3 EMA Breakout + RSI Pro3in 1 will mostly tell yuo the direction of the trend and tell you to buy and sell but it is not a guarantee that you will take the trade this is only a technical analysis.
트렌드 어낼리시스
Supertrend Auto BUY / SELL Signals by ISadd super trend 10.3 and 10.1 in the chart, add EMA 20, use this script for auto buy and sell signal. target you have to decide based on your instrument
Daily Pivot Points LEVELS S-RThis indicator plots daily pivot points based on the previous day’s high, low, and close. It displays the main pivot line, as well as the first levels of support (S1) and resistance (R1), with optional second levels (R2, S2) for additional reference. Ideal for
Bitcoin ETF Cumulative Net InflowIndicator Description:
This indicator calculates and plots the cumulative net inflow (in billions of USD) for selected Bitcoin ETFs on the main price chart. It uses AUM data from TradingView to estimate daily net flows, adjusted for BTC price changes, and accumulates them over time. The line is overlaid on the price chart (e.g., BTCUSD) with a right scale for better visibility, helping to identify correlations between ETF inflows and Bitcoin price movements.
Key Features:
Supports selection of 10 major Bitcoin ETFs (IBIT, FBTC, ARKB, etc.) via inputs.
Cumulative inflow line (purple, linewidth=2) for trend analysis.
Data sourced from request.financial("AUM", "D") for accuracy.
Timeframe LiquidityTimeframe Liquidity – Multi-Timeframe Highs & Lows by
Timeframe Liquidity automatically plots previous day, week, month, and year highs and lows, key liquidity zones used by smart money and price-action traders. These levels extend into the future and can automatically stop once price wicks through, showing clear liquidity sweeps and tested zones.
Perfect for traders using ICT / SMC concepts, liquidity theory, or market structure analysis. Instantly see where liquidity rests, where it’s been taken, and how price reacts at major support and resistance.
Features:
Auto-plots PDH/PDL, PWH/PWL, PMH/PML, PYH/PYL
Custom line styles, colors, and label sizes
Option to stop line on wick (liquidity sweep)
Smart timeframe visibility (hides same-TF levels)
Accurate UTC offset handling
Identify liquidity pools fast, trade cleaner charts, and track where smart money hunts liquidity.
Built for precision, clarity, and confluence.
Fib OscillatorWhat is Fib Oscillator and How to Use it?
🔶 1. Conceptual Overview
The Fib Oscillator is a Fibonacci-based relative position oscillator.
Instead of measuring momentum (like RSI or MACD), it measures where price currently sits between the recent swing high and swing low, expressed as a percentage within the Fibonacci range.
In other words:
It answers: “Where is price right now within its most recent dynamic range?”
It visualizes retracement and extension zones numerically, providing continuous feedback between 0% and 100% (and beyond if extended).
🔶 2. What the Script Does
The indicator:
Automatically detects recent high and low levels using an adaptive lookback window, which depends on ATR volatility.
Calculates the current price’s position between those levels as a percentage (0–100).
Plots that percentage as an oscillator — showing visually whether price is near the top, middle, or bottom of its recent range.
Overlays Fibonacci retracement levels (23.6%, 38.2%, 50%, 61.8%, 78.6%) as reference zones.
Generates alerts when the oscillator crosses key Fib thresholds — which can signal retracement completion, breakout potential, or pullback exhaustion.
🔶 3. Technical Flow Breakdown
(a) Inputs
Input Description Default Notes
atrLength ATR period used for volatility estimation 14 Used to dynamically tune lookback sensitivity
minLookback Minimum lookback window (candles) 20 Ensures stability even in low volatility
maxLookback Maximum lookback window 100 Limits over-expansion during high volatility
isInverse Inverts chart orientation false Useful for inverse markets (e.g. shorts or inverse BTC view)
(b) Volatility-Adaptive Lookback
Instead of using a fixed lookback, it calculates:
lookback
=
SMA(ATR,10)
/
SMA(Close,10)
×
500
lookback=SMA(ATR,10)/SMA(Close,10)×500
Then it clamps this between minLookback and maxLookback.
This makes the oscillator:
More reactive during high volatility (shorter lookback)
More stable during calm markets (longer lookback)
Essentially, it self-adjusts to market rhythm — you don’t have to constantly tweak lookback manually.
(c) High-Low Reference Points
It takes the highest and lowest points within the dynamic lookback window.
If isInverse = true, it flips the candle logic (useful if viewing inverse instruments like stablecoin pairs or when analyzing bearish setups invertedly).
(d) Oscillator Core
The main oscillator line:
osc
=
(
close
−
low
)
(
high
−
low
)
×
100
osc=
(high−low)
(close−low)
×100
0% = Price is at the lookback low.
100% = Price is at the lookback high.
50% = Midpoint (balanced).
Between Fibonacci percentages (23.6%, 38.2%, 61.8%, etc.), the oscillator indicates retracement stages.
(e) Fibonacci Levels as Reference
It overlays horizontal reference lines at:
0%, 23.6%, 38.2%, 50%, 61.8%, 78.6%, 100%
These act as support/resistance bands in oscillator space.
You can read it similar to how traders use Fibonacci retracements on charts, but compressed into a single line oscillator.
(f) Alerts
The script includes built-in alert conditions for crossovers at each major Fibonacci level.
You can set TradingView alerts such as:
“Oscillator crossed above 61.8%” → possible bullish continuation or breakout.
“Oscillator crossed below 38.2%” → possible pullback or correction starting.
This allows automated monitoring of fib retracement completions without manually drawing fib levels.
🔶 4. How to Use It
🔸 Visual Interpretation
Oscillator Value Zone Market Context
0–23.6% Deep Retracement Potential exhaustion of a down-move / early reversal
23.6–38.2% Shallow retracement zone Possible continuation phase
38.2–50% Mid retracement Neutral or indecisive structure
50–61.8% Key pivot region Common trend resumption zone
61.8–78.6% Late retracement Often “last pullback” area
78.6–100% Near high range Possible overextension / profit-taking
>100% Range breakout New leg formation / expansion
🔸 Practical Application Steps
Load the indicator on your chart (set overlay = false, so it’s below the main price chart).
Observe oscillator position relative to fib bands:
Use it to determine retracement depth.
Combine with structure tools:
Trend lines, swing points, or HTF market structure.
Use crossovers for timing:
Crossing above 61.8% in an uptrend often confirms breakout continuation.
Crossing below 38.2% in a downtrend signals renewed downside momentum.
For range markets, oscillator swings between 23.6% and 78.6% can define accumulation/distribution boundaries.
🔶 5. When to Use It
During Retracements: To gauge how deep the pullback has gone.
During Range Markets: To identify relative overbought/oversold positions.
