRegimeFilter)
Regime filter for filtering signals based on trend strength
Parameters:
src (float) : The source series
threshold (float) : The threshold for the filter
useRegimeFilter (bool) : Whether to apply the regime filter
Returns: Filtered result indicating whether the signal should be used
regime_filter_v2(src, threshold, useRegimeFilter)
Regime filter for filtering signals based on trend strength
Parameters:
src (float) : The source series
threshold (float) : The threshold for the filter
useRegimeFilter (bool) : Whether to apply the regime filter
Returns: Filtered result indicating whether the signal should be used
filter_adx(src, length, adxThreshold, useAdxFilter)
ADX filter for filtering signals based on ADX strength
Parameters:
src (float) : The source series
length (simple int) : The length of the ADX calculation
adxThreshold (int) : The ADX threshold
useAdxFilter (bool) : Whether to apply the ADX filter
Returns: Filtered result indicating whether the signal should be used
filter_volatility(minLength, maxLength, useVolatilityFilter)
Volatility filter for filtering signals based on volatility
Parameters:
minLength (simple int) : The minimum length for ATR calculation
maxLength (simple int) : The maximum length for ATR calculation
useVolatilityFilter (bool) : Whether to apply the volatility filter
Returns: Filtered result indicating whether the signal should be used
filter_ulcer(src, length, ulcerThreshold, useUlcerFilter)
Ulcer Index filter for filtering signals based on Ulcer Index
Parameters:
src (float) : The source series
length (int) : The length of the Ulcer Index calculation
ulcerThreshold (float) : The Ulcer Index threshold (default: average Ulcer Index)
useUlcerFilter (bool) : Whether to apply the Ulcer Index filter
Returns: Filtered result indicating whether the signal should be used
filter_stddev(src, length, stdDevThreshold, useStdDevFilter)
Standard Deviation filter for filtering signals based on Standard Deviation
Parameters:
src (float) : The source series
length (int) : The length of the Standard Deviation calculation
stdDevThreshold (float) : The Standard Deviation threshold (default: average Standard Deviation)
useStdDevFilter (bool) : Whether to apply the Standard Deviation filter
Returns: Filtered result indicating whether the signal should be used
filter_macdv(src, shortLength, longLength, signalSmoothing, macdVThreshold, useMacdVFilter)
MACD-V filter for filtering signals based on MACD-V
Parameters:
src (float) : The source series
shortLength (simple int) : The short length for MACD calculation
longLength (simple int) : The long length for MACD calculation
signalSmoothing (simple int) : The signal smoothing length for MACD
macdVThreshold (float) : The MACD-V threshold (default: average MACD-V)
useMacdVFilter (bool) : Whether to apply the MACD-V filter
Returns: Filtered result indicating whether the signal should be used
filter_atr(length, atrThreshold, useAtrFilter)
ATR filter for filtering signals based on Average True Range (ATR)
Parameters:
length (simple int) : The length of the ATR calculation
atrThreshold (float) : The ATR threshold (default: average ATR)
useAtrFilter (bool) : Whether to apply the ATR filter
Returns: Filtered result indicating whether the signal should be used
filter_candle_body_and_atr(length, bodyThreshold, atrThreshold, useFilter)
Candle Body and ATR filter for filtering signals
Parameters:
length (simple int) : The length of the ATR calculation
bodyThreshold (float) : The threshold for candle body size (relative to ATR)
atrThreshold (float) : The ATR threshold (default: average ATR)
useFilter (bool) : Whether to apply the candle body and ATR filter
Returns: Filtered result indicating whether the signal should be used
filter_atrp(length, atrpThreshold, useAtrpFilter)
ATRP filter for filtering signals based on ATR Percentage (ATRP)
Parameters:
length (simple int) : The length of the ATR calculation
atrpThreshold (float) : The ATRP threshold (default: average ATRP)
useAtrpFilter (bool) : Whether to apply the ATRP filter
Returns: Filtered result indicating whether the signal should be used
filter_jma(src, length, phase, useJmaFilter)
Parameters:
src (float)
length (simple int)
phase (float)
useJmaFilter (bool)
filter_cidi(src, rsiLength, shortMaLength, longMaLength, useCidiFilter)
Parameters:
src (float)
rsiLength (simple int)
shortMaLength (int)
longMaLength (int)
useCidiFilter (bool)
filter_rsi(src, length, rsiThreshold, useRsiFilter)
Parameters:
src (float)
length (simple int)
rsiThreshold (float)
useRsiFilter (bool)
