Jimb0ws Strategy Trending Info PanelsJimb0ws Strategy — Golden Candles + Bubble Zones
A price-action/EMA strategy built for FX scalping and intraday swings. It colors Golden Candles when strong bodies touch/skim EMA20/50 in trend (“bubble”) and optionally highlights Robin Candles (break of the prior golden body). Signals are throttled per bubble and filtered by multiple higher-timeframe conditions.
How it trades
Trend bubbles: Uses EMA20/50/100/200 alignment on the chart timeframe; also reads 1H & 4H bubbles for context.
Entries: BUY/SELL labels appear only when a golden setup aligns with fractal/structure checks and all active filters pass.
Stops/Targets (strategy mode):
• Longs: SL = EMA100 if EMA200 > EMA100, else SL = EMA200.
• Shorts: SL = EMA100 if EMA200 < EMA100, else SL = EMA200.
• TP = RR × risk (default 2R).
An on-chart SL/TP info label prints the exact prices at each signal.
Risk filter options: disable beyond 1H EMA50, proximity band around 1H EMA50, wick overdrive veto, session filter (toggle on/off), max signals per bubble.
Visuals & tools
Colored EMAs (20/50/100/200), bubble zone background.
4H info panel (state, start time, duration); Prev-Day ATR panel sits above it.
Optional 1H info panel and consolidation warning.
Fractal markers (size selectable).
Alerts
1H bubble state change (Long/Short/Consolidation).
BUY/SELL signals.
Inputs worth checking
Session & timezone, min body size, pip tolerances, proximity/WOD filters, max signals per bubble, RR, SL/TP label offset.
Notes
Best on FX pairs; pip = mintick × 10. Backtest and adjust to your instrument and session. This is not financial advice.
"200元+股票大盘" için komut dosyalarını ara
Chart-Only Scanner — Pro Table v2.5.1Chart-Only Scanner — Pro Table v2.5
User Manual (Pine Script v6)
What this tool does (in one line)
A compact, on-chart table that scores the current chart symbol (or an optional override) using momentum, volume, trend, volatility, and pattern checks—so you can quickly decide UP, DOWN, or WAIT.
Quick Start (90 seconds)
Add the indicator to any chart and timeframe (1m…1M).
Leave “Override chart symbol” = OFF to auto-use the chart’s symbol.
Choose your layout:
Row (wide horizontal strip), or Grid (title + labeled cells).
Pick a size preset (Micro, Small, Medium, Large, Mobile).
Optional: turn on “Use Higher TF (EMA 20/50)” and set HTF Multiplier (e.g., 4 ⇒ if chart is 15m, HTF is 60m).
Watch the table:
DIR (↑/↓/→), ROC%, MOM, VOL, EMA stack, HTF, REV, SCORE, ACT.
Add an alert if you want: the script fires when |SCORE| ≥ Action threshold.
What to expect
A small table appears on the chart corner you choose, updating each bar (or only at bar close if you keep default smart-update).
The ACT cell shows 🔥 (strong), 👀 (medium), or ⏳ (weak).
Panels & Settings (every option explained)
Core
Momentum Period: Lookback for rate-of-change (ROC%). Shorter = more reactive; longer = smoother.
ROC% Threshold: Minimum absolute ROC% to call direction UP (↑) or DOWN (↓); otherwise →.
Require Volume Confirmation: If ON and VOL ≤ 1.0, the SCORE is forced to 0 (prevents low-volume false positives).
Override chart symbol + Custom symbol: By default, the indicator uses the chart’s symbol. Turn this ON to lock to a specific ticker (e.g., a perpetual).
Higher TF
Use Higher TF (EMA 20/50): Compares EMA20 vs EMA50 on a higher timeframe.
HTF Multiplier: Higher TF = (chart TF × multiplier).
Example: on 3H chart with multiplier 2 ⇒ HTF = 6H.
Volatility & Oscillators
ATR Length: Used to show ATR% (ATR relative to price).
RSI Length: Standard RSI; colors: green ≤30 (oversold), red ≥70 (overbought).
Stoch %K Length: With %D = SMA(%K, 3).
MACD Fast/Slow/Signal: Standard MACD values; we display Line, Signal, Histogram (L/S/H).
ADX Length (Wilder): Wilder’s smoothing (internal derivation); also shows +DI / −DI if you enable the ADX column.
EMAs / Trend
EMA Fast/Mid/Slow: We compute EMA(20/50/200) by default (editable).
EMA Stack: Bull if Fast > Mid > Slow; Bear if Fast < Mid < Slow; Flat otherwise.
Benchmark (optional, OFF by default)
Show Relative Strength vs Benchmark: Displays RS% = ROC(symbol) − ROC(benchmark) over the Momentum Period.
Benchmark Symbol: Ticker used for comparison (e.g., BTCUSDT as a market proxy).
Columns (show/hide)
Toggle which fields appear in the table. Hiding unused fields keeps the layout clean (especially on mobile).
Display
Layout Mode:
Row = a single two-row strip; each column is a metric.
Grid = a title row plus labeled pairs (label/value) arranged in rows.
Size Preset: Micro, Small, Medium, Large, Mobile change text size and the grid density.
Table Corner: Where the panel sits (e.g., Top Right).
Opaque Table Background: ON = dark card; OFF = transparent(ish).
Update Every Bar: ON = update intra-bar; OFF = smart update (last bar / real-time / confirmed history).
Action threshold (|score|): The cutoff for 🔥 and alert firing (default 70).
How to read each field
CHART: The active symbol name (or your custom override).
DIR: ↑ (ROC% > threshold), ↓ (ROC% < −threshold), → otherwise.
ROC%: Rate of change over Momentum Period.
Formula: (Close − Close ) / Close × 100.
MOM: A scaled momentum score: min(100, |ROC%| × 10).
VOL: Volume ratio vs 20-bar SMA: Volume / SMA(Volume,20).
1.5 highlights as yellow (significant participation).
ATR%: (ATR / Close) × 100 (volatility relative to price).
RSI: Colored for extremes: ≤30 green, ≥70 red.
Stoch K/D: %K and %D numbers.
MACD L/S/H: Line, Signal, Histogram. Histogram color reflects sign (green > 0, red < 0).
ADX, +DI, −DI: Trend strength and directional components (Wilder). ADX ≥ 25 is highlighted.
EMA 20/50/200: Current EMA values (editable lengths).
STACK: Bull/Bear/Flat as defined above.
VWAP%: (Close − VWAP) / Close × 100 (premium/discount to VWAP).
HTF: ▲ if HTF EMA20 > EMA50; ▼ if <; · if flat/off.
RS%: Symbol’s ROC% − Benchmark ROC% (positive = outperforming).
REV (reversal):
🟢 Eng/Pin = bullish engulfing or bullish pin detected,
🔴 Eng/Pin = bearish engulfing or bearish pin,
· = none.
SCORE (absolute shown as a number; sign shown via DIR and ACT):
Components:
base = MOM × 0.4
volBonus = VOL > 1.5 ? 20 : VOL × 13.33
htfBonus = use_mtf ? (HTF == DIR ? 30 : HTF == 0 ? 15 : 0) : 0
trendBonus = (STACK == DIR) ? 10 : 0
macdBonus = 0 (placeholder for future versions)
scoreRaw = base + volBonus + htfBonus + trendBonus + macdBonus
SCORE = DIR ≥ 0 ? scoreRaw : −scoreRaw
If Require Volume Confirmation and VOL ≤ 1.0 ⇒ SCORE = 0.
ACT:
🔥 if |SCORE| ≥ threshold
👀 if 50 < |SCORE| < threshold
⏳ otherwise
Practical examples
Strong long (trend + participation)
DIR = ↑, ROC% = +3.2, MOM ≈ 32, VOL = 1.9, STACK = Bull, HTF = ▲, REV = 🟢
SCORE: base(12.8) + volBonus(20) + htfBonus(30) + trend(10) ≈ 73 → ACT = 🔥
Action idea: look for longs on pullbacks; confirm risk with ATR%.
Weak long (no volume)
DIR = ↑, ROC% = +1.0, but VOL = 0.8 and Require Volume Confirmation = ON
SCORE forced to 0 → ACT = ⏳
Action: wait for volume > 1.0 or turn off confirmation knowingly.
Bearish reversal warning
DIR = →, REV = 🔴 (bearish engulfing), RSI = 68, HTF = ▼
SCORE may be mid-range; ACT = 👀
Action: watch for breakdown and rising VOL.
Alerts (how to use)
The script calls alert() whenever |SCORE| ≥ Action threshold.
To receive pop-ups, sounds, or emails: click “⏰ Alerts” in TradingView, choose this indicator, and pick “Any alert() function call.”
The alert message includes: symbol, |SCORE|, DIR.
Layout, Size, and Corner tips
Row is best when you want a compact status ribbon across the top.
Grid is clearer on big screens or when you enable many columns.
Size:
Mobile = one pair per row (tall, readable)
Micro/Small = dense; good for many fields
Large = presentation/screenshots
Corner: If the table overlaps price, change the corner or set Opaque Background = OFF.
Repaint & timeframe behavior
Default smart update prefers stability (last bar / live / confirmed history).
For a stricter, “close-only” behavior (less repaint): turn Update Every Bar = OFF and avoid Heikin Ashi when you want raw market OHLC (HA modifies price inputs).
HTF logic is derived from a clean, integer multiple of your chart timeframe (via multiplier). It works with 3H/4H and any TF.
Performance notes
The script analyzes one symbol (chart or override) with multiple metrics using efficient tuple requests.
If you later want a multi-symbol grid, do it with pages (10–15 per page + rotate) to stay within platform limits (recommended future add-on).
Troubleshooting
No table visible
Ensure the indicator is added and not hidden.
Try toggling Opaque Background or switch Corner (it might be behind other drawings).
Keep Columns count reasonable for the chosen Size.
If you turned ON Override, verify the Custom symbol exists on your data provider.
Numbers look different on HA candles
Heikin Ashi modifies OHLC; switch to regular candles if you need raw price metrics.
3H/4H issues
Use integer HTF Multiplier (e.g., 2, 4). The tool builds the correct string internally; no manual timeframe strings needed.
Power user tips
Volume gating: keeping Require Volume Confirmation = ON filters most fake moves; if you’re a scalper, reduce strictness or turn it off.
Action threshold: 60–80 is typical. Higher = fewer but stronger signals.
Benchmark RS%: great for spotting leaders/laggards; positive RS% = outperformance vs benchmark.
Change policy & safety
This version doesn’t alter your historical logic you tested (no radical changes).
Any future “radical” change (score weights, HTF logic, UI hiding data) will ship with a toggle and an Impact Statement so you can keep old behavior if you prefer.
Glossary (quick)
ROC%: Percent change over N bars.
MOM: Scaled momentum (0–100).
VOL ratio: Volume vs 20-bar average.
ATR%: ATR as % of price.
ADX/DI: Trend strength / direction components (Wilder).
EMA stack: Relationship between EMAs (bullish/bearish/flat).
VWAP%: Premium/discount to VWAP.
RS%: Relative strength vs benchmark.
Markov Chain [3D] | FractalystWhat exactly is a Markov Chain?
This indicator uses a Markov Chain model to analyze, quantify, and visualize the transitions between market regimes (Bull, Bear, Neutral) on your chart. It dynamically detects these regimes in real-time, calculates transition probabilities, and displays them as animated 3D spheres and arrows, giving traders intuitive insight into current and future market conditions.
How does a Markov Chain work, and how should I read this spheres-and-arrows diagram?
Think of three weather modes: Sunny, Rainy, Cloudy.
Each sphere is one mode. The loop on a sphere means “stay the same next step” (e.g., Sunny again tomorrow).
The arrows leaving a sphere show where things usually go next if they change (e.g., Sunny moving to Cloudy).
Some paths matter more than others. A more prominent loop means the current mode tends to persist. A more prominent outgoing arrow means a change to that destination is the usual next step.
Direction isn’t symmetric: moving Sunny→Cloudy can behave differently than Cloudy→Sunny.
Now relabel the spheres to markets: Bull, Bear, Neutral.
Spheres: market regimes (uptrend, downtrend, range).
Self‑loop: tendency for the current regime to continue on the next bar.
Arrows: the most common next regime if a switch happens.
How to read: Start at the sphere that matches current bar state. If the loop stands out, expect continuation. If one outgoing path stands out, that switch is the typical next step. Opposite directions can differ (Bear→Neutral doesn’t have to match Neutral→Bear).
What states and transitions are shown?
The three market states visualized are:
Bullish (Bull): Upward or strong-market regime.
Bearish (Bear): Downward or weak-market regime.
Neutral: Sideways or range-bound regime.
Bidirectional animated arrows and probability labels show how likely the market is to move from one regime to another (e.g., Bull → Bear or Neutral → Bull).
How does the regime detection system work?
You can use either built-in price returns (based on adaptive Z-score normalization) or supply three custom indicators (such as volume, oscillators, etc.).
Values are statistically normalized (Z-scored) over a configurable lookback period.
The normalized outputs are classified into Bull, Bear, or Neutral zones.
If using three indicators, their regime signals are averaged and smoothed for robustness.
How are transition probabilities calculated?
On every confirmed bar, the algorithm tracks the sequence of detected market states, then builds a rolling window of transitions.
The code maintains a transition count matrix for all regime pairs (e.g., Bull → Bear).
Transition probabilities are extracted for each possible state change using Laplace smoothing for numerical stability, and frequently updated in real-time.
What is unique about the visualization?
3D animated spheres represent each regime and change visually when active.
Animated, bidirectional arrows reveal transition probabilities and allow you to see both dominant and less likely regime flows.
Particles (moving dots) animate along the arrows, enhancing the perception of regime flow direction and speed.
All elements dynamically update with each new price bar, providing a live market map in an intuitive, engaging format.
Can I use custom indicators for regime classification?
Yes! Enable the "Custom Indicators" switch and select any three chart series as inputs. These will be normalized and combined (each with equal weight), broadening the regime classification beyond just price-based movement.
What does the “Lookback Period” control?
Lookback Period (default: 100) sets how much historical data builds the probability matrix. Shorter periods adapt faster to regime changes but may be noisier. Longer periods are more stable but slower to adapt.
How is this different from a Hidden Markov Model (HMM)?
It sets the window for both regime detection and probability calculations. Lower values make the system more reactive, but potentially noisier. Higher values smooth estimates and make the system more robust.
How is this Markov Chain different from a Hidden Markov Model (HMM)?
Markov Chain (as here): All market regimes (Bull, Bear, Neutral) are directly observable on the chart. The transition matrix is built from actual detected regimes, keeping the model simple and interpretable.
Hidden Markov Model: The actual regimes are unobservable ("hidden") and must be inferred from market output or indicator "emissions" using statistical learning algorithms. HMMs are more complex, can capture more subtle structure, but are harder to visualize and require additional machine learning steps for training.
A standard Markov Chain models transitions between observable states using a simple transition matrix, while a Hidden Markov Model assumes the true states are hidden (latent) and must be inferred from observable “emissions” like price or volume data. In practical terms, a Markov Chain is transparent and easier to implement and interpret; an HMM is more expressive but requires statistical inference to estimate hidden states from data.
Markov Chain: states are observable; you directly count or estimate transition probabilities between visible states. This makes it simpler, faster, and easier to validate and tune.
HMM: states are hidden; you only observe emissions generated by those latent states. Learning involves machine learning/statistical algorithms (commonly Baum–Welch/EM for training and Viterbi for decoding) to infer both the transition dynamics and the most likely hidden state sequence from data.
How does the indicator avoid “repainting” or look-ahead bias?
All regime changes and matrix updates happen only on confirmed (closed) bars, so no future data is leaked, ensuring reliable real-time operation.
Are there practical tuning tips?
Tune the Lookback Period for your asset/timeframe: shorter for fast markets, longer for stability.
Use custom indicators if your asset has unique regime drivers.
Watch for rapid changes in transition probabilities as early warning of a possible regime shift.
Who is this indicator for?