Before Breakouts: Crossovers of 61.8% or 78.6% often precede impulsive moves.
In Multi-Timeframe Contexts:
LTF (15M–1H): Detect intraday retracement exhaustion.
HTF (4H–1D): Confirm major range expansions or key reversal zones.
🔶 6. Ideal Companion Indicators
The Fib Oscillator works best when contextualized with structure, volatility, and trend bias indicators.
Below are optimal pairings:
Companion Indicator Purpose Integration Insight
Market Structure MTF Tool Identify active trend direction Use Fib Oscillator only in trend direction for cleaner signals
EMA Ribbon / Supertrend Trend confirmation Align oscillator crossovers with EMA bias
ATR Bands / Volatility Envelope Validate breakout strength If oscillator >78.6% & ATR rising → valid breakout
Volume Oscillator Confirm retracement strength Volume contraction + oscillator under 38.2% → potential reversal
HTF Fib Retracement Tool Combine LTF oscillator with HTF fib confluence Powerful multi-timeframe setups
RSI or Stochastic Measure momentum relative to position RSI divergence while oscillator near 78.6% → exhaustion clue
🔶 7. Understanding the Settings
Setting Function Practical Impact
ATR Period (14) Controls volatility sampling Higher = smoother lookback adaptation
Min Lookback (20) Smallest window allowed Lower = more reactive but noisier
Max Lookback (100) Largest window allowed Higher = smoother but slower to react
Inverse Candle Chart Flips oscillator vertically Useful when analyzing bearish or inverse scenarios (e.g. short-side fib mapping)
Recommended Configs:
For scalping/intraday: ATR 10–14, lookback 20–50
For swing/position trading: ATR 14–21, lookback 50–100
🔶 8. Example Trade Logic (Practical Use)
Scenario: Uptrend on 4H chart
Oscillator drops to below 38.2% → retracement zone
Price consolidates → oscillator stabilizes
Oscillator crosses above 50% → pullback ending
Entry: Long when oscillator crosses above 61.8%
Exit: Near 78.6–100% zone or upon divergence with RSI
For Short Bias (Inverse Setup):
Enable isInverse = true to visually flip the oscillator (so lows become highs).
Use the same thresholds inversely.
🔶 9. Strengths & Limitations
✅ Strengths
Dynamic, self-adapting to volatility
Quantifies Fib retracement as a continuous function
Compact oscillator view (no clutter on chart)
Works well across all timeframes
Compatible with both trending and ranging markets
⚠️ Limitations
Doesn’t define trend direction — must be used with structure filters
Can whipsaw during choppy consolidations
The “lookback auto-adjust” may lag in sudden volatility shifts
Shouldn’t be used standalone for entries without structural confluence
🔶 10. Summary
The “Fib Oscillator” is a dynamic Fibonacci-relative positioning tool that merges retracement theory with adaptive volatility logic.
It gives traders an intuitive, quantified view of where price sits within its recent fib range, allowing anticipation of pullbacks, reversals, or breakout momentum.
Think of it as a "Fibonacci RSI", but instead of momentum strength, it shows positional depth — the vibrational location of price within its natural swing cycle.
10Y–2Y Treasury Yield Curve Spread & MES % Change📝 Description:
This indicator tracks the U.S. 10-Year minus 2-Year Treasury yield spread — a powerful macroeconomic signal often used by professional traders to gauge market sentiment and recession risk — and overlays an optional MES % change line to help intraday futures traders spot macro–price divergences in real time.
Features:
🏦 Plots the 10Y–2Y spread, with optional EMA smoothing.
📉 Highlights yield curve inversion (background turns red when spread < 0).
📊 Optional MES % change line from daily or RTH open for directional bias.
🔔 Alert conditions for:
Yield curve inversion / un-inversion.
Sudden spread spikes in basis points (customizable).
🧮 Optional correlation plot to visualize relationship strength between MES and the yield curve.
🧭 Z-score normalization allows both series to be viewed in one pane without scaling issues.
Why it matters:
A falling or inverted 2s10s spread often signals risk-off behavior and pressure on equities.
A steepening curve tends to support risk-on rallies.
Divergences between MES price action and the spread can provide early warning signals of reversals or fakeouts.
Best used with:
MES (MES1!) or MYM charts for intraday & swing bias.
Fed event days, CPI/NFP, or any macro-sensitive sessions.
VWAP or structure-based intraday trading strategies.
⚠️ Note: This indicator is for informational purposes only and does not constitute financial advice. Always combine macro context with your own trade plan and risk management.
Ichimoku MultiTF WillyArt v1.0.0What this indicator does
Ichimoku WillyArt turns the Ichimoku lines into angle-based momentum across multiple timeframes (W, D, 4H, 1H, 30m, 5m).
For each TF it computes the slope (angle in degrees) of:
Tenkan-sen
Kijun-sen
Senkou Span A
Senkou Span B
Angles are normalized so they’re comparable across assets and scales. You get a table with the angle per line and a quick emoji direction (↑, →, ↓), optional plots of the chosen line, and ready-to-use alerts.
Why angle?
Slope-as-degrees is an intuitive proxy for momentum/impulse:
Positive angle → line rising (bullish impulse).
Negative angle → line falling (bearish impulse).
Near zero → flat/indecisive.
Two normalization modes
ATR (default): slope / ATR. Robust across instruments; less sensitive to price level.
%Price: slope / price. More sensitive; can highlight subtle turns on low-volatility symbols.
Inputs you’ll actually care about
Timeframes: W, D, 4H, 1H, 30m, 5m (all fetched MTF, independent of chart TF).
Ichimoku lengths: Tenkan (9), Kijun (26), Span B (52) — standard defaults.
Bars for slope (ΔN): How many bars back the slope is measured. Higher = smoother, slower.
Threshold (°) for “strong”: Angle magnitude that qualifies as strong ↑/↓.
What you’ll see
Matrix/Table (top-right): For each TF, the angle (°) of Tenkan, Kijun, Span A, Span B + an emoji:
↑ above threshold, ↓ below −threshold, → in between.
Optional plots: Toggle “Plot angles” to visualize the chosen series’ angle across TFs.
Alerts included (ready to pick in “Create Alert”)
Sustained state: e.g., “Kijun 4H: strong ↑ angle” triggers while angle > threshold.
Threshold cross (one-shot): e.g., “Kijun 1H: upward threshold cross” fires on crossing.
Consensus (multi-TF): “Kijun consensus ↑ (D/4H/1H/30m/5m)” when all selected TFs align up (and the symmetric down case).
Messages are constant strings (TradingView requirement), so they compile cleanly. If you want dynamic text (current angle, threshold value, etc.), enable your own alert() calls—this script structure supports adding them.
How to use it (workflow)
Add to chart. No need to switch chart TF; the script pulls W/D/4H/1H/30m/5m internally.