filter_ichimoku_oscillator(length, threshold, useFilter)
Ichimoku Oscillator filter for filtering signals based on Ichimoku Oscillator
Parameters:
length (int) : The length of the Ichimoku Oscillator calculation
threshold (float) : The threshold for the filter (default: average Ichimoku Oscillator)
useFilter (bool) : Whether to apply the filter
Returns: Filtered result indicating whether the signal should be used
filter_cmb_composite_index(src, shortLength, longLength, threshold, useFilter)
CMB Composite Index filter for filtering signals based on CMB Composite Index
Parameters:
src (float) : The source series
shortLength (simple int) : The short length for CMB calculation
longLength (simple int) : The long length for CMB calculation
threshold (float) : The threshold for the filter (default: average CMB Composite Index)
useFilter (bool) : Whether to apply the filter
Returns: Filtered result indicating whether the signal should be used
filter_connors_rsi(src, rsiLength, rocLength, streakLength, threshold, useFilter)
Connors RSI filter for filtering signals based on Connors RSI
Parameters:
src (float) : The source series
rsiLength (simple int) : The length for RSI calculation
rocLength (int) : The length for ROC calculation
streakLength (simple int) : The length for streak calculation
threshold (float) : The threshold for the filter (default: average Connors RSI)
useFilter (bool) : Whether to apply the filter
Returns: Filtered result indicating whether the signal should be used
filter_coppock_curve(src, roc1Length, roc2Length, wmaLength, threshold, useFilter)
Coppock Curve filter for filtering signals based on Coppock Curve
Parameters:
src (float) : The source series
roc1Length (int) : The length for the first ROC calculation
roc2Length (int) : The length for the second ROC calculation
wmaLength (int) : The length for the WMA calculation
threshold (float) : The threshold for the filter (default: average Coppock Curve)
useFilter (bool) : Whether to apply the filter
Returns: Filtered result indicating whether the signal should be used
filter_pmo(src, pmoLength, smoothingLength, threshold, useFilter)
DecisionPoint Price Momentum Oscillator filter for filtering signals based on PMO
Parameters:
src (float) : The source series
pmoLength (simple int) : The length for PMO calculation
smoothingLength (simple int) : The smoothing length for PMO
threshold (float) : The threshold for the filter (default: average PMO Oscillator)
useFilter (bool) : Whether to apply the filter
Returns: Filtered result indicating whether the signal should be used
filter_macd(src, shortLength, longLength, signalSmoothing, threshold, useFilter)
MACD filter for filtering signals based on MACD
Parameters:
src (float) : The source series
shortLength (simple int) : The short length for MACD calculation
longLength (simple int) : The long length for MACD calculation
signalSmoothing (simple int) : The signal smoothing length for MACD
threshold (float) : The threshold for the filter (default: average MACD)
useFilter (bool) : Whether to apply the filter
Returns: Filtered result indicating whether the signal should be used
filter_macd_histogram(src, shortLength, longLength, signalSmoothing, threshold, useFilter)
MACD-Histogram filter for filtering signals based on MACD-Histogram
Parameters:
src (float) : The source series
shortLength (simple int) : The short length for MACD calculation
longLength (simple int) : The long length for MACD calculation
signalSmoothing (simple int) : The signal smoothing length for MACD
threshold (float) : The threshold for the filter (default: average MACD-Histogram)
useFilter (bool) : Whether to apply the filter
Returns: Filtered result indicating whether the signal should be used
filter_kst(src, r1, r2, r3, r4, sm1, sm2, sm3, sm4, signalLength, threshold, useFilter)
Pring's Know Sure Thing filter for filtering signals based on KST
Parameters:
src (float) : The source series
r1 (int) : The first ROC length
r2 (int) : The second ROC length
r3 (int) : The third ROC length
r4 (int) : The fourth ROC length
sm1 (int) : The first smoothing length
sm2 (int) : The second smoothing length
sm3 (int) : The third smoothing length
sm4 (int) : The fourth smoothing length
signalLength (int) : The signal line smoothing length
threshold (float) : The threshold for the filter (default: average KST Oscillator)
useFilter (bool) : Whether to apply the filter
Returns: Filtered result indicating whether the signal should be used
filter_special_k(src, r1, r2, r3, r4, sm1, sm2, sm3, sm4, threshold, useFilter)