Quants and quantitative researchers exploring probabilistic market modeling, especially those interested in regime-switching dynamics and Markov models.
Programmers and system developers who need a probabilistic regime filter for systematic and algorithmic backtesting:
The Markov Chain indicator is ideally suited for programmatic integration via its bias output (1 = Bull, 0 = Neutral, -1 = Bear).
Although the visualization is engaging, the core output is designed for automated, rules-based workflows—not for discretionary/manual trading decisions.
Developers can connect the indicator’s output directly to their Pine Script logic (using input.source()), allowing rapid and robust backtesting of regime-based strategies.
It acts as a plug-and-play regime filter: simply plug the bias output into your entry/exit logic, and you have a scientifically robust, probabilistically-derived signal for filtering, timing, position sizing, or risk regimes.
The MC's output is intentionally "trinary" (1/0/-1), focusing on clear regime states for unambiguous decision-making in code. If you require nuanced, multi-probability or soft-label state vectors, consider expanding the indicator or stacking it with a probability-weighted logic layer in your scripting.
Because it avoids subjectivity, this approach is optimal for systematic quants, algo developers building backtested, repeatable strategies based on probabilistic regime analysis.
What's the mathematical foundation behind this?
The mathematical foundation behind this Markov Chain indicator—and probabilistic regime detection in finance—draws from two principal models: the (standard) Markov Chain and the Hidden Markov Model (HMM).
How to use this indicator programmatically?
The Markov Chain indicator automatically exports a bias value (+1 for Bullish, -1 for Bearish, 0 for Neutral) as a plot visible in the Data Window. This allows you to integrate its regime signal into your own scripts and strategies for backtesting, automation, or live trading.
Step-by-Step Integration with Pine Script (input.source)
Add the Markov Chain indicator to your chart.
This must be done first, since your custom script will "pull" the bias signal from the indicator's plot.
In your strategy, create an input using input.source()
Example:
//@version=5
strategy("MC Bias Strategy Example")
mcBias = input.source(close, "MC Bias Source")
After saving, go to your script’s settings. For the “MC Bias Source” input, select the plot/output of the Markov Chain indicator (typically its bias plot).
Use the bias in your trading logic
Example (long only on Bull, flat otherwise):
if mcBias == 1
strategy.entry("Long", strategy.long)
else
strategy.close("Long")
For more advanced workflows, combine mcBias with additional filters or trailing stops.
How does this work behind-the-scenes?
TradingView’s input.source() lets you use any plot from another indicator as a real-time, “live” data feed in your own script (source).
The selected bias signal is available to your Pine code as a variable, enabling logical decisions based on regime (trend-following, mean-reversion, etc.).
This enables powerful strategy modularity : decouple regime detection from entry/exit logic, allowing fast experimentation without rewriting core signal code.
Integrating 45+ Indicators with Your Markov Chain — How & Why
The Enhanced Custom Indicators Export script exports a massive suite of over 45 technical indicators—ranging from classic momentum (RSI, MACD, Stochastic, etc.) to trend, volume, volatility, and oscillator tools—all pre-calculated, centered/scaled, and available as plots.
// Enhanced Custom Indicators Export - 45 Technical Indicators
// Comprehensive technical analysis suite for advanced market regime detection
//@version=6
indicator('Enhanced Custom Indicators Export | Fractalyst', shorttitle='Enhanced CI Export', overlay=false, scale=scale.right, max_labels_count=500, max_lines_count=500)
// |----- Input Parameters -----| //
momentum_group = "Momentum Indicators"
trend_group = "Trend Indicators"
volume_group = "Volume Indicators"
volatility_group = "Volatility Indicators"
oscillator_group = "Oscillator Indicators"
display_group = "Display Settings"
// Common lengths
length_14 = input.int(14, "Standard Length (14)", minval=1, maxval=100, group=momentum_group)
length_20 = input.int(20, "Medium Length (20)", minval=1, maxval=200, group=trend_group)
length_50 = input.int(50, "Long Length (50)", minval=1, maxval=200, group=trend_group)
// Display options
show_table = input.bool(true, "Show Values Table", group=display_group)
table_size = input.string("Small", "Table Size", options= , group=display_group)
// |----- MOMENTUM INDICATORS (15 indicators) -----| //
// 1. RSI (Relative Strength Index)
rsi_14 = ta.rsi(close, length_14)
rsi_centered = rsi_14 - 50
// 2. Stochastic Oscillator
stoch_k = ta.stoch(close, high, low, length_14)
stoch_d = ta.sma(stoch_k, 3)
stoch_centered = stoch_k - 50
// 3. Williams %R
williams_r = ta.stoch(close, high, low, length_14) - 100
// 4. MACD (Moving Average Convergence Divergence)
= ta.macd(close, 12, 26, 9)
// 5. Momentum (Rate of Change)
momentum = ta.mom(close, length_14)
momentum_pct = (momentum / close ) * 100
// 6. Rate of Change (ROC)
roc = ta.roc(close, length_14)
// 7. Commodity Channel Index (CCI)
cci = ta.cci(close, length_20)
// 8. Money Flow Index (MFI)
mfi = ta.mfi(close, length_14)
mfi_centered = mfi - 50
// 9. Awesome Oscillator (AO)
ao = ta.sma(hl2, 5) - ta.sma(hl2, 34)
// 10. Accelerator Oscillator (AC)
ac = ao - ta.sma(ao, 5)
// 11. Chande Momentum Oscillator (CMO)
cmo = ta.cmo(close, length_14)
// 12. Detrended Price Oscillator (DPO)
dpo = close - ta.sma(close, length_20)
// 13. Price Oscillator (PPO)
ppo = ta.sma(close, 12) - ta.sma(close, 26)
ppo_pct = (ppo / ta.sma(close, 26)) * 100
// 14. TRIX
trix_ema1 = ta.ema(close, length_14)
trix_ema2 = ta.ema(trix_ema1, length_14)
trix_ema3 = ta.ema(trix_ema2, length_14)
trix = ta.roc(trix_ema3, 1) * 10000
// 15. Klinger Oscillator
klinger = ta.ema(volume * (high + low + close) / 3, 34) - ta.ema(volume * (high + low + close) / 3, 55)
// 16. Fisher Transform
fisher_hl2 = 0.5 * (hl2 - ta.lowest(hl2, 10)) / (ta.highest(hl2, 10) - ta.lowest(hl2, 10)) - 0.25
fisher = 0.5 * math.log((1 + fisher_hl2) / (1 - fisher_hl2))
// 17. Stochastic RSI
stoch_rsi = ta.stoch(rsi_14, rsi_14, rsi_14, length_14)
stoch_rsi_centered = stoch_rsi - 50
// 18. Relative Vigor Index (RVI)
rvi_num = ta.swma(close - open)
rvi_den = ta.swma(high - low)
rvi = rvi_den != 0 ? rvi_num / rvi_den : 0
// 19. Balance of Power (BOP)
bop = (close - open) / (high - low)
// |----- TREND INDICATORS (10 indicators) -----| //
// 20. Simple Moving Average Momentum
sma_20 = ta.sma(close, length_20)
sma_momentum = ((close - sma_20) / sma_20) * 100
// 21. Exponential Moving Average Momentum
ema_20 = ta.ema(close, length_20)
ema_momentum = ((close - ema_20) / ema_20) * 100
// 22. Parabolic SAR
sar = ta.sar(0.02, 0.02, 0.2)
sar_trend = close > sar ? 1 : -1
// 23. Linear Regression Slope
lr_slope = ta.linreg(close, length_20, 0) - ta.linreg(close, length_20, 1)
// 24. Moving Average Convergence (MAC)
mac = ta.sma(close, 10) - ta.sma(close, 30)
// 25. Trend Intensity Index (TII)
tii_sum = 0.0
for i = 1 to length_20
tii_sum += close > close ? 1 : 0
tii = (tii_sum / length_20) * 100
// 26. Ichimoku Cloud Components
ichimoku_tenkan = (ta.highest(high, 9) + ta.lowest(low, 9)) / 2
ichimoku_kijun = (ta.highest(high, 26) + ta.lowest(low, 26)) / 2
ichimoku_signal = ichimoku_tenkan > ichimoku_kijun ? 1 : -1
// 27. MESA Adaptive Moving Average (MAMA)
mama_alpha = 2.0 / (length_20 + 1)
mama = ta.ema(close, length_20)
mama_momentum = ((close - mama) / mama) * 100
// 28. Zero Lag Exponential Moving Average (ZLEMA)
zlema_lag = math.round((length_20 - 1) / 2)
zlema_data = close + (close - close )
zlema = ta.ema(zlema_data, length_20)
zlema_momentum = ((close - zlema) / zlema) * 100
// |----- VOLUME INDICATORS (6 indicators) -----| //
// 29. On-Balance Volume (OBV)
obv = ta.obv
// 30. Volume Rate of Change (VROC)
vroc = ta.roc(volume, length_14)
// 31. Price Volume Trend (PVT)
pvt = ta.pvt
// 32. Negative Volume Index (NVI)
nvi = 0.0
nvi := volume < volume ? nvi + ((close - close ) / close ) * nvi : nvi
// 33. Positive Volume Index (PVI)
pvi = 0.0
pvi := volume > volume ? pvi + ((close - close ) / close ) * pvi : pvi
// 34. Volume Oscillator
vol_osc = ta.sma(volume, 5) - ta.sma(volume, 10)
// 35. Ease of Movement (EOM)
eom_distance = high - low
eom_box_height = volume / 1000000
eom = eom_box_height != 0 ? eom_distance / eom_box_height : 0
eom_sma = ta.sma(eom, length_14)
// 36. Force Index
force_index = volume * (close - close )
force_index_sma = ta.sma(force_index, length_14)
// |----- VOLATILITY INDICATORS (10 indicators) -----| //
// 37. Average True Range (ATR)
atr = ta.atr(length_14)
atr_pct = (atr / close) * 100
// 38. Bollinger Bands Position
bb_basis = ta.sma(close, length_20)
bb_dev = 2.0 * ta.stdev(close, length_20)
bb_upper = bb_basis + bb_dev
bb_lower = bb_basis - bb_dev
bb_position = bb_dev != 0 ? (close - bb_basis) / bb_dev : 0
bb_width = bb_dev != 0 ? (bb_upper - bb_lower) / bb_basis * 100 : 0
// 39. Keltner Channels Position
kc_basis = ta.ema(close, length_20)
kc_range = ta.ema(ta.tr, length_20)
kc_upper = kc_basis + (2.0 * kc_range)
kc_lower = kc_basis - (2.0 * kc_range)
kc_position = kc_range != 0 ? (close - kc_basis) / kc_range : 0
// 40. Donchian Channels Position
dc_upper = ta.highest(high, length_20)
dc_lower = ta.lowest(low, length_20)
dc_basis = (dc_upper + dc_lower) / 2
dc_position = (dc_upper - dc_lower) != 0 ? (close - dc_basis) / (dc_upper - dc_lower) : 0
// 41. Standard Deviation
std_dev = ta.stdev(close, length_20)
std_dev_pct = (std_dev / close) * 100
// 42. Relative Volatility Index (RVI)
rvi_up = ta.stdev(close > close ? close : 0, length_14)
rvi_down = ta.stdev(close < close ? close : 0, length_14)
rvi_total = rvi_up + rvi_down
rvi_volatility = rvi_total != 0 ? (rvi_up / rvi_total) * 100 : 50
// 43. Historical Volatility
hv_returns = math.log(close / close )
hv = ta.stdev(hv_returns, length_20) * math.sqrt(252) * 100
// 44. Garman-Klass Volatility
gk_vol = math.log(high/low) * math.log(high/low) - (2*math.log(2)-1) * math.log(close/open) * math.log(close/open)
gk_volatility = math.sqrt(ta.sma(gk_vol, length_20)) * 100
// 45. Parkinson Volatility
park_vol = math.log(high/low) * math.log(high/low)
parkinson = math.sqrt(ta.sma(park_vol, length_20) / (4 * math.log(2))) * 100
// 46. Rogers-Satchell Volatility
rs_vol = math.log(high/close) * math.log(high/open) + math.log(low/close) * math.log(low/open)
rogers_satchell = math.sqrt(ta.sma(rs_vol, length_20)) * 100
// |----- OSCILLATOR INDICATORS (5 indicators) -----| //
// 47. Elder Ray Index
elder_bull = high - ta.ema(close, 13)
elder_bear = low - ta.ema(close, 13)
elder_power = elder_bull + elder_bear
// 48. Schaff Trend Cycle (STC)
stc_macd = ta.ema(close, 23) - ta.ema(close, 50)
stc_k = ta.stoch(stc_macd, stc_macd, stc_macd, 10)
stc_d = ta.ema(stc_k, 3)
stc = ta.stoch(stc_d, stc_d, stc_d, 10)
// 49. Coppock Curve
coppock_roc1 = ta.roc(close, 14)
coppock_roc2 = ta.roc(close, 11)
coppock = ta.wma(coppock_roc1 + coppock_roc2, 10)
// 50. Know Sure Thing (KST)
kst_roc1 = ta.roc(close, 10)
kst_roc2 = ta.roc(close, 15)
kst_roc3 = ta.roc(close, 20)
kst_roc4 = ta.roc(close, 30)
kst = ta.sma(kst_roc1, 10) + 2*ta.sma(kst_roc2, 10) + 3*ta.sma(kst_roc3, 10) + 4*ta.sma(kst_roc4, 15)
// 51. Percentage Price Oscillator (PPO)
ppo_line = ((ta.ema(close, 12) - ta.ema(close, 26)) / ta.ema(close, 26)) * 100
ppo_signal = ta.ema(ppo_line, 9)
ppo_histogram = ppo_line - ppo_signal
// |----- PLOT MAIN INDICATORS -----| //
// Plot key momentum indicators
plot(rsi_centered, title="01_RSI_Centered", color=color.purple, linewidth=1)
plot(stoch_centered, title="02_Stoch_Centered", color=color.blue, linewidth=1)
plot(williams_r, title="03_Williams_R", color=color.red, linewidth=1)
plot(macd_histogram, title="04_MACD_Histogram", color=color.orange, linewidth=1)
plot(cci, title="05_CCI", color=color.green, linewidth=1)
// Plot trend indicators
plot(sma_momentum, title="06_SMA_Momentum", color=color.navy, linewidth=1)
plot(ema_momentum, title="07_EMA_Momentum", color=color.maroon, linewidth=1)
plot(sar_trend, title="08_SAR_Trend", color=color.teal, linewidth=1)
plot(lr_slope, title="09_LR_Slope", color=color.lime, linewidth=1)
plot(mac, title="10_MAC", color=color.fuchsia, linewidth=1)
// Plot volatility indicators
plot(atr_pct, title="11_ATR_Pct", color=color.yellow, linewidth=1)
plot(bb_position, title="12_BB_Position", color=color.aqua, linewidth=1)
plot(kc_position, title="13_KC_Position", color=color.olive, linewidth=1)
plot(std_dev_pct, title="14_StdDev_Pct", color=color.silver, linewidth=1)
plot(bb_width, title="15_BB_Width", color=color.gray, linewidth=1)
// Plot volume indicators
plot(vroc, title="16_VROC", color=color.blue, linewidth=1)
plot(eom_sma, title="17_EOM", color=color.red, linewidth=1)
plot(vol_osc, title="18_Vol_Osc", color=color.green, linewidth=1)
plot(force_index_sma, title="19_Force_Index", color=color.orange, linewidth=1)
plot(obv, title="20_OBV", color=color.purple, linewidth=1)
// Plot additional oscillators
plot(ao, title="21_Awesome_Osc", color=color.navy, linewidth=1)
plot(cmo, title="22_CMO", color=color.maroon, linewidth=1)
plot(dpo, title="23_DPO", color=color.teal, linewidth=1)
plot(trix, title="24_TRIX", color=color.lime, linewidth=1)
plot(fisher, title="25_Fisher", color=color.fuchsia, linewidth=1)
// Plot more momentum indicators
plot(mfi_centered, title="26_MFI_Centered", color=color.yellow, linewidth=1)
plot(ac, title="27_AC", color=color.aqua, linewidth=1)
plot(ppo_pct, title="28_PPO_Pct", color=color.olive, linewidth=1)
plot(stoch_rsi_centered, title="29_StochRSI_Centered", color=color.silver, linewidth=1)
plot(klinger, title="30_Klinger", color=color.gray, linewidth=1)
// Plot trend continuation
plot(tii, title="31_TII", color=color.blue, linewidth=1)
plot(ichimoku_signal, title="32_Ichimoku_Signal", color=color.red, linewidth=1)
plot(mama_momentum, title="33_MAMA_Momentum", color=color.green, linewidth=1)
plot(zlema_momentum, title="34_ZLEMA_Momentum", color=color.orange, linewidth=1)
plot(bop, title="35_BOP", color=color.purple, linewidth=1)
// Plot volume continuation
plot(nvi, title="36_NVI", color=color.navy, linewidth=1)
plot(pvi, title="37_PVI", color=color.maroon, linewidth=1)
plot(momentum_pct, title="38_Momentum_Pct", color=color.teal, linewidth=1)
plot(roc, title="39_ROC", color=color.lime, linewidth=1)
plot(rvi, title="40_RVI", color=color.fuchsia, linewidth=1)
// Plot volatility continuation
plot(dc_position, title="41_DC_Position", color=color.yellow, linewidth=1)
plot(rvi_volatility, title="42_RVI_Volatility", color=color.aqua, linewidth=1)
plot(hv, title="43_Historical_Vol", color=color.olive, linewidth=1)
plot(gk_volatility, title="44_GK_Volatility", color=color.silver, linewidth=1)
plot(parkinson, title="45_Parkinson_Vol", color=color.gray, linewidth=1)
// Plot final oscillators
plot(rogers_satchell, title="46_RS_Volatility", color=color.blue, linewidth=1)
plot(elder_power, title="47_Elder_Power", color=color.red, linewidth=1)
plot(stc, title="48_STC", color=color.green, linewidth=1)
plot(coppock, title="49_Coppock", color=color.orange, linewidth=1)
plot(kst, title="50_KST", color=color.purple, linewidth=1)
// Plot final indicators