Pick normalization. Start with ATR. Switch to %Price if you want more sensitivity.
Set ΔN & threshold.
Intraday momentum: try ΔN = 3–5 and threshold ≈ 4–8°.
Swing/position: ΔN = 5–9 and threshold ≈ 3–6° (with ATR).
Scan the table. Look for alignment (multiple TFs with ↑ or ↓ on Kijun/Spans).
Kijun + Span A up together → trending push.
Span B up/down → cloud baseline tilting (trend quality).
Turn on alerts that match your style: reactive cross for entries, sustained for trend follow, consensus to filter noise.
Reading tips
Kijun angle: great “trend backbone.” Strong ↑ on several TFs = higher-probability pullback buys.
Span A vs. Span B:
Span A reacts faster (momentum).
Span B is slower (structure).
When both tilt the same way, the cloud is genuinely rotating.
Mixed signals? Use higher TFs (W/D/4H) as bias, lower TFs (1H/30m/5m) for timing.
Good to know (limits & best practices)
Angles measure rate of change, not overbought/oversold. Combine with price structure and risk rules.
Extremely low volatility or illiquid symbols can produce tiny angles—%Price mode may help.
ΔN and thresholds are contextual: adapt per market (crypto vs FX vs equities).
Want me to bundle a “pro template” of alert presets (intraday / swing) and a heatmap color scale for the table? Happy to ship v2. 🚀
ema200 plus Description:
This advanced indicator displays Exponential Moving Averages (EMA) across multiple timeframes to help traders identify trend direction and strength across different market perspectives.
Key Features:
Multi-Timeframe EMA Analysis:
Plots 200-period EMA on four different timeframes: 30-minute, 1-hour, 4-hour, and Daily
Each timeframe is displayed with distinct colors for easy visual identification
Visual Elements:
Chart Lines: Four colored EMA lines plotted directly on the price chart
Price Labels: Clear labels showing each EMA's current value at the latest bar
Color-coded Table: Comprehensive data table showing price position relative to each EMA
Trend Identification:
Bullish Signal: When price closes above an EMA (green background in table)
Bearish Signal: When price closes below an EMA (dark background in table)
Helps identify confluence when multiple timeframes align in direction
Customizable Settings:
Adjustable EMA length (default: 200 periods)
Customizable line width and offset
Flexible table positioning (top/middle/bottom, left/center/right)
Configurable table cell size and text appearance
Swing traders analyzing multiple timeframes
Position traders looking for trend confirmation
Technical analysts seeking confluence across time horizons
This indicator provides a comprehensive view of market trends across different time perspectives, helping traders make more informed decisions based on multi-timeframe analysis.
This indicator does not provide trading advice. It is for educational and informational purposes only.
**指标名称：多时间框架200 EMA**
**描述：**
这款高级指标在多个时间框架上显示指数移动平均线（EMA），帮助交易者识别不同市场视角下的趋势方向和强度。
**主要特点：**
1. **多时间框架EMA分析：**
- 在四个不同时间框架上绘制200周期EMA：30分钟、1小时、4小时和日线
- 每个时间框架使用独特颜色显示，便于视觉识别
2. **视觉元素：**
- **图表线：** 在价格图表上直接绘制四条彩色EMA线
- **价格标签：** 清晰显示最新K线处各EMA的当前值
- **颜色编码表格：** 综合数据表格显示价格相对于各EMA的位置
3. **趋势识别：**
- **看涨信号：** 当价格收于EMA上方时（表格中显示绿色背景）
- **看跌信号：** 当价格收于EMA下方时（表格中显示深色背景）
- 帮助识别多个时间框架方向一致时的共振信号
4. **可自定义设置：**
- 可调整EMA长度（默认：200周期）
- 可自定义线宽和偏移量
- 灵活的表格定位（上/中/下，左/中/右）
- 可配置表格单元格大小和文本外观
**适合人群：**
- 分析多时间框架的摆动交易者
- 寻求趋势确认的头寸交易者
- 寻找不同时间维度共振信号的技术分析师
3 EMA Breakout + RSI Pro3in 1 will mostly tell yuo the direction of the trend and tell you to buy and sell but it is not a guarantee that you will take the trade this is only a technical analysis.
EMA HI/LO Cloud Shift + Extra EMA//@version=6
indicator("EMA HI/LO Cloud Shift + Extra EMA + Shift EMA Line", overlay=true, max_lines_count=6, max_labels_count=0)
// ------------------------
// Inputs
// ------------------------
emaLength = input.int(22, "Main EMA Length", minval=1, maxval=200)
emaLineColor = input.color(color.blue, "Main EMA Lines Color")
// Main Cloud colors
cloudAboveColor = input.color(color.new(color.green, 80), "Main Cloud Color (Price Above)")
cloudBelowColor = input.color(color.new(color.red, 80), "Main Cloud Color (Price Below)")
cloudInsideColor = input.color(color.new(color.orange, 80), "Main Cloud Color (Price Inside)")
// ------------------------
// Shift EMA (new logic)
// ------------------------
showShiftEMA = input.bool(true, "Show Shift EMA Line?")
shiftEMALength = input.int(26, "Shift EMA Length", minval=1, maxval=500)
shiftEMASource = input.source(close, "Shift EMA Source") // fully customizable source
shiftEMAColor = input.color(color.purple, "Shift EMA Color")
shiftEMAWide = input.int(2, "Shift EMA Line Width", minval=1, maxval=5)
shiftEMAOffset = input.int(0, "Shift EMA Offset", minval=-100, maxval=100)
// ------------------------
// Second EMA (independent)
// ------------------------
showSecondEMA = input.bool(true, "Show Second EMA?")
secondEMALength = input.int(200, "Second EMA Length", minval=1, maxval=1000)
secondEMAColor = input.color(color.yellow, "Second EMA Color")
secondEMAWide = input.int(2, "Second EMA Line Width", minval=1, maxval=5)
// ------------------------
// Main EMA Cloud Calculations
// ------------------------
emaHigh = ta.ema(high, emaLength)
emaLow = ta.ema(low, emaLength)
// ------------------------
// Main Cloud logic
// ------------------------
priceAboveMain = close > emaHigh
priceBelowMain = close < emaLow
priceInsideMain = not priceAboveMain and not priceBelowMain
cloudColorMain = priceAboveMain ? cloudAboveColor : priceBelowMain ? cloudBelowColor : cloudInsideColor
p1_main = plot(emaHigh, title="Main EMA High", color=emaLineColor, linewidth=2)
p2_main = plot(emaLow, title="Main EMA Low", color=emaLineColor, linewidth=2)
fill(p1_main, p2_main, color=cloudColorMain, title="Main EMA Cloud")
// ------------------------
// Shift EMA Line (replaces cloud offset)
// ------------------------
shiftEMA = ta.ema(shiftEMASource, shiftEMALength)
plot(showShiftEMA ? shiftEMA : na, title="Shift EMA Line", color=shiftEMAColor, linewidth=shiftEMAWide, offset=shiftEMAOffset)
// ------------------------
// Second EMA Plot (Independent)
// ------------------------
secondEMA = ta.ema(close, secondEMALength)
plot(showSecondEMA ? secondEMA : na, title="Second EMA", color=secondEMAColor, linewidth=secondEMAWide)
Custom Date MarkersCustom Date Markers - Pine Script Indicator
This indicator provides a powerful visual tool for technical and pattern analysis by allowing traders to mark up to 10 specific historical dates with customizable vertical lines on any chart. Each date can be assigned its own unique color, making it easy to categorize and distinguish between different types of events or market catalysts.