Pring's Special K filter for filtering signals based on Special K
Parameters:
src (float) : The source series
r1 (int) : The first ROC length
r2 (int) : The second ROC length
r3 (int) : The third ROC length
r4 (int) : The fourth ROC length
sm1 (int) : The first smoothing length
sm2 (int) : The second smoothing length
sm3 (int) : The third smoothing length
sm4 (int) : The fourth smoothing length
threshold (float) : The threshold for the filter (default: average Special K)
useFilter (bool) : Whether to apply the filter
Returns: Filtered result indicating whether the signal should be used
filter_roc_momentum(src, rocLength, momentumLength, threshold, useFilter)
ROC and Momentum filter for filtering signals based on ROC and Momentum
Parameters:
src (float) : The source series
rocLength (int) : The length for ROC calculation
momentumLength (int) : The length for Momentum calculation
threshold (float) : The threshold for the filter (default: average ROC and Momentum)
useFilter (bool) : Whether to apply the filter
Returns: Filtered result indicating whether the signal should be used
filter_rrg_relative_strength(src, length, threshold, useFilter)
RRG Relative Strength filter for filtering signals based on RRG Relative Strength
Parameters:
src (float) : The source series
length (int) : The length for RRG Relative Strength calculation
threshold (float) : The threshold for the filter (default: average RRG Relative Strength)
useFilter (bool) : Whether to apply the filter
Returns: Filtered result indicating whether the signal should be used
filter_alligator(useFilter)
Parameters:
useFilter (bool)
filter_wyckoff(useFilter)
Parameters:
useFilter (bool)
filter_squeeze_momentum(bbLength, bbStdDev, kcLength, kcMult, useFilter)
Parameters:
bbLength (int)
bbStdDev (float)
kcLength (simple int)
kcMult (float)
useFilter (bool)
filter_atr_compression(length, atrThreshold, useFilter)
Parameters:
length (simple int)
atrThreshold (float)
useFilter (bool)
filter_low_volume(length, useFilter)
Parameters:
length (int)
useFilter (bool)
filter_nvi_accumulation(useFilter)
Parameters:
useFilter (bool)
filter_ma_slope(src, length, slopeThreshold, useFilter)
Parameters:
src (float)
length (int)
slopeThreshold (float)
useFilter (bool)
filter_adx_low(len, lensig, adxThreshold, useFilter)
Parameters:
len (simple int)
lensig (simple int)
adxThreshold (int)
useFilter (bool)
filter_choppiness_index(length, chopThreshold, useFilter)
Parameters:
length (int)
chopThreshold (float)
useFilter (bool)
filter_range_detection(length, useFilter)
Parameters:
length (int)
useFilter (bool)
DRKMetricsLibrary "DRKMetrics"
TODO: add library description here
curve(disp_ind)
Call function to get a certain curve of your strategy.
Parameters:
disp_ind (string)
Returns: Returns type of curve plot.
cleaner(disp_ind, plot)
Call function to filter out your Strategy plots
Parameters:
disp_ind (string)
plot (float)
cobraTable(option, position)
Assign this function to a random variable to get the "Performance Table"
Parameters:
option (simple string)
position (simple string)
Enhanced Economic Composite with Dynamic WeightEnhanced Economic Composite with Dynamic Weight
Overview of the Indicator :
The "Enhanced Economic Composite with Dynamic Weight" is a comprehensive tool that combines multiple economic indicators, technical signals, and dynamic weighting to provide insights into market and economic health. It adjusts based on current volatility and recession risk, offering a detailed view of market conditions.
What This Indicator Does :
Tracks Economic Health: Uses key economic and market indicators to assess overall market conditions.
Dynamic Weighting: Adjusts the importance of components like stock indices, gold, and bonds based on volatility (VIX) and yield curve inversion.
Technical Signals: Identifies market momentum shifts through key crossovers like the Golden Cross, Death Cross, Silver Cross, and Hospice Cross.
Recession Shading: Marks known recessions for historical context.
Economic Factors Considered :
TIP (Treasury Inflation-Protected Securities): Reflects inflation expectations.
Gold: A safe-haven asset, increases in weight during volatility or rising momentum.
US Dollar Index (DXY): Measures USD strength, fixed weight of 10%, smoothed with EMA.
Commodities (DBC): Indicates global demand; weight increases with momentum or volatility.
Volatility Index (VIX): Reflects market risk, inversely related to market confidence.
Stock Indices (S&P 500, DJIA, NASDAQ, Russell 2000): Represent market performance, with weights reduced during high volatility or negative yield spread.