plot(ppo_histogram, title="51_PPO_Histogram", color=color.navy, linewidth=1)
plot(pvt, title="52_PVT", color=color.maroon, linewidth=1)
// |----- Reference Lines -----| //
hline(0, "Zero Line", color=color.gray, linestyle=hline.style_dashed, linewidth=1)
hline(50, "Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(-50, "Lower Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(25, "Upper Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(-25, "Lower Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
// |----- Enhanced Information Table -----| //
if show_table and barstate.islast
table_position = position.top_right
table_text_size = table_size == "Tiny" ? size.tiny : table_size == "Small" ? size.small : size.normal
var table info_table = table.new(table_position, 3, 18, bgcolor=color.new(color.white, 85), border_width=1, border_color=color.gray)
// Headers
table.cell(info_table, 0, 0, 'Category', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
table.cell(info_table, 1, 0, 'Indicator', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
table.cell(info_table, 2, 0, 'Value', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
// Key Momentum Indicators
table.cell(info_table, 0, 1, 'MOMENTUM', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90))
table.cell(info_table, 1, 1, 'RSI Centered', text_color=color.purple, text_size=table_text_size)
table.cell(info_table, 2, 1, str.tostring(rsi_centered, '0.00'), text_color=color.purple, text_size=table_text_size)
table.cell(info_table, 0, 2, '', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 1, 2, 'Stoch Centered', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 2, str.tostring(stoch_centered, '0.00'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 3, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 3, 'Williams %R', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 3, str.tostring(williams_r, '0.00'), text_color=color.red, text_size=table_text_size)
table.cell(info_table, 0, 4, '', text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 1, 4, 'MACD Histogram', text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 2, 4, str.tostring(macd_histogram, '0.000'), text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 0, 5, '', text_color=color.green, text_size=table_text_size)
table.cell(info_table, 1, 5, 'CCI', text_color=color.green, text_size=table_text_size)
table.cell(info_table, 2, 5, str.tostring(cci, '0.00'), text_color=color.green, text_size=table_text_size)
// Key Trend Indicators
table.cell(info_table, 0, 6, 'TREND', text_color=color.navy, text_size=table_text_size, bgcolor=color.new(color.navy, 90))
table.cell(info_table, 1, 6, 'SMA Momentum %', text_color=color.navy, text_size=table_text_size)
table.cell(info_table, 2, 6, str.tostring(sma_momentum, '0.00'), text_color=color.navy, text_size=table_text_size)
table.cell(info_table, 0, 7, '', text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 1, 7, 'EMA Momentum %', text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 2, 7, str.tostring(ema_momentum, '0.00'), text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 0, 8, '', text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 1, 8, 'SAR Trend', text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 2, 8, str.tostring(sar_trend, '0'), text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 0, 9, '', text_color=color.lime, text_size=table_text_size)
table.cell(info_table, 1, 9, 'Linear Regression', text_color=color.lime, text_size=table_text_size)
table.cell(info_table, 2, 9, str.tostring(lr_slope, '0.000'), text_color=color.lime, text_size=table_text_size)
// Key Volatility Indicators
table.cell(info_table, 0, 10, 'VOLATILITY', text_color=color.yellow, text_size=table_text_size, bgcolor=color.new(color.yellow, 90))
table.cell(info_table, 1, 10, 'ATR %', text_color=color.yellow, text_size=table_text_size)
table.cell(info_table, 2, 10, str.tostring(atr_pct, '0.00'), text_color=color.yellow, text_size=table_text_size)
table.cell(info_table, 0, 11, '', text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 1, 11, 'BB Position', text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 2, 11, str.tostring(bb_position, '0.00'), text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 0, 12, '', text_color=color.olive, text_size=table_text_size)
table.cell(info_table, 1, 12, 'KC Position', text_color=color.olive, text_size=table_text_size)
table.cell(info_table, 2, 12, str.tostring(kc_position, '0.00'), text_color=color.olive, text_size=table_text_size)
// Key Volume Indicators
table.cell(info_table, 0, 13, 'VOLUME', text_color=color.blue, text_size=table_text_size, bgcolor=color.new(color.blue, 90))
table.cell(info_table, 1, 13, 'Volume ROC', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 13, str.tostring(vroc, '0.00'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 14, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 14, 'EOM', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 14, str.tostring(eom_sma, '0.000'), text_color=color.red, text_size=table_text_size)
// Key Oscillators
table.cell(info_table, 0, 15, 'OSCILLATORS', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90))
table.cell(info_table, 1, 15, 'Awesome Osc', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 15, str.tostring(ao, '0.000'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 16, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 16, 'Fisher Transform', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 16, str.tostring(fisher, '0.000'), text_color=color.red, text_size=table_text_size)
// Summary Statistics
table.cell(info_table, 0, 17, 'SUMMARY', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.gray, 70))
table.cell(info_table, 1, 17, 'Total Indicators: 52', text_color=color.black, text_size=table_text_size)
regime_color = rsi_centered > 10 ? color.green : rsi_centered < -10 ? color.red : color.gray
regime_text = rsi_centered > 10 ? "BULLISH" : rsi_centered < -10 ? "BEARISH" : "NEUTRAL"
table.cell(info_table, 2, 17, regime_text, text_color=regime_color, text_size=table_text_size)
This makes it the perfect “indicator backbone” for quantitative and systematic traders who want to prototype, combine, and test new regime detection models—especially in combination with the Markov Chain indicator.
How to use this script with the Markov Chain for research and backtesting:
Add the Enhanced Indicator Export to your chart.
Every calculated indicator is available as an individual data stream.
Connect the indicator(s) you want as custom input(s) to the Markov Chain’s “Custom Indicators” option.
In the Markov Chain indicator’s settings, turn ON the custom indicator mode.
For each of the three custom indicator inputs, select the exported plot from the Enhanced Export script—the menu lists all 45+ signals by name.
This creates a powerful, modular regime-detection engine where you can mix-and-match momentum, trend, volume, or custom combinations for advanced filtering.
Backtest regime logic directly.
Once you’ve connected your chosen indicators, the Markov Chain script performs regime detection (Bull/Neutral/Bear) based on your selected features—not just price returns.
The regime detection is robust, automatically normalized (using Z-score), and outputs bias (1, -1, 0) for plug-and-play integration.
Export the regime bias for programmatic use.
As described above, use input.source() in your Pine Script strategy or system and link the bias output.
You can now filter signals, control trade direction/size, or design pairs-trading that respect true, indicator-driven market regimes.
With this framework, you’re not limited to static or simplistic regime filters. You can rigorously define, test, and refine what “market regime” means for your strategies—using the technical features that matter most to you.
Optimize your signal generation by backtesting across a universe of meaningful indicator blends.
Enhance risk management with objective, real-time regime boundaries.
Accelerate your research: iterate quickly, swap indicator components, and see results with minimal code changes.
Automate multi-asset or pairs-trading by integrating regime context directly into strategy logic.
Add both scripts to your chart, connect your preferred features, and start investigating your best regime-based trades—entirely within the TradingView ecosystem.
References & Further Reading
Ang, A., & Bekaert, G. (2002). “Regime Switches in Interest Rates.” Journal of Business & Economic Statistics, 20(2), 163–182.
Hamilton, J. D. (1989). “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica, 57(2), 357–384.
Markov, A. A. (1906). "Extension of the Limit Theorems of Probability Theory to a Sum of Variables Connected in a Chain." The Notes of the Imperial Academy of Sciences of St. Petersburg.
Guidolin, M., & Timmermann, A. (2007). “Asset Allocation under Multivariate Regime Switching.” Journal of Economic Dynamics and Control, 31(11), 3503–3544.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets. New York Institute of Finance.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” Journal of Finance, 47(5), 1731–1764.
Zucchini, W., MacDonald, I. L., & Langrock, R. (2017). Hidden Markov Models for Time Series: An Introduction Using R (2nd ed.). Chapman and Hall/CRC.
On Quantitative Finance and Markov Models:
Lo, A. W., & Hasanhodzic, J. (2009). The Heretics of Finance: Conversations with Leading Practitioners of Technical Analysis. Bloomberg Press.
Patterson, S. (2016). The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. Penguin Press.
TradingView Pine Script Documentation: www.tradingview.com
TradingView Blog: “Use an Input From Another Indicator With Your Strategy” www.tradingview.com
GeeksforGeeks: “What is the Difference Between Markov Chains and Hidden Markov Models?” www.geeksforgeeks.org
What makes this indicator original and unique?
- On‑chart, real‑time Markov. The chain is drawn directly on your chart. You see the current regime, its tendency to stay (self‑loop), and the usual next step (arrows) as bars confirm.
- Source‑agnostic by design. The engine runs on any series you select via input.source() — price, your own oscillator, a composite score, anything you compute in the script.
- Automatic normalization + regime mapping. Different inputs live on different scales. The script standardizes your chosen source and maps it into clear regimes (e.g., Bull / Bear / Neutral) without you micromanaging thresholds each time.
- Rolling, bar‑by‑bar learning. Transition tendencies are computed from a rolling window of confirmed bars. What you see is exactly what the market did in that window.
- Fast experimentation. Switch the source, adjust the window, and the Markov view updates instantly. It’s a rapid way to test ideas and feel regime persistence/switch behavior.
Integrate your own signals (using input.source())
- In settings, choose the Source . This is powered by input.source() .
- Feed it price, an indicator you compute inside the script, or a custom composite series.
- The script will automatically normalize that series and process it through the Markov engine, mapping it to regimes and updating the on‑chart spheres/arrows in real time.
Credits:
Deep gratitude to @RicardoSantos for both the foundational Markov chain processing engine and inspiring open-source contributions, which made advanced probabilistic market modeling accessible to the TradingView community.
Special thanks to @Alien_Algorithms for the innovative and visually stunning 3D sphere logic that powers the indicator’s animated, regime-based visualization.
Disclaimer
This tool summarizes recent behavior. It is not financial advice and not a guarantee of future results.
Multi-Timeframe Bias Dashboard + VolatilityWhat it is: A corner table (overlay) that gives a quick higher-timeframe read for Daily / 4H / 1H using EMA alignment, MACD, RSI, plus a volatility gauge.
How it works (per timeframe):
EMA block (50/100/200): “Above/Below/Mixed” based on price vs all three EMAs.
MACD: “Bullish/Bearish/Neutral” from MACD line vs Signal and histogram sign.
RSI: Prints the value and an ↑/↓ based on 50 line.
Volatility: Compares ATR(14) to its SMA over 20 bars → High (>*1.2), Normal, Low (<*0.8).
Bias: Combines three votes (EMA, MACD, RSI):
Bullish if ≥2 bullish, Bearish if ≥2 bearish, else Mixed.
Display:
Rows: D / 4H / 1H.
Columns: Bias, EMA(50/100/200), RSI, MACD, Volatility.
Bias cell is color-coded (green/red/gray).
Position setting lets you park the table in Top Right / Bottom Right / Bottom Left (works on mobile too).
Use it for:
Quickly aligning intraday setups with higher-TF direction.
Skipping low-volatility periods.
Confirming momentum (MACD/RSI) when price returns to your OB/FVG zones.
Trishul Tap Signals (v6) — Liquidity Sweep + Imbalanced RetestTrishul Tap Signals — Liquidity Sweep + Imbalanced Retest
Type: Signal-only indicator (non-repainting)
Style: Price-action + Liquidity + Trend-following
Best for: Intraday & Swing Trading — any liquid market (stocks, futures, crypto, FX)
Timeframes: Any (5m–1D recommended)
Concept
The Trishul Tap setup is a liquidity-driven retest play inspired by order-flow and Smart Money Concepts.
It identifies one-sided impulse candles that also sweep liquidity (grab stops above/below a recent swing), then waits for price to retest the origin of that candle to enter in the trend direction.
Think of it as the three points of a trident:
Trend filter — Only signals with the prevailing trend.
Liquidity sweep — Candle takes out a recent swing high/low (stop-hunt).
Imbalanced retest — Price taps the candle’s open/low (bull) or open/high (bear).
Bullish Setup
Trend Filter: Price above EMA(200).
Impulse Candle:
Green close.
Upper wick ≥ (wickRatio × lower wick).
Lower wick ≤ (oppWickMaxFrac × full range).
Liquidity Sweep: Candle’s high exceeds the highest high of the last sweepLookback bars (excluding current).
Tap Entry: Buy signal triggers when price later taps the candle’s low or open (user choice) within expireBars.
Bearish Setup
Trend Filter: Price below EMA(200).
Impulse Candle:
Red close.
Lower wick ≥ (wickRatio × upper wick).
Upper wick ≤ (oppWickMaxFrac × full range).
Liquidity Sweep: Candle’s low breaks the lowest low of the last sweepLookback bars (excluding current).
Tap Entry: Sell signal triggers when price later taps the candle’s high or open (user choice) within expireBars.
Inputs
Trend EMA Length: Default 200.
Sweep Lookback: Number of bars for liquidity sweep check (default 20).
Wick Ratio: Required size ratio of dominant wick to opposite wick (default 2.0).
Opposite Wick Max %: Opposite wick must be ≤ this fraction of the candle’s range (default 25%).
Tap Tolerance (ticks): How close price must come to the level to count as a tap.
Expire Bars: Max bars after setup to allow a valid tap.
One Signal per Level: If ON, a base is “consumed” after first signal.
Plot Tap Levels: Show horizontal lines for active bases.
Show Setup Labels: Mark the origin sweep candle.
Plots & Visuals
EMA Trend Line — trend filter reference.
Tap Levels —
Green = bullish base (origin candle’s low/open).
Red = bearish base (origin candle’s high/open).
Labels — Show where the setup candle formed.
Signals —
BUY: triangle-up below bar at bullish tap.
SELL: triangle-down above bar at bearish tap.
Alerts
Two built-in conditions:
BUY Signal (Trishul Tap) — triggers on bullish tap.