Primary Use Cases:
The indicator excels at identifying cyclical patterns and recurring market behavior. By marking significant dates such as earnings announcements, Federal Reserve meetings, dividend ex-dates, or seasonal events, traders can quickly visualize whether stocks consistently react in similar ways around these recurring dates. This is particularly valuable for discovering hidden patterns that might not be obvious from price action alone.
Practical Applications:
Earnings Analysis: Mark historical earnings dates to see if a stock tends to rally or sell-off before/after announcements
Macro Events: Identify how assets respond to FOMC meetings, CPI releases, or other economic data
Seasonal Patterns: Track dates that show recurring volatility or directional moves (like tax deadline periods, end-of-quarter re balancing, etc.)
Event Studies: Analyze the impact of company-specific events like product launches, FDA approvals, or leadership changes
Advanced Insights:
What makes this tool particularly interesting is its ability to reveal non-obvious correlations. For example, you might discover that a retail stock consistently experiences volume spikes 2-3 weeks before Black Friday across multiple years, or that certain tech stocks show weakness during specific conference dates. The color-coding feature allows you to layer multiple event types simultaneously—perhaps using red for bearish catalysts and green for bullish ones—creating a visual heat map of historical market reactions.
The indicator's 6-month default spacing (covering 4.5 years) is strategically designed to capture multiple business cycles while maintaining clarity on the chart. This timeframe is long enough to identify genuine patterns rather than coincidences, yet focused enough to remain relevant to current market conditions.
Pro Tip: Combine this indicator with volume analysis or other technical indicators to validate whether the patterns you observe are accompanied by meaningful market participation or if they're statistical noise.
Trend Candles Full ColorThe coloring over the candle sticks isn't showing up on the picture for some reason but when you click on the indicator the color coding will appear on the chart.
Trend Candles Full Color Indicator Explanation The "Trend Candles Full Color" indicator, designed for TradingView, visually enhances candlestick charts by coloring candles based on their position relative to a simple moving average (SMA). Here's how it works and how it can benefit traders: How It Works Input : Adjust the SMA period (default is 20) to define the trend length.
Logic : The indicator compares the closing price of each candle to the SMA: Green Candle : Close is above the SMA (indicating an uptrend).
Red Candle : Close is below the SMA (indicating a downtrend).
Gray Candle : Close equals the SMA (neutral/no clear trend).
Output : Candles (body, wick, and border) are colored green, red, or gray based on the trend, overlaid directly on your price chart.
Benefits and Use Cases Trend-Following Strategies Benefit: Clearly identifies bullish (green) or bearish (red) trends, helping traders ride momentum.
Example: A swing trader using a 20-period SMA can enter long positions when candles turn green (price above SMA) and exit or short when candles turn red, confirming trend reversals.
Reversal Trading Benefit: Gray candles signal indecision near the SMA, often a precursor to reversals.
Example: A day trader might watch for gray candles after a prolonged uptrend (green candles) to anticipate a potential bearish reversal, combining with other indicators like RSI for confirmation.
Scalping Benefit: Quick visual cues for short-term trend changes on lower timeframes.
Example: A scalper on a 5-minute chart can use green candles to confirm quick bullish moves and red candles to avoid counter-trend trades, enhancing decision speed.
Position Sizing or Risk Management Benefit: Color changes highlight trend strength, aiding in adjusting trade size or stops.
Example: A trader might increase position size during strong green candle sequences (sustained uptrend) and tighten stops when gray candles appear, signaling potential trend weakness.
Tips for Use Adjust the MA Length to suit your trading style (e.g., shorter for scalping, longer for swing trading).
Combine with other indicators (e.g., support/resistance, MACD) for better accuracy.
Test on different timeframes to match your strategy.
Recommended MA Length for 1-Minute Charts Short-Term/Scalping (1-5 minute trades):10-period SMA : Very sensitive, ideal for capturing quick price movements in fast markets. May produce more noise (false signals).
20-period SMA : A balanced choice for 1-minute charts, smoothing minor fluctuations while reacting to short-term trends. A great starting point for scalpers.
Intraday Trend Trading (10-30 minute holds):50-period SMA : Captures broader intraday trends, reducing noise but lagging slightly. Suitable for larger moves within a session.
This indicator simplifies trend identification, making it a versatile tool for traders of all styles, from beginners to advanced users!
Recommended MA Length for Swing Trading / Higher Timeframes Swing Trading (holding trades for days to weeks):50-period SMA : A popular choice for swing traders on higher timeframes (e.g., 1-hour or 4-hour charts). It smooths out short-term fluctuations while identifying medium-term trends. Ideal for capturing multi-day swings.
100-period SMA : Slightly longer, this MA is great for confirming stronger, more sustained trends. It’s useful on 4-hour or daily charts for swing traders aiming to ride larger price moves.
Longer-Term Trend Trading (holding for weeks to months):200-period SMA : A classic choice for higher timeframes like daily or weekly charts. It highlights major market trends and is widely used by swing and position traders to filter out noise and focus on long-term direction.
150-period SMA : A middle ground between the 100 and 200 SMA, suitable for daily charts when you want a balance between responsiveness and trend reliability.
ATM Strike Line with Call & Put Premiums (ARJO)This indicator is designed specifically for the Indian market (NSE) and helps traders visualize the At-The-Money (ATM) strike line along with real-time Call (CE) and Put (PE) option premiums.
Key Features
Automatic ATM Detection: The script automatically identifies the ATM strike based on the underlying price, with an option for manual input.
Dynamic Expiry Control: Select expiry date easily (Year, Month, Day) in YYMMDD format.
Flexible Timeframe Support: Choose between the chart’s current timeframe or custom intervals.
Smart Symbol & Strike Interval: Automatically adapts to the selected underlying symbol (e.g., NIFTY, BANKNIFTY, RELIANCE, etc.) or allows manual setup.
Visual Representation:
ATM line plotted clearly on the chart.