Yield Spread (10Y - 2Y Treasuries): Predicts recessions; negative spread reduces stock weighting.
Credit Spread (HYG - TLT): Indicates market risk through corporate vs. government bond yields.
How and Why Factors are Weighted:
Stock Indices get more weight in stable markets (low VIX, positive yield spread), while safe-haven assets like gold and bonds gain weight in volatile markets or during yield curve inversions. This dynamic adjustment ensures the composite reflects current market sentiment.
Technical Signals:
Golden Cross: 50 EMA crossing above 200 SMA, signaling bullish momentum.
Death Cross: 50 EMA below 200 SMA, indicating bearish momentum.
Silver Cross: 21 EMA crossing above 50 EMA, plotted only if below the 200-day SMA, signaling potential upside in downtrend conditions.
Hospice Cross: 50 EMA crosses below 21 EMA, plotted only if 21 EMA is below 200 SMA, a leading bearish signal.
Recession Shading:
Recession periods like the Great Recession, Early 2000s Recession, and COVID-19 Recession are shaded to provide historical context.
Benefits of Using This Indicator:
Comprehensive Analysis: Combines economic fundamentals and technical analysis for a full market view.
Dynamic Risk Adjustment: Weights shift between growth and safe-haven assets based on volatility and recession risk.
Early Signals: The Silver Cross and Hospice Cross provide early warnings of potential market shifts.
Recession Forecasting: Helps predict downturns through the yield curve and recession indicators.
Who Can Benefit:
Traders: Identify market momentum shifts early through crossovers.
Long-term Investors: Use recession warnings and dynamic adjustments to protect portfolios.
Analysts: A holistic tool for analyzing both economic trends and market movements.
This indicator helps users navigate varying market conditions by dynamically adjusting based on economic factors and providing early technical signals for market momentum shifts.
25-Day Momentum IndexDescription:
The 25-Day Momentum Index (25D MI) is a technical indicator designed to measure the strength and direction of price movements over a 25-day period. Inspired by classic momentum analysis, this indicator helps traders identify trends and potential reversal points in the market.
How It Works:
Momentum Calculation: The 25D MI calculates momentum as the difference between the current closing price and the closing price 25 days ago. This difference provides insights into the market's recent strength or weakness.
Plotting: The indicator plots the Momentum Index as a blue line, showing the raw momentum values. A zero line is also plotted in gray to serve as a reference point for positive and negative momentum.
Highlighting Zones:
Positive Momentum: When the Momentum Index is above zero, it is plotted in green, highlighting positive momentum phases.
Negative Momentum: When the Momentum Index is below zero, it is plotted in red, highlighting negative momentum phases.
Usage:
A rising curve means an increase in upward momentum - if it is above the zero line. A rising curve below the zero line signifies a decrease in downward momentum. By the same token, a falling curve means an increase in downward momentum below the zero line, a decrease in upward momentum above the zero line.
This indicator is ideal for traders looking to complement their strategy with a visual tool that captures the essence of market momentum over a significant period. Use it to enhance your technical analysis and refine your trading decisions.
Relative VolumeHello traders,
"There's nothing new on Wall Street" is an age-old saying that still shows its relevance in modern day financial markets; volume still serves as a valuable tool for any trader just as it did for those that came and succeeded before us; in order to succeed in modern day markets one has to take it up a notch and dabble in complicated topics, like math. Now I dunno about you reader but I’m not keen on sitting around all day just to watch numbers on a screen; it’s pretty important to add some color into your life before it becomes dull but how can someone add colors into their trading toolkit as an aid rather than bother? With a bit of help from 3 other amazing open-source indicators you too can become a statistics enjoyer by combining math and colors to make pattern recognition much more intuitive and offering more peace of mind when trading. “Sir but how?”, glad you didn’t ask, it helps with simplifying statistics, in this case a Gaussian bellcurve
“HUH?”, you say? Alright class, Gaussian bellcurves for math dislikers 101 is in session
- Imagine that we have a bunch of numbers that we want to graph. We could just draw a line and plot the numbers on it, but that might not be very interesting.
- Instead, we can use the shape of a bell to show how many of each number we have.
- Let's say we have a lot of people and we want to graph how tall they are. We would start by making a line from the shortest person to the tallest person, and then we would draw the bell shape around the line.
- The bell shape is called a "Gaussian Bell Curve," and it shows us how many people are a certain height.