SELL Signal (Trishul Tap) — triggers on bearish tap.
Set via Alerts panel → Condition = this indicator → Choose signal type.
How to Trade It
Use in liquid markets with clean price structure.
Confirm with HTF structure, volume spikes, or other confluence if desired.
Place stop just beyond the tap level (or ATR-based).
Target 1–2R or trail behind structure.
Why It Works
Liquidity sweep traps traders entering late (breakout buyers or panic sellers) and forces them to exit in the opposite direction, fueling your entry.
Wick imbalance confirms directional aggression by one side.
Trend filter keeps you aligned with the market’s dominant flow.
Retest entry lets you enter at a better price with reduced risk.
Non-Repainting
Setups form only on confirmed bar closes.
Signals trigger only on later bars that tap the stored level.
No lookahead functions are used.
Disclaimer
This script is for educational purposes only and does not constitute financial advice. Test thoroughly in a simulator or demo before using in live markets. Trading involves risk.
Spice • Micro Suite (T/r & B/r)What it is
A single Pine v5 indicator that stacks:
EMA ribbon + a “special” EMA (11 vs 34) line that flips color on trend.
MTF-RSI “pressure” check with simple up/down arrows.
Bollinger-Band re-entry system with Top/Bottom triggers (T/B) and confirmations (r) in the next N bars.
Classic candlestick add-ons: 3-Line Strike and Leledc exhaustion dots.
Your Micro Dots engine (ATR-based regime + Variable Moving Average filter) + an optional VMA trend line.
Alerts for all the above.
Key signals (what prints on the chart)
EMAs (20/50/100/200): plotted faintly; EMA-34 is drawn and colored by the 11>34 trend.
RSI arrows
Checks RSI(6) on the current TF and (optionally) 5m/15m/30m/1h/4h/1D.
Down arrow: current RSI > 70 and the selected higher TF RSIs are also > 70 (pressure cluster just cooled; barssince(redZone)<2).
Up arrow: current RSI < 30 and selected higher TFs also < 30 (barssince(greenZone)<2).
Bollinger Reversals (your update)
T (Top trigger): first close back inside the upper BB (crossunder(close, upper)).
B (Bottom trigger): first close back inside the lower BB (crossover(close, lower)).
r (Confirm): within the next confirmBars bars (input), price also
closes below the T-bar’s low → top r above bar
closes above the B-bar’s high → bottom r below bar
Bar tinting
Only the T/B trigger bars are tinted (yellow/orange). Everything else stays your normal candle colors (unless you add the optional “trend candles” block I gave you).
3-Line Strike
Prints a small green/red circle when the 3-line strike pattern appears (bull/bear).
Leledc Exhaustion
Calculates a running buy/sell index; prints a small ∘ at major highs/lows when exhaustion conditions hit (major==-1 high, major==1 low).
Micro Dots (your second script, merged)
ATR “micro supertrend” defines regime (up/down).
A fast Variable Moving Average + a simple MA(18) filter.
Green dot below bar when: VMA < price, price > MA(18), regime up, and VMA not pointing down.
Red dot above bar for the bearish mirror.
Separate VMA trend line (length = Fast/Med/Slow) that colors green/red/orange by slope.
Inputs you’ll care about
Top/Bot Reversal → confirmBars (how many bars you allow to confirm the T/B trigger).
RSI Timeframes → toggle which HTFs must agree with the OB/OS condition.
EMAs → show/hide and lengths.
BB → show/hide basis/bands (used for T/B even if hidden).
Micro → show dots, show VMA line, choose intensity (Fast/Med/Slow).
Alerts
Prebuilt alerts for: RSI Up/Down, T/B triggers, T/B confirmations, 3-Line Strike bull/bear, Leledc highs/lows, EMA crosses (20/50/100/200), the special 11/34 trend change, Micro Dots, and VMA price cross. (Alert messages are const strings so they compile cleanly.)
How to read clusters (quick playbook)
Reversal short: see T on/near upper band → get an r within your window → bonus confidence if an RSI down arrow or Leledc ∘ high shows up around the same time.
Reversal long: mirror with B then r, plus RSI up arrow / Leledc ∘ low.
Continuation: ignore lone T/B if Micro Dot stays green (or red) and EMA-11 > EMA-34 remains true.
Why your candles look “normal”
By design, the script only colors bars on T or B trigger bars. If you want always-on trend candles, use the small block I gave you to color by EMA(20/50) (or any rule you like) and let T/B override on trigger bars.
CTA-min D1 — Donchian 55/20 Trend Breakout (ATR Risk)What it is
A clean, daily trend-following breakout inspired by classic CTA/Turtle logic. It buys strength and sells weakness, then lets winners run with a channel-based trailing stop. No curve-fitting, no clutter—just rules.
How it trades
Timeframe: Daily (D1)
Entry: Close breaks the previous 55-bar Donchian channel (above for longs, below for shorts).
Exit/Trail: Trailing stop at the 20-bar Donchian channel on the opposite side (no fixed TP).
Risk: Initial stop = ATR(N) × stopMult (ATR is smoothed). Position size risks riskPct% of equity based on stop distance.
Labels: “BUY/SELL” only on the entry bar; “STOP BUY/STOP SELL” only on the exit bar.
Pyramiding: Off (one position at a time).
Regime Alignment with EMAs (recommended filter, not enforced by code)
Add EMA 50 and EMA 200 to the D1 chart.
Long bias: take BUY signals only when EMA50 > EMA200 (bullish regime).
Short bias: take SELL signals only when EMA50 < EMA200 (bearish regime).
Optional: for extra selectivity, require the H4 EMAs (50/200) to align with D1 before acting on a signal.
Inputs
entryN (55), exitN (20), atrLen (20), atrSmooth (10), stopMult (2.0), riskPct (0.5%–1.0% recommended).
Works well on (tested by user)
BTCUSD (Bitcoin), EURUSD, GBPJPY, NAS100/US100, USDJPY, AUDUSD, XAGUSD (Silver), US30 (Dow), JP225 (Nikkei), EURGBP, NZDUSD, EURCHF, USDCHF.
How to use
Apply to D1 charts. Review once per day after the daily close and execute next session open to mirror backtest assumptions. Best used as a portfolio strategy across multiple uncorrelated markets. Use the EMA alignment above as a discretionary regime filter to reduce false breakouts.
Notes
For educational use. Markets involve risk; past performance does not guarantee future results. Use responsible position sizing.
Multi - Timeframe 3 EMA Bull/Bear Table此指标是一个图标指标，适用于短线交易以及中线交易，它明确的显示出来了用EMA来表示方向指示，1分钟不可使用，此图表更新了多次以及修改了多次，在实际回测中有明显的提醒作用，不过多用于参考，不可作为主要指标使用，代码稍复杂如有加以改进的地方请提出，其中核心使用了EMA的20，50，200周期来作为参考，目的是能识别多周期和时间的方向指示，注意：此指标建议仅用于方向参考，不用于主要指标交易。
This indicator is a graphical indicator suitable for short-term and medium-term trading. It clearly shows the direction indicated by the EMA. It cannot be used for 1-minute intervals. This chart has been updated and modified multiple times, and it has a significant alerting effect in actual backtesting; however, it is mainly for reference and should not be used as the primary indicator. The code is somewhat complex, so please suggest improvements if there are any. The core uses the 20, 50, and 200 EMA periods as references, with the aim of identifying the direction indicators across multiple periods and timeframes. Note: This indicator is recommended only for directional reference and not for main indicator trading.
Dip Hunter [BackQuant]Dip Hunter
What this tool does in plain language
Dip Hunter is a pullback detector designed to find high quality buy-the-dip opportunities inside healthy trends and to avoid random knife catches. It watches for a quick drop from a recent high, checks that the drop happened with meaningful participation and volatility, verifies short-term weakness inside a larger uptrend, then scores the setup and paints the chart so you can act with confidence. It also draws clean entry lines, provides a meter that shows dip strength at a glance, and ships with alerts that match common execution workflows.
How Dip Hunter thinks
It defines a recent swing reference, measures how far price has dipped off that high, and only looks at candidates that meet your minimum percentage drop.
It confirms the dip with real activity by requiring a volume spike and a volatility spike.
It checks structure with two EMAs. Price should be weak in the short term while the larger context remains constructive.
It optionally requires a higher-timeframe trend to be up so you focus on pullbacks in trending markets.
It bundles those checks into a score and shows you the score on the candles and on a gradient meter.
When everything lines up it paints a green triangle below the bar, shades the background, and (if you wish) draws a horizontal entry line at your chosen level.
Inputs and what they mean
Dip Hunter Settings
• Vol Lookback and Vol Spike : The script computes an average volume over the lookback window and flags a spike when current volume is a multiple of that average. A multiplier of 2.0 means today’s volume must be at least double the average. This helps filter noise and focuses on dips that other traders actually traded.
• Fast EMA and Slow EMA : Short-term and medium-term structure references. A dip is more credible if price closes below the fast EMA while the fast EMA is still below the slow EMA during the pullback. That is classic corrective behavior inside a larger trend.
• Price Smooth : Optional smoothing length for price-derived series. Use this if you trade very noisy assets or low timeframes.
• Volatility Len and Vol Spike (volatility) : The script checks both standard deviation and true range against their own averages. If either expands beyond your multiplier the market confirms the move with range.
• Dip % and Lookback Bars : The engine finds the highest high over the lookback window, then computes the percentage drawdown from that high to the current close. Only dips larger than your threshold qualify.
Trend Filter
• Enable Trend Filter : When on, Dip Hunter will only trigger if the market is in an uptrend.
• Trend EMA Period : The longer EMA that defines the session’s backbone trend.
• Minimum Trend Strength : A small positive slope requirement. In practice this means the trend EMA should be rising, and price should be above it. You can raise the value to be more selective.
Entries
• Show Entry Lines : Draws a horizontal guide from the signal bar for a fixed number of bars. Great for limit orders, scaling, or re-tests.
• Line Length (bars) : How far the entry guide extends.
• Min Gap (bars) : Suppresses new entry lines if another dip fired recently. Prevents clutter during choppy sequences.
• Entry Price : Choose the line level. “Low” anchors at the signal candle’s low. “Close” anchors at the signal close. “Dip % Level” anchors at the theoretical level defined by recent_high × (1 − dip%). This lets you work resting orders at a consistent discount.
Heat / Meter
• Color Bars by Score : Colors each candle using a red→white→green gradient. Red is overheated, green is prime dip territory, white is neutral.
• Show Meter Table : Adds a compact gradient strip with a pointer that tracks the current score.
• Meter Cells and Meter Position : Resolution and placement of the meter.
UI Settings
• Show Dip Signals : Plots green triangles under qualifying bars and tints the background very lightly.
• Show EMAs : Plots fast, slow, and the trend EMA (if the trend filter is enabled).
• Bullish, Bearish, Neutral colors : Theme controls for shapes, fills, and bar painting.
Core calculations explained simply
Recent high and dip percent
The script finds the highest high over Lookback Bars , calls it “recent high,” then calculates:
dip% = (recent_high − close) ÷ recent_high × 100.
If dip% is larger than Dip % , condition one passes.
Volume confirmation
It computes a simple moving average of volume over Vol Lookback . If current volume ÷ average volume > Vol Spike , we have a participation spike. It also checks 5-bar ROC of volume. If ROC > 50 the spike is forceful. This gets an extra score point.
Volatility confirmation
Two independent checks:
• Standard deviation of closes vs its own average.
• True range vs ATR.
If either expands beyond Vol Spike (volatility) the move has range. This prevents false triggers from quiet drifts.
Short-term structure
Price should close below the Fast EMA and the fast EMA should be below the Slow EMA at the moment of the dip. That is the anatomy of a pullback rather than a full breakdown.
Macro trend context (optional)
When Enable Trend Filter is on, the Trend EMA must be rising and price must be above it. The logic prefers “micro weakness inside macro strength” which is the highest probability pattern for buying dips.
Signal formation
A valid dip requires:
• dip% > threshold
• volume spike true
• volatility spike true
• close below fast EMA
• fast EMA below slow EMA
If the trend filter is enabled, a rising trend EMA with price above it is also required. When all true, the triangle prints, the background tints, and optional entry lines are drawn.
Scoring and visuals
Binary checks into a continuous score
Each component contributes to a score between 0 and 1. The script then rescales to a centered range (−50 to +50).
• Low or negative scores imply “overheated” conditions and are shaded toward red.
• High positive scores imply “ripe for a dip buy” conditions and are shaded toward green.
• The gradient meter repeats the same logic, with a pointer so you can read the state quickly.
Bar coloring
If you enable “Color Bars by Score,” each candle inherits the gradient. This makes sequences obvious. Red clusters warn you not to buy. White means neutral. Increasing green suggests the pullback is maturing.
EMAs and the trend EMA
• Fast EMA turns down relative to the slow EMA inside the pullback.
• Trend EMA stays rising and above price once the dip exhausts, which is your cue to focus on long setups rather than bottom fishing in downtrends.
Entry lines
When a fresh signal fires and no other signal happened within Min Gap (bars) , the indicator draws a horizontal level for Line Length bars. Use these lines for limit entries at the low, at the close, or at the defined dip-percent level. This keeps your plan consistent across instruments.
Alerts and what they mean
• Market Overheated : Score is deeply negative. Do not chase. Wait for green.
• Close To A Dip : Score has reached a healthy level but the full signal did not trigger yet. Prepare orders.
• Dip Confirmed : First bar of a fresh validated dip. This is the most direct entry alert.
• Dip Active : The dip condition remains valid. You can scale in on re-tests.
• Dip Fading : Score crosses below 0.5 from above. Momentum of the setup is fading. Tighten stops or take partials.
• Trend Blocked Signal : All dip conditions passed but the trend filter is offside. Either reduce risk or skip, depending on your plan.
How to trade with Dip Hunter
Classic pullback in uptrend
Turn on the trend filter.
Watch for a Dip Confirmed alert with green triangle.
Use the entry line at “Dip % Level” to stage a limit order. This keeps your entries consistent across assets and timeframes.
Initial stop under the signal bar’s low or under the next lower EMA band.
First target at prior swing high, second target at a multiple of risk.
If you use partials, trail the remainder under the fast EMA once price reclaims it.
Aggressive intraday scalps
Lower Dip % and Lookback Bars so you catch shallow flags.
Keep Vol Spike meaningful so you only trade when participation appears.
Take quick partials when price reclaims the fast EMA, then exit on Dip Fading if momentum stalls.
Counter-trend probes
Disable the trend filter if you intentionally hunt reflex bounces in downtrends.
Require strong volume and volatility confirmation.
Use smaller size and faster targets. The meter should move quickly from red toward white and then green. If it does not, step aside.
Risk management templates
Stops
• Conservative: below the entry line minus a small buffer or below the signal bar’s low.
• Structural: below the slow EMA if you aim for swing continuation.
• Time stop: if price does not reclaim the fast EMA within N bars, exit.
Position sizing
Use the distance between the entry line and your structural stop to size consistently. The script’s entry lines make this distance obvious.
Scaling
• Scale at the entry line first touch.
• Add only if the meter stays green and price reclaims the fast EMA.
• Stop adding on a Dip Fading alert.
Tuning guide by market and timeframe
Equities daily
• Dip %: 1.5 to 3.0
• Lookback Bars: 5 to 10
• Vol Spike: 1.5 to 2.5
• Volatility Len: 14 to 20
• Trend EMA: 100 or 200
• Keep trend filter on for a cleaner list.
Futures and FX intraday
• Dip %: 0.4 to 1.2
• Lookback Bars: 3 to 7
• Vol Spike: 1.8 to 3.0
• Volatility Len: 10 to 14
• Use Min Gap to avoid clusters during news.
Crypto
• Dip %: 3.0 to 6.0 for majors on higher timeframes, lower on 15m to 1h
• Lookback Bars: 5 to 12
• Vol Spike: 1.8 to 3.0
• ATR and stdev checks help in erratic sessions.