CE and PE premium labels are displayed on each side of the ATM line.
ATM strike price label shown at the center.
Call–Put Volume Ratio (CPVR): Displays the live CPVR value to quickly assess market sentiment.
CPVR Interpretation
Bullish Bias: CPVR ≥ 1.25
Bearish Bias: CPVR ≤ 0.75
Neutral Zone: Between 0.75 and 1.25
⚙️ Customization
Adjustable colors for ATM line, CE/PE labels, and CPVR.
Option to manually select strike, symbol, and interval for maximum flexibility.
This tool may help to track option sentiment directly on the price chart, making it ideal for option traders and intraday analysts focusing on NIFTY, BANKNIFTY, and other NSE stocks.
Happy Trading. ARJO
London Open High/Low 9:00-9:15indicator marks out high and low of the first 15 minutes of the London session.
Relative Vigor Index with Divergence and SMA FilterThis script implements the Relative Vigor Index (RVI), originally developed by John Ehlers, enhanced with three practical analytical layers:
1. Configurable SMA filter applied to the RVI line (default: 14 periods) to smooth noise and clarify the underlying momentum trend.
2. Automated divergence detection between price action and the RVI oscillator, identifying both:
- Regular divergences ("R"): potential reversal signals (e.g., price makes a lower low while RVI makes a higher low).
- Hidden divergences ("H"): potential continuation signals (e.g., price makes a higher low while RVI makes a lower low).
3.Visual aids: labeled markers ("R"/"H") and connecting lines to make divergence patterns immediately recognizable.
Unlike basic RVI implementations, this version is designed to highlight momentum-price decoupling—a core concept in technical analysis—using robust pivot detection (`ta.pivotlow`/`ta.pivothigh`) with user-defined lookback and search ranges (default: 5–60 bars). The SMA filter helps traders distinguish between genuine momentum shifts and short-term volatility.
How it works:
- The RVI is calculated as the ratio of smoothed (close – open) to smoothed (high – low), reflecting the idea that in uptrends, closes tend to occur near highs, and in downtrends, near lows.
- Divergences are confirmed only when both a valid price pivot and a corresponding RVI pivot occur within the specified bar range.
- Hidden bearish divergences are disabled by default to reduce noise on shorter timeframes.
Suggested use:
- Use regular bullish divergences near negative RVI extremes as potential long setups.
- Watch for regular bearish divergences at positive RVI peaks as early reversal warnings.
- Combine with support/resistance or trend structure for higher-confidence entries.
This script is not a simple mashup: the integration of divergence logic with the RVI’s unique behavior, configurable sensitivity, and clean visualization provides a cohesive analytical tool that goes beyond standard implementations.
> Disclaimer: This script is for educational and informational purposes only. It does not constitute financial, investment, or trading advice. Past performance is not indicative of future results.
—
Credits:
- Relative Vigor Index concept: John Ehlers
- Divergence methodology: Standard technical analysis practice
- Implementation and enhancements: © Carlos Mauricio Vizcarra (2025)
- Licensed under MPL 2.0
Senkou Span AUse it in conjunction with Senkou Span B to create effective kumo alert signals when kumo changes direction: bullish or bearish.
CHOCH + FVG Signals [30m Optimized]CHOCH + FVG Signals
🎯 What It Does:
This script automatically scans your chart for high-probability Smart Money Concepts (SMC) setups based on two key institutional trading principles:
Change of Character (CHOCH) – A shift in market structure signaling potential reversal
Fair Value Gap (FVG) – An imbalance zone where price moved too fast, often acting as support/resistance
When both conditions align, the script plots clear Buy (▲) and Sell (▼) signals directly on your chart — ideal for intraday trading on the 30-minute timeframe (but works on any timeframe).
✅ Key Features:
🔹 Visual Fair Value Gaps
Green shaded zones = Bullish FVGs (potential support)
Red shaded zones = Bearish FVGs (potential resistance)
Toggle on/off in settings
🔹 Smart CHOCH Detection
Detects breaks of recent swing highs/lows with proper context
Avoids false signals by confirming prior price structure
🔹 Clear Trade Signals
Green ▲ below bar = Buy signal (Bullish CHOCH + FVG confluence)
Red ▼ above bar = Sell signal (Bearish CHOCH + FVG confluence)
🔹 Customizable Filters
Option to require FVG for a signal (recommended for higher accuracy)
Adjust sensitivity via swing detection settings (default optimized for 30m)
🔹 Alert-Ready
Built-in alert conditions for instant notifications on TradingView mobile/desktop
⚙️ How to Use:
Apply to a 30-minute chart (e.g., EURUSD, Gold, NAS100, BTC)
Wait for at least 50–100 bars to load (so swing points appear)
Look for:
A green triangle (▲) → consider long entry near FVG support
A red triangle (▼) → consider short entry near FVG resistance
Confirm with price action: Wait for a strong candle close or rejection at the FVG zone
Use stop-loss below/above the FVG and target recent liquidity pools
💡 Pro Tip: Best used during high-volume sessions (e.g., London Open 7–10 AM UTC, NY Open 12:30–3:30 PM UTC).
🛠️ Settings (Inputs):
Show Fair Value Gaps
✅ Enabled
Visualize FVG zones
Max FVG History
100 bars
Prevent chart clutter
Require FVG for Signal?
✅ Enabled
Higher-quality setups (disable to test CHOCH-only)
⚠️ Important Notes:
This is a signal generator, not financial advice. Always manage risk.
Works best in trending or breaking markets — avoid during low-volatility ranges.
FVGs may get filled (tested) before price continues — patience improves results.
Backtest on historical data before live trading.
📣 Ideal For:
Retail traders learning Smart Money Concepts (SMC)
Price action traders seeking institutional-level confluence
Intraday scalpers & swing traders on 30m–1H timeframes
Senkou Span BUsing in conjunction with Senkou Span A to create effective kumo alert signals when kumo changes direction: bullish or bearish.
Bitcoin Fair Price Calculator [bitcoinfairprice.com]1. Purpose of the scriptLong-term Bitcoin valuation based on historical time (days since Genesis block)
Fair Price = theoretically “fair” price according to power law.
Bottom Price = lower support (historically ~58% below Fair Price).
Daily display as on the website – without external access.
Buy/sell signals in case of strong overvaluation/undervaluation.
2. Mathematical model (original formula)pinescript
Bottom Price = Fair Price × 0.42
→ Corresponds historically to ~58% below Fair Price.
Days since Genesis block are calculated automatically per bar.