- In the middle of the bell, where it's the widest, we have the most people who are about average height. As we move to the sides of the bell, the curve gets lower because there are fewer people who are really tall or really short.
The bell curve discussed is the main idea for the candle coloring component of this indicator as being able to analyze the distribution of an entire dataset, in this case volume, can alert us when volume/participation in the market is away from its average using color, and therefore an opportunity could be present. Fair warning, it’s important to not strictly focus on volume as volume is meant to be confluence to the current structure of the market rather than causing tunnel vision.
Why 3 indicators to combine?
It starts with the RVOL by Mik3Christ3ns3n indicator as the backbone by calculating the average volume over a specified period of time, and then compares each new volume value to this average to determine whether it is above or below the average. The indicator then normalizes the volume data and calculates the z-score/standard deviation to determine whether the volume is within normal range or is an anomaly beyond a specified threshold which can also be set into an alert to aid in eyeing possible opportunities.
The code also includes Candle Coloring by Morty as it calculates a function to get the z-score for the size of the candle's body, and then compares it to the z-score for volume to determine whether the body size is a factor in the price action.
Finally, the code plots the anomalies and the normalized volume data on the chart using the first RVOL indicator mentioned, and colors the bars of the chart based on whether they are within normal range or are anomalies which comes from using code from veryfid's relative volume indicator.
Overall, this custom technical indicator is best used to identify unusual changes in trading volume, which may indicate potential price movements in the underlying.
How about some examples?
This first example is for my scalpers wanting to get in and out but not having much of an idea where or let alone how; using a tool like VWAP can be great for determining the area value to execute mean reversion trades once a speculator spots a colored candle anomaly at standard deviation band. Works best when VWAP is flat as it signals lack of conviction from both bulls and bears
This second example is for my fire and forget intraweek swing traders who want to execute a higher timeframe trend-following bias. A speculator starting 2023 off notices that the negative sentiment around Binance from late last year has quieted down and has conviction in upside after BTC began an uptrend as monthly VWAP (right chart) has began sloping up as well as a rally with momentum shown with the blue colored candle so the trader waits wait for a pullback for entry. On the chart to the left of the 4H the speculator notices a pullback into the area of interest to do business so a limit bid is left to enter for continued upside in Bitcoin through January 2023 just by keeping things simple
That’s really the main purpose of this indicator: simplicity of statistics for confluence using volume
Volume precedes price and price moves only for narrative to follow- why wait for your subjective Twitter timeline to give you a biased narrative to trade when you can use objective analysis by combining statistics and colors to allow for a cleaner execution process
“But what about risk management?” Glad you didn’t ask reader!
One last example then, we meet our trend following trader again feeling euphoric so they know profit taking season is coming soon but wants to leave emotion out of it. How to go about it? Same idea as our last trend following example: we see on the 4h chart to the right side shows Bitcoin lose and trade back within the 2nd standard deviation of quarterly VWAP which is telling our speculator that the uptrend has broken on top of which notices on the 30 minute chart on the left that aggressive market buyers have been steadily absorbed by limit sellers on multiple occasions of retesting 30,500 shown with the green colored candles and volume bars below, time to sell.
Turns out that selling was proactive risk management because price dumped thereafter
Hope this explanation gave you some useful insights on using statistics as colors from cherrypicked examples, remember that just because my examples are cherrypicked doesn’t invalidate these concepts at all as the market only does two things, initiate aggressive auctions and respond passively to auctions. This tool makes for seeing where that initiative aggressive activity is happening much simpler to deduce if others will respond to an anomaly of initiative aggressive activity or if the aggression will continue.
If there’s just one thing you take from this- simplicity above all, cheers and good luck
Fake StrategyTHIS IS A FAKE STRATEGY. PLEASE DO NOT USE THIS FOR TRADING.
Just publishing this to display how easily you can fake backtest results in the strategies. However, there are ways to identify the scams. Let's discuss about major red herrings in a strategy. How to identify them and stay away from them.
Any strategy which proclaims significantly high win rate (such as this) are not practical and can only be achieved via following means
Significantly high risk compared to reward
Trades are set in such a way that profits are taken in small movement whereas stops are significantly farther. By doing this, win rate will surely increase. But, will be picking pennies by risking plenty of capital. General trait of such strategies can be identified by comparing average trade and max drawdown . These kind of strategies will have significantly higher drawdown even though the number of losses are less. For example, 1 losing trade leading to drawdown of 10+% whereas every winning only contributes 0.25%.