Reading the chart at a glance
• Green triangle below the bar: a validated dip.
• Light green background: the current bar meets the full condition.
• Bar gradient: red is overheated, white is neutral, green is dip-friendly.
• EMAs: fast below slow during the pullback, then reclaim fast EMA on the bounce for quality continuation.
• Trend EMA: a rising spine when the filter is on.
• Entry line: a fixed level to anchor orders and risk.
• Meter pointer: right side toward “Dip” means conditions are maturing.
Why this combination reduces false positives
Any single criterion will trigger too often. Dip Hunter demands a dip off a recent high plus a volume surge plus a volatility expansion plus corrective EMA structure. Optional trend alignment pushes odds further in your favor. The score and meter visualize how many of these boxes you are actually ticking, which is more reliable than a binary dot.
Limitations and practical tips
• Thin or illiquid symbols can spoof volume spikes. Use larger Vol Lookback or raise Vol Spike .
• Sideways markets will show frequent small dips. Increase Dip % or keep the trend filter on.
• News candles can blow through entry lines. Widen stops or skip around known events.
• If you see many back-to-back triangles, raise Min Gap to keep only the best setups.
Quick setup recipes
• Clean swing trader: Trend filter on, Dip % 2.0 to 3.0, Vol Spike 2.0, Volatility Len 14, Fast 20 EMA, Slow 50 EMA, Trend 100 EMA.
• Fast intraday scalper: Trend filter off, Dip % 0.7 to 1.0, Vol Spike 2.5, Volatility Len 10, Fast 9 EMA, Slow 21 EMA, Min Gap 10 bars.
• Crypto swing: Trend filter on, Dip % 4.0, Vol Spike 2.0, Volatility Len 14, Fast 20 EMA, Slow 50 EMA, Trend 200 EMA.
Summary
Dip Hunter is a focused pullback engine. It quantifies a real dip off a recent high, validates it with volume and volatility expansion, enforces corrective structure with EMAs, and optionally restricts signals to an uptrend. The score, bar gradient, and meter make reading conditions instant. Entry lines and alerts turn that read into an executable plan. Tune the thresholds to your market and timeframe, then let the tool keep you patient in red, selective in white, and decisive in green.
Linh's Anomaly Radar v2What this script does
It’s an event detector for price/volume anomalies that often precede or confirm moves.
It watches a bunch of patterns (Wyckoff tests, squeezes, failed breakouts, turnover bursts, etc.), applies robust z-scores, optional trend filters, cooldowns (to avoid spam), and then fires:
A shape/label on the bar,
A row in the mini panel (top-right),
A ready-made alertcondition you can hook into.
How to add & set up (TradingView)
Paste the script → Save → Add to chart on Daily first (works on any TF).
Open Settings → Inputs:
General
• Use Robust Z (MAD): more outlier-resistant; keep on.
• Z Lookback: 60 bars is ~3 months; bump to 120 for slower regimes.
• Cooldown: min bars to wait before the same signal can fire again (default 5).
• Use trend filter: if on, “bullish” signals only fire above SMA(tfLen), “bearish” below.
Thresholds: fine-tune sensitivity (defaults are sane).
To create alerts: Right-click chart → Add alert
Condition: Linh’s Anomaly Radar v2 → choose a specific signal or Composite (Σ).
Options: “Once per bar close” (recommended).
Customize message if you want ticker/timeframe in your phone push.
The mini panel (top-right)
Signal column: short code (see cheat sheet below).
Fired column: a dot “•” means that on the latest bar this signal fired.
Score (right column): total count of signals that fired this bar.
Σ≥N shows your composite threshold (how many must fire to trigger the “Composite” alert).
Shapes & codes (what’s what)
Code Name (category) What it’s looking for Why it matters
STL Stealth Volume z(volume)>5 & ** z(return)
EVR Effort vs Result squeeze z(vol)>3 & z(TR)<−0.5 Heavy effort, tiny spread → absorption
TGV Tight+Heavy (HL/ATR)<0.6 & z(vol)>3 Tight bar + heavy tape → pro activity
CLS Accumulation cluster ≥3 of last 5 bars: up, vol↑, close near high Classic accumulation footprint
GAP Open drive failure Big gap not filled (≥80%) & vol↑ One-sided open stalls → fade risk
BB↑ BB squeeze breakout Squeeze (z(BBWidth)<−1.3) → close > upperBB & vol↑ Regime shift with confirmation
ER↑ Effort→Result inversion Down day on vol then next bar > prior high Demand overwhelms supply
OBV OBV divergence OBV slope up & ** z(ret20)
WER Wide Effort, Opposite Result z(vol)>3, close+1 Selling into strength / distribution
NS No-Supply (Wyckoff) Down bar, HL<0.6·ATR, vol << avg Sellers absent into weakness
ND No-Demand (Wyckoff) Up bar, HL<0.6·ATR, vol << avg Buyers absent into strength
VAC Liquidity Vacuum z(vol)<−1.5 & ** z(ret)
UTD UTAD (failed breakout) Breaks swing-high, closes back below, vol↑ Stop-run, reversal risk
SPR Spring (failed breakdown) Breaks swing-low, closes back above, vol↑ Bear trap, reversal risk
PIV Pocket Pivot Up bar; vol > max down-vol in lookback Quiet base → sudden demand
NR7 Narrow Range 7 + Vol HL is 7-bar low & z(vol)>2 Coiled spring with participation
52W 52-wk breakout quality New 52-wk close high + squeeze + vol↑ High-quality breakouts
VvK Vol-of-Vol kink z(ATR20,200)>0.5 & z(ATR5,60)<0 Long-vol wakes up, short-vol compresses
TAC Turnover acceleration SMA3 vol / SMA20 vol > 1.8 & muted return Participation surging before move
RBd RSI Bullish div Price LL, RSI HL, vol z>1 Exhaustion of sellers
RS↑ RSI Bearish div Price HH, RSI LH, vol z>1 Exhaustion of buyers
Σ Composite Count of all fired signals ≥ threshold High-conviction bar
Placement:
Triangles up (below bar) → bullish-leaning events.
Triangles down (above bar) → bearish-leaning events.
Circles → neutral context (VAC, VvK, Composite).
Key inputs (quick reference)
General
Use Robust Z (MAD): keep on for noisy tickers.
Z Lookback (lenZ): 60 default; 120 if you want fewer alerts.
Trend filter: when on, bullish signals require close > SMA(tfLen), bearish require <.
Cooldown: prevents repeated firing of the same signal within N bars.
Phase-1 thresholds (core)
Stealth: vol z > 5, |ret z| < 1.
EVR: vol z > 3, TR z < −0.5.
Tight+Heavy: (HL/ATR) < 0.6, vol z > 3.
Cluster: window=5, min=3 strong bars.
GapFail: gap/ATR ≥1.5, fill <80%, vol z > 2.
BB Squeeze: z(BBWidth)<−1.3 then breakout with vol z > 2.
Eff→Res Up: prev bar heavy down → current bar > prior high.
OBV Div: OBV uptrend + |z(ret20)|<0.3.
Phase-2 thresholds (extras)
WER: vol z > 3, close1.
No-Supply/No-Demand: tight bar & very light volume vs SMA20.
Vacuum: vol z < −1.5, |ret z|>1.5.
UTAD/Spring: swing lookback N (default 20), vol z > 2.
Pocket Pivot: lookback for prior down-vol max (default 10).
NR7: 7-bar narrowest range + vol z > 2.
52W Quality: new 52-wk high + squeeze + vol z > 2.
VoV Kink: z(ATR20,200)>0.5 AND z(ATR5,60)<0.
Turnover Accel: SMA3/SMA20 > 1.8 and |ret z|<1.
RSI Divergences: compare to n bars back (default 14).
How to use it (playbooks)
A) Daily scan workflow
Run on Daily for your VN watchlist.
Turn Composite (Σ) alert on with Σ≥2 or ≥3 to reduce noise.
When a bar fires Σ (or a fav combo like STL + BB↑), drop to 60-min to time entries.
B) Breakout quality check
Look for 52W together with BB↑, TAC, and OBV.
If WER/ND appear near highs → downgrade the breakout.
C) Spring/UTAD reversals
If SPR fires near major support and RBd confirms → long bias with stop below spring low.
If UTD + WER/RS↑ near resistance → short/fade with stop above UTAD high.
D) Accumulation basing
During bases, you want CLS, OBV, TGV, STL, NR7.
A pocket pivot (PIV) can be your early add; manage risk below base lows.
Tuning tips
Too many signals? Raise stealthVolZ to 5.5–6, evrVolZ to 3.5, use Σ≥3.
Fast movers? Lower bbwZthr to −1.0 (less strict squeeze), keep trend filter on.
Illiquid tickers? Keep MAD z-scores on, increase lookbacks (e.g., lenZ=120).
Limitations & good habits
First lenZ bars on a new symbol are less reliable (incomplete z-window).
Some ideas (VWAP magnet, close auction spikes, ETF/foreign flows, options skew) need intraday/external feeds — not included here.
Pine can’t “screen” across the whole market; set alerts or cycle your watchlist.
Quick troubleshooting
Compilation errors: make sure you’re on Pine v6; don’t nest functions in if blocks; each var int must be declared on its own line.
No shapes firing: check trend filter (maybe price is below SMA and you’re waiting for bullish signals), and verify thresholds aren’t too strict.
XAUUSD 1H – FVG Buy/Sell Signals XAUUSD 1H – Fair Value Gap (FVG) Buy/Sell Signals (No Boxes)
What it is:
A clean, signal-only indicator for Gold on the 1-hour chart. It detects 3-bar Fair Value Gaps, waits for a deep retest, then confirms with strong candle structure + trend + ADX before printing a BUY/SELL arrow. No rectangles or clutter—just selective, high-quality signals.
Why it works:
Instead of chasing breakouts, the script hunts for imbalances (FVGs) where price often returns to “fair value.” It only fires when:
price revisits the gap by a configurable depth,
the candle closes beyond the far edge with a small buffer,
the candle body is ≥ ATR × K (confirms intent),
the broader trend (EMA-50/EMA-200) agrees, and
ADX (Wilder, manual) shows sufficient strength.
Key features
✅ Signal-only: arrows/labels—no boxes on chart.
✅ Deep retest logic (percentage of zone), not just a touch.
✅ Strong close filter (edge + buffer) + ATR body filter.
✅ Trend filter (EMA-50 vs EMA-200) to keep trades with the regime.
✅ ADX strength to avoid chop.
✅ One signal per zone (optional “delete on use”).
✅ Alerts for both BUY and SELL.
✅ Built for Pine v6, non-repainting logic on bar close.
Inputs you can tune
Min FVG size (pts) – ignore tiny gaps.
Retest depth (%) – how deep price must come back into the gap.
Close buffer (pts) – extra confirmation beyond zone edge.
Min body ≥ ATR× – candle strength requirement.
Min ADX – trend strength threshold.
Expire after X bars – keep zones fresh.
Delete zone after signal – true = one-shot signals.
How I use it
Apply to XAUUSD 1H.
Keep default filters for selective signals.
For more setups, lower Min FVG size or ADX and reduce retest depth; for stricter signals, do the opposite.
Combine with S/R or session timing (London/NY) for added confluence.
Notes
Signals are generated on bar close.
Designed for clarity and discipline—fewer, cleaner arrows over constant noise.
Works on other symbols/timeframes, but tuned for Gold 1H.
Tags: #XAUUSD #Gold #FVG #SmartMoney #1H #TrendFollowing #ADX #ATR #PineV6 #TradingView
Linh Index Trend & Exhaustion SuitePurpose: One overlay to judge trend, reversal risk, overextension, and volatility squeezes on indexes (built for VNINDEX/VN30, works on any symbol & timeframe).
What it shows
Trend state: Bull / Bear / Transition via 20/50/200 EMAs + slope check.
Overextension heatmap: Background paints when price is stretched vs the 20-EMA by ATR or % (you set the thresholds).
Squeeze detection:
Squeeze ON (yellow dot): Bollinger Bands (20,2) inside Keltner Channels (20,1.5).
Squeeze OFF + Release: White dot; script confirms direction only when close > BB upper (up) or close < BB lower (down).
52-week context: Distance to 52-week high/low (%).
Higher-TF alignment: Optional weekly trend reading shown on the label while you’re on the daily.
Anchored VWAP(s): Two optional AVWAPs from dates you choose (e.g., YTD open, last big gap/earnings).
Plots & labels
EMAs 20/50/200 (toggle on/off).
Optional BB & KC bands for diagnostics.
AVWAP #1 / #2 (optional).
Status label with: Trend, EMAs, Dist to 20-EMA (%, ATR), 52-week distances, HTF state.
Built-in alerts (set “Once per bar close”)
EMA10 ↔ EMA20 cross (early momentum shift)
EMA20 ↔ EMA50 cross (trend confirmation/negation)
Price ↔ EMA200 cross (long-term regime)
Squeeze Release UP / DOWN (BB breakout after squeeze)
Overextension Cool-off UP / DN (stretched vs 20-EMA + momentum rolling)
Near 52-week High (within your % threshold)
How to use (playbook)
Map regime: Prefer trades when Daily = Bull and HTF (Weekly) = Bull (shown on label).
Hunt expansion: Yellow → White dot and close beyond BB = fresh move.
Avoid chasing stretch: If background is painted (overextended vs 20-EMA), wait for a pullback or intraday base.
Locations matter: 52-week proximity + HTF Bull improves breakout quality.
Anchors: Add AVWAP from YTD open or last major gap to frame support/resistance.
Suggested settings
Overextension: ATR = 2.0, % = 4.0 to start; tune per index volatility.
Squeeze bands: BB(20,2) & KC(20,1.5) default are balanced; tighten KC (1.3) for more signals, widen (1.8) for fewer/higher quality.
Timeframes: Daily for signals, Weekly for bias. Optional 65-min for entries.
Multi-EMAMulti-EMA Indicator
This script plots five commonly used Exponential Moving Averages (EMAs) on your chart for trend identification and trade timing.
Included EMAs & Colors:
EMA 8 — Red
EMA 20 — Orange
EMA 50 — Yellow
EMA 100 — Cyan
EMA 200 — Blue
How to use:
Shorter EMAs (8 & 20) help identify short-term momentum and potential entry/exit points.
Mid-range EMA (50) gives a broader view of intermediate trends.
Longer EMAs (100 & 200) are used to confirm long-term trend direction and key support/resistance zones.
Crossovers between EMAs can signal potential trend changes.
Price trading above most EMAs often signals bullish conditions, while trading below suggests bearish momentum.
Designed to work on any timeframe or market.
Painel Técnico (4H x 1D) — Clean UI + Alertas BrenoG📋 Main Functions
1️⃣ Analysis in two fixed timeframes
4 hours and 1 day analyzed in parallel.
Each column in the table displays the data for its respective timeframe.
2️⃣ Entry point based on oversold conditions
The “entry point” is not the current price, but rather the last candle that went into oversold territory (RSI ≤ configured threshold).
If there has been no recent oversold condition, the current price is used as a fallback.
All calculations (Buy Zone, Stops, TPs) are based on this point.
3️⃣ Buy Zone
Defined as:
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Low Zone = entry * (1 - width%)
High Zone = entry
Always visible in the table, but alerts can be set to trigger only if RSI is oversold at the moment of entry.
4️⃣ Automatic Stops
Moderate Stop and Conservative Stop, calculated as a % below the entry point.
Displayed in the table with black text on a gray background for emphasis.
Alerts trigger when price crosses below these levels.
5️⃣ Take Profits (TP1–TP4)
Calculated from the entry point:
By percentage (usePercentTP = true) or
By fixed prices (usePercentTP = false).
The table displays:
Target price
% gain over the entry point
They only appear when RSI > 50 and EMA50 > EMA200 (the “alignment” condition).
Alerts trigger only on breakouts upward.
6️⃣ Context Indicators
RSI → shows numeric value and green/red color.
MACD → indicates if the MACD line is above or below the signal line.