3. What is displayed in the chart?
Fair Price Average power law line (“fair price”) Blue
Bottom Price Lower support (“floor”) Green
Power Law Corridor Filled area between 0.1× and 2.5× Fair Price Light blue (transparent)
Table (top right) Daily values as on the website Black with white text
Label (for >20% deviation) Shows current prices + percentage Red (overvalued) / Green (undervalued)
4. Recommended use Timeframe
Recommendation Weekly / Monthly Best long-term signals
Daily Good balance
Log scale Be sure to activate! (Right-click on Y-axis → “Logarithmic scale”)
9. Strategy tips (based on the model)
Price near bottom --> Buy / accumulate
Price > 2.5× fair price --> Sell part of position / caution
Price between fair & bottom --> Strong buy zone
Deviation < -20% --> HODL signal
Translated with DeepL.com (free version)
COT IndexTHE HIDDEN INTELLIGENCE IN FUTURES MARKETS
What if you could see what the smartest players in the futures markets are doing before the crowd catches on? While retail traders chase momentum indicators and moving averages, obsess over Japanese candlestick patterns, and debate whether the RSI should be set to fourteen or twenty-one periods, institutional players leave footprints in the sand through their mandatory reporting to the Commodity Futures Trading Commission. These footprints, published weekly in the Commitment of Traders reports, have been hiding in plain sight for decades, available to anyone with an internet connection, yet remarkably few traders understand how to interpret them correctly. The COT Index indicator transforms this raw institutional positioning data into actionable trading signals, bringing Wall Street intelligence to your trading screen without requiring expensive Bloomberg terminals or insider connections.
The uncomfortable truth is this: Most retail traders operate in a binary world. Long or short. Buy or sell. They apply technical analysis to individual positions, constrained by limited capital that forces them to concentrate risk in single directional bets. Meanwhile, institutional traders operate in an entirely different dimension. They manage portfolios dynamically weighted across multiple markets, adjusting exposure based on evolving market conditions, correlation shifts, and risk assessments that retail traders never see. A hedge fund might be simultaneously long gold, short oil, neutral on copper, and overweight agricultural commodities, with position sizes calibrated to volatility and portfolio Greeks. When they increase gold exposure from five percent to eight percent of portfolio allocation, this rebalancing decision reflects sophisticated analysis of opportunity cost, risk parity, and cross-market dynamics that no individual chart pattern can capture.
This portfolio reweighting activity, multiplied across hundreds of institutional participants, manifests in the aggregate positioning data published weekly by the CFTC. The Commitment of Traders report does not show individual trades or strategies. It shows the collective footprint of how actual commercial hedgers and large speculators have allocated their capital across different markets. When mining companies collectively increase forward gold sales to hedge thirty percent more production than last quarter, they are not reacting to a moving average crossover. They are making strategic allocation decisions based on production forecasts, cost structures, and price expectations derived from operational realities invisible to outside observers. This is portfolio management in action, revealed through positioning data rather than price charts.
If you want to understand how institutional capital actually flows, how sophisticated traders genuinely position themselves across market cycles, the COT report provides a rare window into that hidden world. But understand what you are getting into. This is not a tool for scalpers seeking confirmation of the next five-minute move. This is not an oscillator that flashes oversold at market bottoms with convenient precision. COT analysis operates on a timescale measured in weeks and months, revealing positioning shifts that precede major market turns but offer no precision timing. The data arrives three days stale, published only once per week, capturing strategic positioning rather than tactical entries.
If you need instant gratification, if you trade intraday moves, if you demand mechanical signals with ninety percent accuracy, close this document now. COT analysis rewards patience, position sizing discipline, and tolerance for being early. It punishes impatience, overleveraging, and the expectation that any single indicator can substitute for market understanding.
The premise is deceptively simple. Every Tuesday, large traders in futures markets must report their positions to the CFTC. By Friday afternoon, this data becomes public. Academic research spanning three decades has consistently shown that not all market participants are created equal. Some traders consistently profit while others consistently lose. Some anticipate major turning points while others chase trends into exhaustion. Bessembinder and Chan (1992) demonstrated in their seminal study that commercial hedgers, those with actual exposure to the underlying commodity or financial instrument, possess superior forecasting ability compared to speculators. Their research, published in the Journal of Finance, found statistically significant predictive power in commercial positioning, particularly at extreme levels. This finding challenged the efficient market hypothesis and opened the door to a new approach to market analysis based on positioning rather than price alone.
Think about what this means. Every week, the government publishes a report showing you exactly how the most informed market participants are positioned. Not their opinions. Not their predictions. Their actual money at risk. When agricultural producers collectively hold their largest short hedge in five years, they are not making idle speculation. They are locking in prices for crops they will harvest, informed by private knowledge of weather conditions, soil quality, inventory levels, and demand expectations invisible to outside observers. When energy companies aggressively hedge forward production at current prices, they reveal information about expected supply that no analyst report can capture. This is not technical analysis based on past prices. This is not fundamental analysis based on publicly available data. This is behavioral analysis based on how the smartest money is actually positioned, how institutions allocate capital across portfolios, and how those allocation decisions shift as market conditions evolve.
WHY SOME TRADERS KNOW MORE THAN OTHERS
Building on this foundation, Sanders, Boris and Manfredo (2004) conducted extensive research examining the behaviour patterns of different trader categories. Their work, which analyzed over a decade of COT data across multiple commodity markets, revealed a fascinating dynamic that challenges much of what retail traders are taught. Commercial hedgers consistently positioned themselves against market extremes, buying when speculators were most bearish and selling when speculators reached peak bullishness. The contrarian positioning of commercials was not random noise but rather reflected their superior information about supply and demand fundamentals. Meanwhile, large speculators, primarily hedge funds and commodity trading advisors, exhibited strong trend-following behaviour that often amplified market moves beyond fundamental values. Small traders, the retail participants, consistently entered positions late in trends, frequently near turning points, making them reliable contrary indicators.
Wang (2003) extended this research by demonstrating that the predictive power of commercial positioning varies significantly across different commodity sectors. His analysis of agricultural commodities showed particularly strong forecasting ability, with commercial net positions explaining up to fifteen percent of return variance in subsequent weeks. This finding suggests that the informational advantages of hedgers are most pronounced in markets where physical supply and demand fundamentals dominate, as opposed to purely financial markets where information asymmetries are smaller. When a corn farmer hedges six months of expected harvest, that decision incorporates private observations about rainfall patterns, crop health, pest pressure, and local storage capacity that no distant analyst can match. When an oil refinery hedges crude oil purchases and gasoline sales simultaneously, the spread relationships reveal expectations about refining margins that reflect operational realities invisible in public data.
The theoretical mechanism underlying these empirical patterns relates to information asymmetry and different participant motivations. Commercial hedgers engage in futures markets not for speculative profit but to manage business risks. An agricultural producer selling forward six months of expected harvest is not making a bet on price direction but rather locking in revenue to facilitate financial planning and ensure business viability. However, this hedging activity necessarily incorporates private information about expected supply, inventory levels, weather conditions, and demand trends that the hedger observes through their commercial operations (Irwin and Sanders, 2012). When aggregated across many participants, this private information manifests in collective positioning.