We can also see this kind of behaviour in option selling strategies such as 0 and 1 DTE option selling strategies. Here too probability of winning can be pretty high (north of 90%). But, on every winning, you make 1-2% of your capital however on remaining trades, you will lose your complete capital - which leads to overall losing position.
Inducing repainting through code
This strategy is an excellent example of how repainting can be induced via code using request.securities method. There are plenty of ways a strategy or code can be made to repaint. Tradingview user manual has lots of information about repainting. Feel free to read through if you have extra time. If you look at this code, it is very simple to induce repainting in a strategy to make it look like an infinite money printing machine.
High Leverage and lack of usage of margin
Using leverage in pine can show false results. This is because, the strategy engine will not stop when equity goes below 0% until the trade is closed. But, that does not happen in real life. This is the reason why using leverage along with high risk and low reward trades can show false results overall making it look like the strategy is unbeatable. But, when you try to use that in real time, it is likely that account will be blown out.
To understand leverage conditions, please have a look at the strategy property fields - Order Size, Pyramiding, Commission, Slippage, Margin Long/Short.
Curve fitting
If the author claims that strategy will only work on particular set of instrument and particular timeframe, then the strategy is not real. It is curve fitting. Knowingly/Unknowingly author has moulded his strategy to fit what has happened in the past. This is general issue even non malicious author go through. It is very much essential to test the strategy across various set of instruments and timeframes to understand the real capability. Use back-testing as test cases. More test cases you have, more bug free your strategy will be. There are many methods to understand curve fitting and perform better testing of the strategy in hand which can be studied and implemented by authors.
Significantly short trades - a sign of lack of strategy
A strategy built using pine in general work on close of candle. So, all the calculations generally happen upon close of the candle. You can force intra-bar calculations using bar magnifier. But, that is not equivalent to tick data. Due to this reason, I consider any trade happening within a bar (Meaning open and close within the same bar) as not reliable. This is because, it is not possible for strategy back-tester to know whether entry condition is satisfied first or exit in a completely foolproof way. Bar magnifier can help reduce this issue - but will not eradicate this problem completely. If there are lots of trades in a strategy - which are closing within the same bar, this is very likely that the strategy backtest results are not reliable.
Hope this helps at least some people to understand the scams and stay away from it.
Multi TF Trend Indicator
...Mark Douglas in his book Trading in the Zone wrote
The longer the time frame, the more significant the trend, so a trending market on a daily bar chart is more significant than a trending market on a 30-minute bar chart. Therefore, the trend on the daily bar chart would take precedence over the trend on the 30-minute bar chart and would be considered the major trend. To determine the direction of the major trend, look at what is happening on a daily bar chart. If the trend is up on the daily, you are only going to look for a sell-off or retracement down to what your edge defines as support on the 30-minute chart. That's where you will become a buyer. On the other hand, if the trend is down on the daily, you are only going to look for a rally up to what your edge defines as a resistance level to be a seller on the 30-minute chart. Your objective is to determine, in a downtrending market, how far it can rally on an intraday basis and still not violate the symmetry of the longer trend. In an up-trending market, your objective is to determine how far it can sell off on an intraday basis without violating the symmetry of the longer trend. There's usually very little risk associated with these intraday support and resistance points, because you don't have to let the market go very far beyond them to tell you the trade isn't working.
The purpose of this indicator to show both the major and minor trend on the same chart with no need to switch between timeframes
Script includes
timeframe to determine the major trend
price curve, close price is default, but you can pick MA you want
type of coloring, either curve color or the background color
Implementation details
major trend is determined by the slope of the price curve
Further improvements
a variation of techniques for determining the major trend (crossing MA, pivot points etc.)
major trend change alerts
Thanks @loxx for pullData helper function
One-Sided Gaussian Filter w/ Channels [Loxx]One-Sided Gaussian Filter w/ Channels is a Gaussian Moving Average that is calculated using a Fibonacci weighting function. Keltner channels have been added to show zones of exhaustion. A better name would be "Half Gaussian bell weighted" or "Half normal distribution weighted" indicator, since the weights for calculation of the average (similar to linear weighted average) are taken from a normal distribution curve like function--but only the half of the curve is used for calculation.
Information of the Gaussian distribution can be found here : en.wikipedia.org and once you take a look at the standard normal distribution curve, it will be much clearer what is exactly done in this indicator.
After the Gaussian Filter is applied to the source input, an Ehlers' 2-Pole Super Smoother is applied to reduce noise without significant lag.
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