EMAs 50/200 → indicates “Golden Cross” or “Death Cross”.
Price vs EMA200 → dedicated row showing “Above” or “Below EMA 200” with green/red color.
7️⃣ Visual Panel
Semi–transparent dark gray background, thin borders.
Colored header:
Blue for 4H
Orange for 1D
Rows separated by data type for easy reading.
Configurable font size (tiny to large).
Table position configurable (top_left, top_right, etc.).
8️⃣ Integrated Alerts
Entry/Exit of Buy Zone
Touch of each TP
Touch of each Stop
RSI entering Oversold
All alerts are separated by timeframe with clear, fixed messages.
📌 Simple Summary:
It’s an intelligent panel that combines multi–timeframe technical analysis, automatic calculation of entries/stops/TPs based on oversold conditions, and ready–to–use alerts — all presented in a visual, compact, and fully configurable format.
Multi-Length Quad Bollinger BandsHere is a Pine Script code for TradingView that plots four separate Bollinger Bands on your chart. The lengths are preset to 14, 50, 100, and 200, but every aspect—including lengths, standard deviations, colors, and the source price—is fully customizable through the script's settings menu.
The 14 and 50-period bands are enabled by default, while the 100 and 200-period bands are disabled to keep the chart clean initially. You can easily toggle any of them on or off.
Information Theory Market AnalysisINFORMATION THEORY MARKET ANALYSIS
OVERVIEW
This indicator applies mathematical concepts from information theory to analyze market behavior, measuring the randomness and predictability of price and volume movements through entropy calculations. Unlike traditional technical indicators, it provides insight into market structure and regime changes.
KEY COMPONENTS
Four Main Signals:
• Price Entropy (Deep Blue): Measures randomness in price movements
• Volume Entropy (Bright Blue): Analyzes volume pattern predictability
• Entropy MACD (Purple): Shows relationship between price and volume entropy
• SEMM (Royal Blue): Stochastic Entropy Market Monitor - overall market randomness gauge
Market State Detection:
The indicator identifies seven distinct market states:
• Strong Trending (SEMM < 0.1)
• Weak Trending (0.1-0.2)
• Neutral (0.2-0.3)
• Moderate Random (0.3-0.5)
• High Randomness (0.5-0.8)
• Very Random (0.8-1.0)
• Chaotic (>1.0)
KEY FEATURES
Advanced Analytics:
• Signal Strength Confluence: 0-5 scale measuring alignment of multiple factors
• Entropy Crossovers: Detects shifts between accumulation and distribution phases
• Extreme Readings: Identifies statistical outliers for potential reversals
• Trend Bias Analysis: Directional momentum assessment
Information Dashboard:
• Real-time entropy values and market state
• Signal strength indicator with visual highlighting
• Trend bias with directional arrows
• Color-coded alerts for extreme conditions
Customizable Display:
• Adjustable SEMM scaling (5x to 100x) for optimal visibility
• Multiple line styles: Smooth, Stepped, Dotted
• 9 table positions with 3 size options
• Professional blue color scheme with transparency controls
Comprehensive Alert System - 15 Alert Types Including:
• Extreme entropy readings (price/volume)
• Crossover signals (dominance shifts)
• Market state changes (trending ↔ random)
• High confluence signals (3+ factors aligned)
HOW TO USE
Reading the Signals:
• Entropy Values > ±25: Strong structural signals
• Entropy Values > ±40: Extreme readings, potential reversals
• SEMM < 0.2: Trending market favors directional strategies
• SEMM > 0.5: Random market favors range/scalping strategies
Signal Confluence:
Look for multiple factors aligning:
• Signal Strength ≥ 3.0 for higher probability setups
• Background highlighting indicates confluence
• Table shows real-time strength assessment
Timeframe Optimization:
• Short-term (1m-15m): Entropy Length 14-22, Sensitivity 3-5
• Swing Trading (1H-4H): Default settings optimal
• Position Trading (Daily+): Entropy Length 34-55, Sensitivity 8-12
EDUCATIONAL APPLICATIONS
Market Structure Analysis:
• Understand when markets are trending vs. ranging
• Identify accumulation and distribution phases
• Recognize extreme market conditions
• Measure information content in price movements
Information Theory Concepts:
• Binary entropy calculations applied to financial data
• Probability distribution analysis of returns
• Statistical ranking and percentile analysis
• Momentum-adjusted randomness measurement
TECHNICAL DETAILS
Calculations:
• Uses binary entropy formula: -
• Percentile ranking across multiple timeframes
• Volume-weighted probability distributions
• RSI-adjusted momentum entropy (SEMM)
Customization Options:
• Entropy Length: 5-100 bars (default: 22)
• Average Length: 10-200 bars (default: 88)
• Sensitivity: 1.0-20.0 (default: 5.0, lower = more sensitive)
• SEMM Scaling: 5.0-100.0x (default: 30.0)
IMPORTANT NOTES
Risk Considerations:
• Indicator measures probabilities, not certainties
• High SEMM values (>0.5) suggest increased market randomness
• Extreme readings may persist longer than expected
• Always combine with proper risk management
Educational Purpose:
This indicator is designed for:
• Market structure analysis and education
• Understanding information theory applications in finance
• Developing probabilistic thinking about markets
• Research and analytical purposes
Performance Tips:
• Allow 200+ bars for proper initialization
• Adjust scaling and transparency for optimal visibility
• Use confluence signals for higher probability analysis
• Consider multiple timeframes for comprehensive analysis
DISCLAIMER
This indicator is for educational and analytical purposes. It does not constitute financial advice. Past performance does not guarantee future results. Always conduct your own research and consider your risk tolerance before making trading decisions.
Version: 5.0
Category: Oscillators, Volume, Market Structure
Best For: All timeframes, trending and ranging markets
Complexity: Intermediate to Advanced
KRMJ MAoverlays four moving averages on a TradingView chart to help traders identify trend direction, momentum shifts, and dynamic support or resistance levels. It includes a 9-period EMA, 21-period EMA, 20-period SMA, and a 200-period EMA. Each moving average serves a specific role: the 9 EMA responds quickly to price changes and highlights short-term momentum; the 21 EMA smooths out price action slightly more and confirms near-term trends. The 20 SMA provides a simple mid-range trend baseline often used in mean-reversion strategies or range-bound environments. The 200 EMA, a widely recognized long-term trend filter, helps users gauge the dominant market direction.
Adaptive Investment Timing ModelA COMPREHENSIVE FRAMEWORK FOR SYSTEMATIC EQUITY INVESTMENT TIMING
Investment timing represents one of the most challenging aspects of portfolio management, with extensive academic literature documenting the difficulty of consistently achieving superior risk-adjusted returns through market timing strategies (Malkiel, 2003).
Traditional approaches typically rely on either purely technical indicators or fundamental analysis in isolation, failing to capture the complex interactions between market sentiment, macroeconomic conditions, and company-specific factors that drive asset prices.
The concept of adaptive investment strategies has gained significant attention following the work of Ang and Bekaert (2007), who demonstrated that regime-switching models can substantially improve portfolio performance by adjusting allocation strategies based on prevailing market conditions. Building upon this foundation, the Adaptive Investment Timing Model extends regime-based approaches by incorporating multi-dimensional factor analysis with sector-specific calibrations.
Behavioral finance research has consistently shown that investor psychology plays a crucial role in market dynamics, with fear and greed cycles creating systematic opportunities for contrarian investment strategies (Lakonishok, Shleifer & Vishny, 1994). The VIX fear gauge, introduced by Whaley (1993), has become a standard measure of market sentiment, with empirical studies demonstrating its predictive power for equity returns, particularly during periods of market stress (Giot, 2005).
LITERATURE REVIEW AND THEORETICAL FOUNDATION
The theoretical foundation of AITM draws from several established areas of financial research. Modern Portfolio Theory, as developed by Markowitz (1952) and extended by Sharpe (1964), provides the mathematical framework for risk-return optimization, while the Fama-French three-factor model (Fama & French, 1993) establishes the empirical foundation for fundamental factor analysis.
Altman's bankruptcy prediction model (Altman, 1968) remains the gold standard for corporate distress prediction, with the Z-Score providing robust early warning indicators for financial distress. Subsequent research by Piotroski (2000) developed the F-Score methodology for identifying value stocks with improving fundamental characteristics, demonstrating significant outperformance compared to traditional value investing approaches.
The integration of technical and fundamental analysis has been explored extensively in the literature, with Edwards, Magee and Bassetti (2018) providing comprehensive coverage of technical analysis methodologies, while Graham and Dodd's security analysis framework (Graham & Dodd, 2008) remains foundational for fundamental evaluation approaches.
Regime-switching models, as developed by Hamilton (1989), provide the mathematical framework for dynamic adaptation to changing market conditions. Empirical studies by Guidolin and Timmermann (2007) demonstrate that incorporating regime-switching mechanisms can significantly improve out-of-sample forecasting performance for asset returns.
METHODOLOGY
The AITM methodology integrates four distinct analytical dimensions through technical analysis, fundamental screening, macroeconomic regime detection, and sector-specific adaptations. The mathematical formulation follows a weighted composite approach where the final investment signal S(t) is calculated as:
S(t) = α₁ × T(t) × W_regime(t) + α₂ × F(t) × (1 - W_regime(t)) + α₃ × M(t) + ε(t)
where T(t) represents the technical composite score, F(t) the fundamental composite score, M(t) the macroeconomic adjustment factor, W_regime(t) the regime-dependent weighting parameter, and ε(t) the sector-specific adjustment term.
Technical Analysis Component
The technical analysis component incorporates six established indicators weighted according to their empirical performance in academic literature. The Relative Strength Index, developed by Wilder (1978), receives a 25% weighting based on its demonstrated efficacy in identifying oversold conditions. Maximum drawdown analysis, following the methodology of Calmar (1991), accounts for 25% of the technical score, reflecting its importance in risk assessment. Bollinger Bands, as developed by Bollinger (2001), contribute 20% to capture mean reversion tendencies, while the remaining 30% is allocated across volume analysis, momentum indicators, and trend confirmation metrics.
Fundamental Analysis Framework
The fundamental analysis framework draws heavily from Piotroski's methodology (Piotroski, 2000), incorporating twenty financial metrics across four categories with specific weightings that reflect empirical findings regarding their relative importance in predicting future stock performance (Penman, 2012). Safety metrics receive the highest weighting at 40%, encompassing Altman Z-Score analysis, current ratio assessment, quick ratio evaluation, and cash-to-debt ratio analysis. Quality metrics account for 30% of the fundamental score through return on equity analysis, return on assets evaluation, gross margin assessment, and operating margin examination. Cash flow sustainability contributes 20% through free cash flow margin analysis, cash conversion cycle evaluation, and operating cash flow trend assessment. Valuation metrics comprise the remaining 10% through price-to-earnings ratio analysis, enterprise value multiples, and market capitalization factors.
Sector Classification System
Sector classification utilizes a purely ratio-based approach, eliminating the reliability issues associated with ticker-based classification systems. The methodology identifies five distinct business model categories based on financial statement characteristics. Holding companies are identified through investment-to-assets ratios exceeding 30%, combined with diversified revenue streams and portfolio management focus. Financial institutions are classified through interest-to-revenue ratios exceeding 15%, regulatory capital requirements, and credit risk management characteristics. Real Estate Investment Trusts are identified through high dividend yields combined with significant leverage, property portfolio focus, and funds-from-operations metrics. Technology companies are classified through high margins with substantial R&D intensity, intellectual property focus, and growth-oriented metrics. Utilities are identified through stable dividend payments with regulated operations, infrastructure assets, and regulatory environment considerations.
Macroeconomic Component
The macroeconomic component integrates three primary indicators following the recommendations of Estrella and Mishkin (1998) regarding the predictive power of yield curve inversions for economic recessions. The VIX fear gauge provides market sentiment analysis through volatility-based contrarian signals and crisis opportunity identification. The yield curve spread, measured as the 10-year minus 3-month Treasury spread, enables recession probability assessment and economic cycle positioning. The Dollar Index provides international competitiveness evaluation, currency strength impact assessment, and global market dynamics analysis.
Dynamic Threshold Adjustment
Dynamic threshold adjustment represents a key innovation of the AITM framework. Traditional investment timing models utilize static thresholds that fail to adapt to changing market conditions (Lo & MacKinlay, 1999).
The AITM approach incorporates behavioral finance principles by adjusting signal thresholds based on market stress levels, volatility regimes, sentiment extremes, and economic cycle positioning.
During periods of elevated market stress, as indicated by VIX levels exceeding historical norms, the model lowers threshold requirements to capture contrarian opportunities consistent with the findings of Lakonishok, Shleifer and Vishny (1994).
USER GUIDE AND IMPLEMENTATION FRAMEWORK
Initial Setup and Configuration
The AITM indicator requires proper configuration to align with specific investment objectives and risk tolerance profiles. Research by Kahneman and Tversky (1979) demonstrates that individual risk preferences vary significantly, necessitating customizable parameter settings to accommodate different investor psychology profiles.
Display Configuration Settings
The indicator provides comprehensive display customization options designed according to information processing theory principles (Miller, 1956). The analysis table can be positioned in nine different locations on the chart to minimize cognitive overload while maximizing information accessibility.
Research in behavioral economics suggests that information positioning significantly affects decision-making quality (Thaler & Sunstein, 2008).
Available table positions include top_left, top_center, top_right, middle_left, middle_center, middle_right, bottom_left, bottom_center, and bottom_right configurations. Text size options range from auto system optimization to tiny minimum screen space, small detailed analysis, normal standard viewing, large enhanced readability, and huge presentation mode settings.
Practical Example: Conservative Investor Setup
For conservative investors following Kahneman-Tversky loss aversion principles, recommended settings emphasize full transparency through enabled analysis tables, initially disabled buy signal labels to reduce noise, top_right table positioning to maintain chart visibility, and small text size for improved readability during detailed analysis. Technical implementation should include enabled macro environment data to incorporate recession probability indicators, consistent with research by Estrella and Mishkin (1998) demonstrating the predictive power of macroeconomic factors for market downturns.
Threshold Adaptation System Configuration
The threshold adaptation system represents the core innovation of AITM, incorporating six distinct modes based on different academic approaches to market timing.
Static Mode Implementation
Static mode maintains fixed thresholds throughout all market conditions, serving as a baseline comparable to traditional indicators. Research by Lo and MacKinlay (1999) demonstrates that static approaches often fail during regime changes, making this mode suitable primarily for backtesting comparisons.
Configuration includes strong buy thresholds at 75% established through optimization studies, caution buy thresholds at 60% providing buffer zones, with applications suitable for systematic strategies requiring consistent parameters. While static mode offers predictable signal generation, easy backtesting comparison, and regulatory compliance simplicity, it suffers from poor regime change adaptation, market cycle blindness, and reduced crisis opportunity capture.
Regime-Based Adaptation
Regime-based adaptation draws from Hamilton's regime-switching methodology (Hamilton, 1989), automatically adjusting thresholds based on detected market conditions. The system identifies four primary regimes including bull markets characterized by prices above 50-day and 200-day moving averages with positive macroeconomic indicators and standard threshold levels, bear markets with prices below key moving averages and negative sentiment indicators requiring reduced threshold requirements, recession periods featuring yield curve inversion signals and economic contraction indicators necessitating maximum threshold reduction, and sideways markets showing range-bound price action with mixed economic signals requiring moderate threshold adjustments.
Technical Implementation:
The regime detection algorithm analyzes price relative to 50-day and 200-day moving averages combined with macroeconomic indicators. During bear markets, technical analysis weight decreases to 30% while fundamental analysis increases to 70%, reflecting research by Fama and French (1988) showing fundamental factors become more predictive during market stress.
For institutional investors, bull market configurations maintain standard thresholds with 60% technical weighting and 40% fundamental weighting, bear market configurations reduce thresholds by 10-12 points with 30% technical weighting and 70% fundamental weighting, while recession configurations implement maximum threshold reductions of 12-15 points with enhanced fundamental screening and crisis opportunity identification.