Consider a gold mining company deciding how much forward production to hedge. Management must estimate ore grades, recovery rates, production costs, equipment reliability, labor availability, and dozens of other operational variables that determine whether locking in prices at current levels makes business sense. If the industry collectively hedges more aggressively than usual, it suggests either exceptional production expectations or concern about sustaining current price levels or combination of both. Either way, this positioning reveals information unavailable to speculators analyzing price charts and economic data. The hedger sees the physical reality behind the financial abstraction.
Large speculators operate under entirely different incentives and constraints. Commodity Trading Advisors managing billions in assets typically employ systematic, trend-following strategies that respond to price momentum rather than fundamental supply and demand. When crude oil rallies from sixty dollars to seventy dollars per barrel, these systems generate buy signals. As the rally continues to eighty dollars, position sizes increase. The strategy works brilliantly during sustained trends but becomes a liability at reversals. By the time oil reaches ninety dollars, trend-following funds are maximally long, having accumulated positions progressively throughout the rally. At this point, they represent not smart money anticipating further gains but rather crowded money vulnerable to reversal. Sanders, Boris and Manfredo (2004) documented this pattern across multiple energy markets, showing that extreme speculator positioning typically marked late-stage trend exhaustion rather than early-stage trend development.
Small traders, the retail participants who fall below reporting thresholds, display the weakest forecasting ability. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns, meaning their aggregate positioning served as a reliable contrary indicator. The explanation combines several factors. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, entering trends after mainstream media coverage when institutional participants are preparing to exit. Perhaps most importantly, they trade with emotion, buying into euphoria and selling into panic at precisely the wrong times.
At major turning points, the three groups often position opposite each other with commercials extremely bearish, large speculators extremely bullish, and small traders piling into longs at the last moment. These high-divergence environments frequently precede increased volatility and trend reversals. The insiders with business exposure quietly exit as the momentum traders hit maximum capacity and retail enthusiasm peaks. Within weeks, the reversal begins, and positions unwind in the opposite sequence.
FROM RAW DATA TO ACTIONABLE SIGNALS
The COT Index indicator operationalizes these academic findings into a practical trading tool accessible through TradingView. At its core, the indicator normalizes net positioning data onto a zero to one hundred scale, creating what we call the COT Index. This normalization is critical because absolute position sizes vary dramatically across different futures contracts and over time. A commercial trader holding fifty thousand contracts net long in crude oil might be extremely bullish by historical standards, or it might be quite neutral depending on the context of total market size and historical ranges. Raw position numbers mean nothing without context. The COT Index solves this problem by calculating where current positioning stands relative to its range over a specified lookback period, typically two hundred fifty-two weeks or approximately five years of weekly data.
The mathematical transformation follows the methodology originally popularized by legendary trader Larry Williams, though the underlying concept appears in statistical normalization techniques across many fields. For any given trader category, we calculate the highest and lowest net position values over the lookback period, establishing the historical range for that specific market and trader group. Current positioning is then expressed as a percentage of this range, where zero represents the most bearish positioning ever seen in the lookback window and one hundred represents the most bullish extreme. A reading of fifty indicates positioning exactly in the middle of the historical range, suggesting neither extreme optimism nor pessimism relative to recent history (Williams and Noseworthy, 2009).
This index-based approach allows for meaningful comparison across different markets and time periods, overcoming the scaling problems inherent in analyzing raw position data. A commercial index reading of eighty-five in gold carries the same interpretive meaning as an eighty-five reading in wheat or crude oil, even though the absolute position sizes differ by orders of magnitude. This standardization enables systematic analysis across entire futures portfolios rather than requiring market-specific expertise for each contract.
The lookback period selection involves a fundamental tradeoff between responsiveness and stability. Shorter lookback periods, perhaps one hundred twenty-six weeks or approximately two and a half years, make the index more sensitive to recent positioning changes. However, it also increases noise and produces more false signals. Longer lookback periods, perhaps five hundred weeks or approximately ten years, create smoother readings that filter short-term noise but become slower to recognize regime changes. The indicator settings allow users to adjust this parameter based on their trading timeframe, risk tolerance, and market characteristics.
UNDERSTANDING CFTC DATA STRUCTURES
The indicator supports both Legacy and Disaggregated COT report formats, reflecting the evolution of CFTC reporting standards over decades of market development. Legacy reports categorize market participants into three broad groups: commercial traders (hedgers with underlying business exposure), non-commercial traders (large speculators seeking profit without commercial interest), and non-reportable traders (small speculators below reporting thresholds). Each category brings distinct motivations and information advantages to the market (CFTC, 2020).
The Disaggregated reports, introduced in September 2009 for physical commodity markets, provide finer granularity by splitting participants into five categories (CFTC, 2009). Producer and merchant positions capture those actually producing, processing, or merchandising the physical commodity. Swap dealers represent financial intermediaries facilitating derivative transactions for clients. Managed money includes commodity trading advisors and hedge funds executing systematic or discretionary strategies. Other reportables encompasses diverse participants not fitting the main categories. Small traders remain as the fifth group, representing retail participation.
This enhanced categorization reveals nuances invisible in Legacy reports, particularly distinguishing between different types of institutional capital and their distinct behavioural patterns. The indicator automatically detects which report type is appropriate for each futures contract and adjusts the display accordingly.
Importantly, Disaggregated reports exist only for physical commodity futures. Agricultural commodities like corn, wheat, and soybeans have Disaggregated reports because clear producer, merchant, and swap dealer categories exist. Energy commodities like crude oil and natural gas similarly have well-defined commercial hedger categories. Metals including gold, silver, and copper also receive Disaggregated treatment (CFTC, 2009). However, financial futures such as equity index futures, Treasury bond futures, and currency futures remain available only in Legacy format. The CFTC has indicated no plans to extend Disaggregated reporting to financial futures due to different market structures and participant categories in these instruments (CFTC, 2020).
THE BEHAVIORAL FOUNDATION
Understanding which trader perspective to follow requires appreciation of their distinct trading styles, success rates, and psychological profiles. Commercial hedgers exhibit anticyclical behaviour rooted in their fundamental knowledge and business imperatives. When agricultural producers hedge forward sales during harvest season, they are not speculating on price direction but rather locking in revenue for crops they will harvest. Their business requires converting volatile commodity exposure into predictable cash flows to facilitate planning and ensure survival through difficult periods. Yet their aggregate positioning reveals valuable information because these hedging decisions incorporate private information about supply conditions, inventory levels, weather observations, and demand expectations that hedgers observe through their commercial operations (Bessembinder and Chan, 1992).