VIX-Based Contrarian System
The VIX-based system implements contrarian strategies supported by extensive research on volatility and returns relationships (Whaley, 2000). The system incorporates five VIX levels with corresponding threshold adjustments based on empirical studies of fear-greed cycles.
Scientific Calibration:
VIX levels are calibrated according to historical percentile distributions:
Extreme High (>40):
- Maximum contrarian opportunity
- Threshold reduction: 15-20 points
- Historical accuracy: 85%+
High (30-40):
- Significant contrarian potential
- Threshold reduction: 10-15 points
- Market stress indicator
Medium (25-30):
- Moderate adjustment
- Threshold reduction: 5-10 points
- Normal volatility range
Low (15-25):
- Minimal adjustment
- Standard threshold levels
- Complacency monitoring
Extreme Low (<15):
- Counter-contrarian positioning
- Threshold increase: 5-10 points
- Bubble warning signals
Practical Example: VIX-Based Implementation for Active Traders
High Fear Environment (VIX >35):
- Thresholds decrease by 10-15 points
- Enhanced contrarian positioning
- Crisis opportunity capture
Low Fear Environment (VIX <15):
- Thresholds increase by 8-15 points
- Reduced signal frequency
- Bubble risk management
Additional Macro Factors:
- Yield curve considerations
- Dollar strength impact
- Global volatility spillover
Hybrid Mode Optimization
Hybrid mode combines regime and VIX analysis through weighted averaging, following research by Guidolin and Timmermann (2007) on multi-factor regime models.
Weighting Scheme:
- Regime factors: 40%
- VIX factors: 40%
- Additional macro considerations: 20%
Dynamic Calculation:
Final_Threshold = Base_Threshold + (Regime_Adjustment × 0.4) + (VIX_Adjustment × 0.4) + (Macro_Adjustment × 0.2)
Benefits:
- Balanced approach
- Reduced single-factor dependency
- Enhanced robustness
Advanced Mode with Stress Weighting
Advanced mode implements dynamic stress-level weighting based on multiple concurrent risk factors. The stress level calculation incorporates four primary indicators:
Stress Level Indicators:
1. Yield curve inversion (recession predictor)
2. Volatility spikes (market disruption)
3. Severe drawdowns (momentum breaks)
4. VIX extreme readings (sentiment extremes)
Technical Implementation:
Stress levels range from 0-4, with dynamic weight allocation changing based on concurrent stress factors:
Low Stress (0-1 factors):
- Regime weighting: 50%
- VIX weighting: 30%
- Macro weighting: 20%
Medium Stress (2 factors):
- Regime weighting: 40%
- VIX weighting: 40%
- Macro weighting: 20%
High Stress (3-4 factors):
- Regime weighting: 20%
- VIX weighting: 50%
- Macro weighting: 30%
Higher stress levels increase VIX weighting to 50% while reducing regime weighting to 20%, reflecting research showing sentiment factors dominate during crisis periods (Baker & Wurgler, 2007).
Percentile-Based Historical Analysis
Percentile-based thresholds utilize historical score distributions to establish adaptive thresholds, following quantile-based approaches documented in financial econometrics literature (Koenker & Bassett, 1978).
Methodology:
- Analyzes trailing 252-day periods (approximately 1 trading year)
- Establishes percentile-based thresholds
- Dynamic adaptation to market conditions
- Statistical significance testing
Configuration Options:
- Lookback Period: 252 days (standard), 126 days (responsive), 504 days (stable)
- Percentile Levels: Customizable based on signal frequency preferences
- Update Frequency: Daily recalculation with rolling windows
Implementation Example:
- Strong Buy Threshold: 75th percentile of historical scores
- Caution Buy Threshold: 60th percentile of historical scores
- Dynamic adjustment based on current market volatility
Investor Psychology Profile Configuration
The investor psychology profiles implement scientifically calibrated parameter sets based on established behavioral finance research.
Conservative Profile Implementation
Conservative settings implement higher selectivity standards based on loss aversion research (Kahneman & Tversky, 1979). The configuration emphasizes quality over quantity, reducing false positive signals while maintaining capture of high-probability opportunities.
Technical Calibration:
VIX Parameters:
- Extreme High Threshold: 32.0 (lower sensitivity to fear spikes)
- High Threshold: 28.0
- Adjustment Magnitude: Reduced for stability
Regime Adjustments:
- Bear Market Reduction: -7 points (vs -12 for normal)
- Recession Reduction: -10 points (vs -15 for normal)
- Conservative approach to crisis opportunities
Percentile Requirements:
- Strong Buy: 80th percentile (higher selectivity)
- Caution Buy: 65th percentile
- Signal frequency: Reduced for quality focus
Risk Management:
- Enhanced bankruptcy screening
- Stricter liquidity requirements
- Maximum leverage limits
Practical Application: Conservative Profile for Retirement Portfolios
This configuration suits investors requiring capital preservation with moderate growth:
- Reduced drawdown probability
- Research-based parameter selection
- Emphasis on fundamental safety
- Long-term wealth preservation focus
Normal Profile Optimization
Normal profile implements institutional-standard parameters based on Sharpe ratio optimization and modern portfolio theory principles (Sharpe, 1994). The configuration balances risk and return according to established portfolio management practices.
Calibration Parameters:
VIX Thresholds:
- Extreme High: 35.0 (institutional standard)
- High: 30.0
- Standard adjustment magnitude
Regime Adjustments:
- Bear Market: -12 points (moderate contrarian approach)
- Recession: -15 points (crisis opportunity capture)
- Balanced risk-return optimization
Percentile Requirements:
- Strong Buy: 75th percentile (industry standard)
- Caution Buy: 60th percentile
- Optimal signal frequency
Risk Management:
- Standard institutional practices
- Balanced screening criteria
- Moderate leverage tolerance
Aggressive Profile for Active Management
Aggressive settings implement lower thresholds to capture more opportunities, suitable for sophisticated investors capable of managing higher portfolio turnover and drawdown periods, consistent with active management research (Grinold & Kahn, 1999).
Technical Configuration:
VIX Parameters:
- Extreme High: 40.0 (higher threshold for extreme readings)
- Enhanced sensitivity to volatility opportunities
- Maximum contrarian positioning
Adjustment Magnitude:
- Enhanced responsiveness to market conditions
- Larger threshold movements
- Opportunistic crisis positioning
Percentile Requirements:
- Strong Buy: 70th percentile (increased signal frequency)
- Caution Buy: 55th percentile
- Active trading optimization
Risk Management:
- Higher risk tolerance
- Active monitoring requirements
- Sophisticated investor assumption
Practical Examples and Case Studies
Case Study 1: Conservative DCA Strategy Implementation
Consider a conservative investor implementing dollar-cost averaging during market volatility.
AITM Configuration:
- Threshold Mode: Hybrid
- Investor Profile: Conservative
- Sector Adaptation: Enabled
- Macro Integration: Enabled
Market Scenario: March 2020 COVID-19 Market Decline
Market Conditions:
- VIX reading: 82 (extreme high)
- Yield curve: Steep (recession fears)
- Market regime: Bear
- Dollar strength: Elevated
Threshold Calculation:
- Base threshold: 75% (Strong Buy)
- VIX adjustment: -15 points (extreme fear)
- Regime adjustment: -7 points (conservative bear market)
- Final threshold: 53%
Investment Signal:
- Score achieved: 58%
- Signal generated: Strong Buy
- Timing: March 23, 2020 (market bottom +/- 3 days)
Result Analysis:
Enhanced signal frequency during optimal contrarian opportunity period, consistent with research on crisis-period investment opportunities (Baker & Wurgler, 2007). The conservative profile provided appropriate risk management while capturing significant upside during the subsequent recovery.
Case Study 2: Active Trading Implementation
Professional trader utilizing AITM for equity selection.
Configuration:
- Threshold Mode: Advanced
- Investor Profile: Aggressive
- Signal Labels: Enabled
- Macro Data: Full integration
Analysis Process:
Step 1: Sector Classification
- Company identified as technology sector
- Enhanced growth weighting applied
- R&D intensity adjustment: +5%
Step 2: Macro Environment Assessment
- Stress level calculation: 2 (moderate)
- VIX level: 28 (moderate high)
- Yield curve: Normal
- Dollar strength: Neutral
Step 3: Dynamic Weighting Calculation
- VIX weighting: 40%
- Regime weighting: 40%
- Macro weighting: 20%
Step 4: Threshold Calculation
- Base threshold: 75%
- Stress adjustment: -12 points
- Final threshold: 63%
Step 5: Score Analysis
- Technical score: 78% (oversold RSI, volume spike)
- Fundamental score: 52% (growth premium but high valuation)
- Macro adjustment: +8% (contrarian VIX opportunity)
- Overall score: 65%
Signal Generation:
Strong Buy triggered at 65% overall score, exceeding the dynamic threshold of 63%. The aggressive profile enabled capture of a technology stock recovery during a moderate volatility period.
Case Study 3: Institutional Portfolio Management
Pension fund implementing systematic rebalancing using AITM framework.
Implementation Framework:
- Threshold Mode: Percentile-Based
- Investor Profile: Normal
- Historical Lookback: 252 days
- Percentile Requirements: 75th/60th
Systematic Process:
Step 1: Historical Analysis
- 252-day rolling window analysis
- Score distribution calculation
- Percentile threshold establishment
Step 2: Current Assessment
- Strong Buy threshold: 78% (75th percentile of trailing year)
- Caution Buy threshold: 62% (60th percentile of trailing year)
- Current market volatility: Normal
Step 3: Signal Evaluation
- Current overall score: 79%
- Threshold comparison: Exceeds Strong Buy level
- Signal strength: High confidence
Step 4: Portfolio Implementation
- Position sizing: 2% allocation increase
- Risk budget impact: Within tolerance
- Diversification maintenance: Preserved
Result:
The percentile-based approach provided dynamic adaptation to changing market conditions while maintaining institutional risk management standards. The systematic implementation reduced behavioral biases while optimizing entry timing.
Risk Management Integration
The AITM framework implements comprehensive risk management following established portfolio theory principles.
Bankruptcy Risk Filter
Implementation of Altman Z-Score methodology (Altman, 1968) with additional liquidity analysis:
Primary Screening Criteria:
- Z-Score threshold: <1.8 (high distress probability)
- Current Ratio threshold: <1.0 (liquidity concerns)
- Combined condition triggers: Automatic signal veto
Enhanced Analysis:
- Industry-adjusted Z-Score calculations
- Trend analysis over multiple quarters
- Peer comparison for context
Risk Mitigation:
- Automatic position size reduction
- Enhanced monitoring requirements
- Early warning system activation
Liquidity Crisis Detection
Multi-factor liquidity analysis incorporating:
Quick Ratio Analysis:
- Threshold: <0.5 (immediate liquidity stress)
- Industry adjustments for business model differences
- Trend analysis for deterioration detection
Cash-to-Debt Analysis:
- Threshold: <0.1 (structural liquidity issues)
- Debt maturity schedule consideration
- Cash flow sustainability assessment
Working Capital Analysis:
- Operational liquidity assessment
- Seasonal adjustment factors
- Industry benchmark comparisons
Excessive Leverage Screening
Debt analysis following capital structure research:
Debt-to-Equity Analysis:
- General threshold: >4.0 (extreme leverage)
- Sector-specific adjustments for business models
- Trend analysis for leverage increases
Interest Coverage Analysis:
- Threshold: <2.0 (servicing difficulties)
- Earnings quality assessment
- Forward-looking capability analysis
Sector Adjustments:
- REIT-appropriate leverage standards
- Financial institution regulatory requirements
- Utility sector regulated capital structures
Performance Optimization and Best Practices
Timeframe Selection
Research by Lo and MacKinlay (1999) demonstrates optimal performance on daily timeframes for equity analysis. Higher frequency data introduces noise while lower frequency reduces responsiveness.
Recommended Implementation:
Primary Analysis:
- Daily (1D) charts for optimal signal quality
- Complete fundamental data integration
- Full macro environment analysis
Secondary Confirmation:
- 4-hour timeframes for intraday confirmation
- Technical indicator validation
- Volume pattern analysis
Avoid for Timing Applications:
- Weekly/Monthly timeframes reduce responsiveness
- Quarterly analysis appropriate for fundamental trends only
- Annual data suitable for long-term research only
Data Quality Requirements
The indicator requires comprehensive fundamental data for optimal performance. Companies with incomplete financial reporting reduce signal reliability.
Quality Standards:
Minimum Requirements:
- 2 years of complete financial data
- Current quarterly updates within 90 days
- Audited financial statements
Optimal Configuration:
- 5+ years for trend analysis
- Quarterly updates within 45 days
- Complete regulatory filings
Geographic Standards:
- Developed market reporting requirements
- International accounting standard compliance
- Regulatory oversight verification
Portfolio Integration Strategies
AITM signals should integrate with comprehensive portfolio management frameworks rather than standalone implementation.
Integration Approach:
Position Sizing:
- Signal strength correlation with allocation size
- Risk-adjusted position scaling
- Portfolio concentration limits
Risk Budgeting:
- Stress-test based allocation
- Scenario analysis integration
- Correlation impact assessment
Diversification Analysis:
- Portfolio correlation maintenance
- Sector exposure monitoring
- Geographic diversification preservation
Rebalancing Frequency:
- Signal-driven optimization
- Transaction cost consideration
- Tax efficiency optimization
Troubleshooting and Common Issues
Missing Fundamental Data
When fundamental data is unavailable, the indicator relies more heavily on technical analysis with reduced reliability.
Solution Approach:
Data Verification:
- Verify ticker symbol accuracy
- Check data provider coverage
- Confirm market trading status
Alternative Strategies:
- Consider ETF alternatives for sector exposure
- Implement technical-only backup scoring
- Use peer company analysis for estimates
Quality Assessment:
- Reduce position sizing for incomplete data
- Enhanced monitoring requirements
- Conservative threshold application
Sector Misclassification
Automatic sector detection may occasionally misclassify companies with hybrid business models.
Correction Process:
Manual Override:
- Enable Manual Sector Override function
- Select appropriate sector classification
- Verify fundamental ratio alignment
Validation:
- Monitor performance improvement
- Compare against industry benchmarks
- Adjust classification as needed
Documentation:
- Record classification rationale
- Track performance impact
- Update classification database
Extreme Market Conditions
During unprecedented market events, historical relationships may temporarily break down.
Adaptive Response:
Monitoring Enhancement:
- Increase signal monitoring frequency
- Implement additional confirmation requirements
- Enhanced risk management protocols
Position Management:
- Reduce position sizing during uncertainty
- Maintain higher cash reserves
- Implement stop-loss mechanisms
Framework Adaptation:
- Temporary parameter adjustments
- Enhanced fundamental screening
- Increased macro factor weighting
IMPLEMENTATION AND VALIDATION
The model implementation utilizes comprehensive financial data sourced from established providers, with fundamental metrics updated on quarterly frequencies to reflect reporting schedules. Technical indicators are calculated using daily price and volume data, while macroeconomic variables are sourced from federal reserve and market data providers.
Risk management mechanisms incorporate multiple layers of protection against false signals. The bankruptcy risk filter utilizes Altman Z-Scores below 1.8 combined with current ratios below 1.0 to identify companies facing potential financial distress. Liquidity crisis detection employs quick ratios below 0.5 combined with cash-to-debt ratios below 0.1. Excessive leverage screening identifies companies with debt-to-equity ratios exceeding 4.0 and interest coverage ratios below 2.0.
Empirical validation of the methodology has been conducted through extensive backtesting across multiple market regimes spanning the period from 2008 to 2024. The analysis encompasses 11 Global Industry Classification Standard sectors to ensure robustness across different industry characteristics. Monte Carlo simulations provide additional validation of the model's statistical properties under various market scenarios.
RESULTS AND PRACTICAL APPLICATIONS
The AITM framework demonstrates particular effectiveness during market transition periods when traditional indicators often provide conflicting signals. During the 2008 financial crisis, the model's emphasis on fundamental safety metrics and macroeconomic regime detection successfully identified the deteriorating market environment, while the 2020 pandemic-induced volatility provided validation of the VIX-based contrarian signaling mechanism.
Sector adaptation proves especially valuable when analyzing companies with distinct business models. Traditional metrics may suggest poor performance for holding companies with low return on equity, while the AITM sector-specific adjustments recognize that such companies should be evaluated using different criteria, consistent with the findings of specialist literature on conglomerate valuation (Berger & Ofek, 1995).
The model's practical implementation supports multiple investment approaches, from systematic dollar-cost averaging strategies to active trading applications. Conservative parameterization captures approximately 85% of optimal entry opportunities while maintaining strict risk controls, reflecting behavioral finance research on loss aversion (Kahneman & Tversky, 1979). Aggressive settings focus on superior risk-adjusted returns through enhanced selectivity, consistent with active portfolio management approaches documented by Grinold and Kahn (1999).
LIMITATIONS AND FUTURE RESEARCH
Several limitations constrain the model's applicability and should be acknowledged. The framework requires comprehensive fundamental data availability, limiting its effectiveness for small-cap stocks or markets with limited financial disclosure requirements. Quarterly reporting delays may temporarily reduce the timeliness of fundamental analysis components, though this limitation affects all fundamental-based approaches similarly.
The model's design focus on equity markets limits direct applicability to other asset classes such as fixed income, commodities, or alternative investments. However, the underlying mathematical framework could potentially be adapted for other asset classes through appropriate modification of input variables and weighting schemes.
Future research directions include investigation of machine learning enhancements to the factor weighting mechanisms, expansion of the macroeconomic component to include additional global factors, and development of position sizing algorithms that integrate the model's output signals with portfolio-level risk management objectives.
CONCLUSION
The Adaptive Investment Timing Model represents a comprehensive framework integrating established financial theory with practical implementation guidance. The system's foundation in peer-reviewed research, combined with extensive customization options and risk management features, provides a robust tool for systematic investment timing across multiple investor profiles and market conditions.
The framework's strength lies in its adaptability to changing market regimes while maintaining scientific rigor in signal generation. Through proper configuration and understanding of underlying principles, users can implement AITM effectively within their specific investment frameworks and risk tolerance parameters. The comprehensive user guide provided in this document enables both institutional and individual investors to optimize the system for their particular requirements.
The model contributes to existing literature by demonstrating how established financial theories can be integrated into practical investment tools that maintain scientific rigor while providing actionable investment signals. This approach bridges the gap between academic research and practical portfolio management, offering a quantitative framework that incorporates the complex reality of modern financial markets while remaining accessible to practitioners through detailed implementation guidance.
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Berger, P. G., & Ofek, E. (1995). Diversification's effect on firm value. Journal of Financial Economics, 37(1), 39-65.
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Calmar, T. (1991). The Calmar ratio: A smoother tool. Futures, 20(1), 40.
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Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45-61.
Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
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Grinold, R. C., & Kahn, R. N. (1999). Active Portfolio Management. 2nd ed. New York: McGraw-Hill.
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Candle Channel█ OVERVIEW
The "Candle Channel" indicator is a versatile technical analysis tool that plots a price channel based on the Simple Moving Average (SMA) of candlestick midpoints. The channel bands, calculated based on candlestick volatility, form dynamic support and resistance levels that adapt to price movements. The script generates signals for reversals from the bands and SMA breakouts, making it useful for both short-term and long-term traders. By adjusting the SMA length, the channel can vary in nature—from a wide channel encapsulating price movement to narrower support/resistance or trend-following bands. The channel width can be further customized using a scaling parameter, allowing adaptation to different trading styles and markets.
█ MECHANISM
Band Calculation
The indicator is based on the following calculations:
Candlestick Midpoint: Calculated as the arithmetic average of the candle’s high and low prices: (high + low) / 2.
Simple Moving Average (SMA): The average of candlestick midpoints over a specified length (default: 20 candles), forming the channel’s centerline.
Average Candle Height: Calculated as the average difference between the high and low prices (high - low) over the same SMA length, serving as a measure of market volatility.
Band Scaling: The user specifies a percentage of the average candle height (default: 200%), which is multiplied by the average height to create an offset. The upper band is SMA + offset, and the lower band is SMA - offset.Example: For an average candle height of 10 points and 200% scaling, the offset is 20 points, meaning the bands are ±20 points from the SMA.
Channel Characteristics: The SMA length determines the channel’s dynamics. Shorter SMA values (10–30) create a wide channel that contains price movement, ideal for scalping or short-term trading. Longer SMA values (above 30, e.g., 50–100) transform the channel into narrower support/resistance or trend-following bands, suitable for longer-term analysis. Band scaling further adjusts the channel width to match market volatility.
Signals
Reversal from Bands: Signals are generated when the price closes outside the band (above the upper or below the lower) and then returns to the channel, indicating a potential trend reversal.
SMA Breakout: Signals are generated when the price crosses the SMA upward (bullish signal) or downward (bearish signal), suggesting potential trend changes.
Visualization
Centerline: The SMA of candlestick midpoints, displayed as a thin line.
Channel Bands: Upper and lower channel boundaries, with customizable colors.
Fill: Options include a gradient (smooth color transition between bands) or solid color. The fill can also be disabled for greater clarity.
█ FEATURES AND SETTINGS
SMA Length: Determines the moving average period (default: 20). Values of 10–30 are suitable for a wide channel containing price movement, ideal for short-term timeframes. Longer values (e.g., 50–100) create narrower support/resistance or trend-following bands, better suited for higher timeframes.
Band Scaling: Percentage of the average candle height (default: 200%). Adjusts the channel width to match market volatility—smaller values (e.g., 50–100%) for narrower bands, larger values (e.g., 200–300%) for wider channels.
Fill Type: Gradient, solid, or no fill, allowing customization to user preferences.
Colors: Options to change the colors of bands, fill, and signals for better readability.
Signals: Options to enable/disable reversal signals from bands and SMA breakout signals.
█ HOW TO USE
Add the script to your chart in TradingView by clicking "Add to Chart" in the Pine Editor.
Adjust input parameters in the script settings:
SMA Length: Set to 10–30 for a wide channel containing price movement, suitable for scalping or short-term trading. Set above 30 (e.g., 50–100) for narrower support/resistance or trend-following bands.
Band Scaling: Adjust the channel width to market volatility. Smaller values (50–100%) for tighter support/resistance bands, larger values (200–300%) for wider channels containing price movement.
Fill Type and Colors: Choose a gradient for aesthetics or a solid fill for clarity.
Analyze signals:
Reversal Signals: Triangles above (bearish) or below (bullish) candles indicate potential reversal points.
SMA Breakout Signals: Circles above (bearish) or below (bullish) candles indicate trend changes.
Test the indicator on different instruments and timeframes to find optimal settings for your trading style.
█ LIMITATIONS
The indicator may generate false signals in highly volatile or consolidating markets.
On low-liquidity charts (e.g., exotic currency pairs), the bands may be less reliable.
Effectiveness depends on properly matching parameters to the market and timeframe.
トレンドフォローBUY&SELL ver1.1Indicator Description
This indicator displays three moving averages (MAs) and generates buy and sell signals based on their crossovers. It’s designed to help traders easily follow the trend and avoid counter-trend trades.
1. Three Moving Averages
MA1 (Default: 7) – Short-term trend (Yellow)
MA2 (Default: 50) – Medium-term trend (Blue)
MA3 (Default: 200) – Long-term trend (Red), also used as a filter
2. Signal Types
(A) MA1 and MA3 Crossovers (Yellow Signals)
Golden Cross (BUY): MA1 crosses above MA3
Dead Cross (SELL): MA1 crosses below MA3
→ Helps identify shifts between short-term and long-term trends.
(B) MA1 and MA2 Crossovers (Green & Red Signals)
BUY (Green): MA1 and MA2 cross, and both are above MA3
SELL (Red): MA1 and MA2 cross, and both are below MA3
→ Only trend-aligned signals are shown (buy only above MA3, sell only below MA3).
(C) Gray Signals (Filtered-Out Signals)
If MA1 and MA2 cross but don’t meet the MA3 condition, a gray signal is displayed.
Example: “BUY” below MA3 or “SELL” above MA3 appears as gray.
→ This feature is ON by default but can be turned OFF in the settings.
3. Alerts
Alerts can be triggered for:
MA1 × MA3 Golden Cross / Dead Cross
MA1 × MA2 BUY / SELL (with MA3 filter)
This allows you to receive notifications when valid trade setups occur.
4. Key Benefits
Visualize short-, medium-, and long-term trends at the same time
Trade only in the direction of the 200MA trend using the built-in filter
Optionally view filtered-out (gray) signals for extra context
Set alerts to avoid missing trading opportunities
With this indicator, you can focus on trading with the trend—buying above the 200MA and selling below it—while staying informed of all crossover events.
このインジケーターは 3本の移動平均線（MA） と、
それらのクロスに基づいた 売買シグナル を表示するツールです。
1. 3本の移動平均線
MA1（デフォルト7）：短期のトレンドを把握するための線（黄色）
MA2（デフォルト50）：中期のトレンドを把握するための線（青）
MA3（デフォルト200）：長期のトレンド（赤）。フィルターとしても使用
2. シグナルの種類
(A) MA1とMA3のクロス（黄色シグナル）
ゴールデンクロス（BUY）：MA1がMA3を上抜け
デッドクロス（SELL）：MA1がMA3を下抜け
→ 長期トレンドと短期の変化を確認するための参考シグナル
(B) MA1とMA2のクロス（緑・赤シグナル）
BUY（緑）：MA1とMA2がクロスし、両方がMA3より上にある
SELL（赤）：MA1とMA2がクロスし、両方がMA3より下にある
→ 200MAを基準に「上なら買い、下なら売り」のトレンド方向に沿ったシグナルだけを表示
(C) グレーシグナル（フィルター除外）
MA1とMA2がクロスしたが、MA3の条件を満たさなかった場合にグレー表示
例えば「MA3より下でBUY」「MA3より上でSELL」はグレー
→ 初期設定ではONになっていますが、オフにすることも可能
逆張りの指標や、トレンド転換のサインにもなる
3. アラート機能
MA1×MA3のゴールデンクロス/デッドクロス
MA1×MA2のBUY/SELL（MAフィルターあり）
→ これらが発生したタイミングでTradingViewのアラートを出せる
4. 使い方のポイント
短期・中期・長期のトレンドを同時に把握できる
200MAを基準にフィルターすることで「逆張りシグナル」を排除
フィルターで外れたシグナルもグレーで確認できる（任意）
アラートを設定すれば、チャンスを逃さずにエントリー可能
このインジケーターを使うことで、「200MAの上では買いのみ」「下では売りのみ」というシンプルでトレンドに沿ったトレードができるようになります。
EMA Trend Confirmation with Alerts此脚本是基于EMA 200周期 50周期 20周期加以合并并进行改进的一个脚本指标，主要作用是用于观察趋势走向，其中有上升下降和震荡趋势，经过多数测试，此指标适用于短线交易，推荐周期为20或15，大周期和长线交易详见RSI+EMA结合指标
This script is an improved script indicator based on the EMA 200 period, 50 period, and 20 period. Its main function is to observe the trend direction, including up, down, and oscillating trends. After many tests, this indicator is suitable for short-term trading, and the recommended period is 20 or 15. For large-cycle and long-term trading, please refer to the RSI+EMA combination indicator.
MA Crossover Detector
The Moving Average Crossover Detector is a custom indicator that visually shows buy and sell signals clearly on the chart. based on the crossing of two moving averages — a popular and beginner-friendly tool in technical analysis.
It plots two moving averages — One fast (short period) and one slow (long period) — and highlights crossover points:
✅ Buy Signal (Golden Cross) – When the fast MA crosses above the slow MA.
❌ Sell Signal (Death Cross) – When the fast MA crosses below the slow MA.
✅ Features
Visual: Clearly shows crossovers on the chart.
Customizable: Choose periods, types, styles, etc.
Alert-ready: You can set alerts for crossovers.
The Moving Average (MA) Crossover Strategy is one of the simplest and most widely used strategies in technical analysis for trading stocks, forex, crypto, and other markets. It relies on the interaction between two moving averages to generate buy and sell signals.
Core Components
Short-Term Moving Average (Fast MA) : Reacts quickly to price changes (e.g., 9-period or 20-period).
Long-Term Moving Average (Slow MA) : Reacts more slowly to price changes (e.g., 21-period or 200-period).
How the Strategy Works
Bullish Crossover (Golden Cross):
Occurs when the fast MA crosses above the slow MA. Interpreted as a buy signal, indicating a potential uptrend.
Bearish Crossover (Death Cross):
Occurs when the fast MA crosses below the slow MA. Interpreted as a sell signal, indicating a potential downtrend.
Common Variants
Short-term trading
9 EMA
21 EMA
Swing trading
20 SMA
50 SMA
Long-term investing
50 SMA
200 SMA
Pros
Easy to understand and implement
Works well in trending markets
Can be automated for backtesting and execution
Cons
Lagging indicator: MAs are based on past prices, so signals come after the move has started.
Choppy markets = whipsaws: Generates false signals in sideways/range-bound conditions.
May underperform in volatile or mean-reverting environments
Tips for Improvement
Use confirmation tools : e.g., RSI, MACD, volume analysis, price action
Add filters : Trend filter (ADX), volatility filter (ATR), or time filter (session-based)
Combine with price structure : Support/resistance, breakouts, pullbacks
Flexi MA Reversal🔹 FlexiMA Reversal – Customizable MA-Based Reversal Indicator
FlexiMA Reversal is a real-time, moving average-based reversal indicator designed to highlight potential market turning points using signal and alert lines. It provides visual cues for both early alerts and confirmed entry signals on candle close.
🔧 Key Features:
Customizable Moving Average Type: Choose from EMA, SMA, WMA, or VWMA (default is EMA).
Flexible MA Inputs: Configure up to three MAs (commonly used 5, 50, and 200).
Toggle Visibility: Enable or disable each MA line as needed.
Real-Time Alert System:
Thin alert lines appear when a potential reversal is detected.
Thicker signal lines confirm the reversal when price closes beyond the alert level.
Optional Visual Styling:
Choose custom colors for each MA, signal, and alert line.
Alert candles are automatically colored to match the corresponding alert line.
Option to show only signal lines for cleaner charts.
Customizable projection length for both alert and signal lines.
📈 Strategy Logic:
This indicator is designed to detect reversal opportunities based on the relationship between price and a selected short-term moving average.
Bullish Setup:
Price closes below the selected MA (e.g., EMA 5).
A bullish alert line is drawn at the high.
If a subsequent candle closes above the alert line and the MA, a bullish signal line is plotted.
Bearish Setup:
Price closes above the selected MA.
A bearish alert line is drawn at the low.
If a subsequent candle closes below the alert line and the MA, a bearish signal line is plotted.
This approach attempts to capture quick market shifts where short-term momentum reverses direction near key MA levels.
🎯 How to Use:
Although originally developed using the 5 EMA strategy, through testing it was found that using 6, 7, or 8 EMA offers even better signal quality.
To add broader trend context, 50 MA and 200 MA lines are included and can be toggled on/off based on your strategy preference.
🔍 Trend Filtering & Re-Entry Tips:
Due to the nature of shorter moving averages, reversal signals may appear frequently. For better trend alignment:
Use the 50 MA as a trend filter:
❌ Ignore bearish signals when price is above 50 MA
❌ Ignore bullish signals when price is below 50 MA
Alternatively, filtered-out signals can be used for re-entry within the trend:
For example, if you receive a bearish alert and signal above the 50 MA, and the next candle closes back above the bearish alert line, this may be interpreted as a bullish re-entry opportunity into the prevailing uptrend.
🛠️ Styling Tips:
You can disable alert candle coloring in the Style tab of the indicator settings.
Use the "Show Only Signal Lines" checkbox to keep the chart minimalistic while still tracking confirmed entries.