Consider a practical example from energy markets. Major oil companies continuously hedge portions of forward production based on price levels, operational costs, and financial planning needs. When crude oil trades at ninety dollars per barrel, they might aggressively hedge the next twelve months of production, locking in prices that provide comfortable profit margins above their extraction costs. This hedging appears as short positioning in COT reports. If oil rallies further to one hundred dollars, they hedge even more aggressively, viewing these prices as exceptional opportunities to secure revenue. Their short positioning grows increasingly extreme. To an outside observer watching only price charts, the rally suggests bullishness. But the commercial positioning reveals that the actual producers of oil find these prices attractive enough to lock in years of sales, suggesting skepticism about sustaining even higher levels. When the eventual reversal occurs and oil declines back to eighty dollars, the commercials who hedged at ninety and one hundred dollars profit while speculators who chased the rally suffer losses.
Large speculators or managed money traders operate under entirely different incentives and constraints. Their systematic, momentum-driven strategies mean they amplify existing trends rather than anticipate reversals. Trend-following systems, the most common approach among large speculators, by definition require confirmation of trend through price momentum before entering positions (Sanders, Boris and Manfredo, 2004). When crude oil rallies from sixty dollars to eighty dollars per barrel over several months, trend-following algorithms generate buy signals based on moving average crossovers, breakouts, and other momentum indicators. As the rally continues, position sizes increase according to the systematic rules.
However, this approach becomes a liability at turning points. By the time oil reaches ninety dollars after a sustained rally, trend-following funds are maximally long, having accumulated positions progressively throughout the move. At this point, their positioning does not predict continued strength. Rather, it often marks late-stage trend exhaustion. The psychological and mechanical explanation is straightforward. Trend followers by definition chase price momentum, entering positions after trends establish rather than anticipating them. Eventually, they become fully invested just as the trend nears completion, leaving no incremental buying power to sustain the rally. When the first signs of reversal appear, systematic stops trigger, creating a cascade of selling that accelerates the downturn.
Small traders consistently display the weakest track record across academic studies. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns in his analysis across multiple commodity markets. This result means that whatever small traders collectively do, the opposite typically proves profitable. The explanation for small trader underperformance combines several factors documented in behavioral finance literature. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, learning about commodity trends through mainstream media coverage that arrives after institutional participants have already positioned. Perhaps most importantly, retail traders are more susceptible to emotional decision-making, buying into euphoria and selling into panic at precisely the wrong times (Tharp, 2008).
SETTINGS, THRESHOLDS, AND SIGNAL GENERATION
The practical implementation of the COT Index requires understanding several key features and settings that users can adjust to match their trading style, timeframe, and risk tolerance. The lookback period determines the time window for calculating historical ranges. The default setting of two hundred fifty-two bars represents approximately one year on daily charts or five years on weekly charts, balancing responsiveness with stability. Conservative traders seeking only the most extreme, highest-probability signals might extend the lookback to five hundred bars or more. Aggressive traders seeking earlier entry and willing to accept more false positives might reduce it to one hundred twenty-six bars or even less for shorter-term applications.
The bullish and bearish thresholds define signal generation levels. Default settings of eighty and twenty respectively reflect academic research suggesting meaningful information content at these extremes. Readings above eighty indicate positioning in the top quintile of the historical range, representing genuine extremes rather than temporary fluctuations. Conversely, readings below twenty occupy the bottom quintile, indicating unusually bearish positioning (Briese, 2008).
However, traders must recognize that appropriate thresholds vary by market, trader category, and personal risk tolerance. Some futures markets exhibit wider positioning swings than others due to seasonal patterns, volatility characteristics, or participant behavior. Conservative traders seeking high-probability setups with fewer signals might raise thresholds to eighty-five and fifteen. Aggressive traders willing to accept more false positives for earlier entry could lower them to seventy-five and twenty-five.
The key is maintaining meaningful differentiation between bullish, neutral, and bearish zones. The default settings of eighty and twenty create a clear three-zone structure. Readings from zero to twenty represent bearish territory where the selected trader group holds unusually bearish positions. Readings from twenty to eighty represent neutral territory where positioning falls within normal historical ranges. Readings from eighty to one hundred represent bullish territory where the selected trader group holds unusually bullish positions.
The trading perspective selection determines which participant group the indicator follows, fundamentally shaping interpretation and signal meaning. For counter-trend traders seeking reversal opportunities, monitoring commercial positioning makes intuitive sense based on the academic research discussed earlier. When commercials reach extreme bearish readings below twenty, indicating unprecedented short positioning relative to recent history, they are effectively betting against the crowd. Given their informational advantages demonstrated by Bessembinder and Chan (1992), this contrarian stance often precedes major bottoms.
Trend followers might instead monitor large speculator positioning, but with inverted logic compared to commercials. When managed money reaches extreme bullish readings above eighty, the trend may be exhausting rather than accelerating. This seeming paradox reflects their late-cycle participation documented by Sanders, Boris and Manfredo (2004). Sophisticated traders thus use speculator extremes as fade signals, entering positions opposite to speculator consensus.
Small trader monitoring serves primarily as a contrary indicator for all trading styles. Extreme small trader bullishness above seventy-five or eighty typically warns of retail FOMO at market tops. Extreme small trader bearishness below twenty or twenty-five often marks capitulation bottoms where the last weak hands have sold.
VISUALIZATION AND USER INTERFACE
The visual design incorporates multiple elements working together to facilitate decision-making and maintain situational awareness during active trading. The primary COT Index line plots in bold with adjustable line width, defaulting to two pixels for clear visibility against busy price charts. An optional glow effect, controlled by a simple toggle, adds additional visual prominence through multiple plot layers with progressively increasing transparency and width.
A twenty-one period exponential moving average overlays the index line, providing trend context for positioning changes. When the index crosses above its moving average, it signals accelerating bullish sentiment among the selected trader group regardless of whether absolute positioning is extreme. Conversely, when the index crosses below its moving average, it signals deteriorating sentiment and potentially the beginning of a reversal in positioning trends.
The EMA provides a dynamic reference line for assessing positioning momentum. When the index trades far above its EMA, positioning is not only extreme in absolute terms but also building with momentum. When the index trades far below its EMA, positioning is contracting or reversing, which may indicate weakening conviction even if absolute levels remain elevated.
The data table positioned at the top right of the chart displays eleven metrics for each trader category, transforming the indicator from a simple index calculation into an analytical dashboard providing multidimensional market intelligence. Beyond the COT Index itself, users can monitor positioning extremity, which measures how unusual current levels are compared to historical norms using statistical techniques. The extremity metric clarifies whether a reading represents the ninety-fifth or ninety-ninth percentile, with values above two standard deviations indicating genuinely exceptional positioning.
Market power quantifies each group's influence on total open interest. This metric expresses each trader category's net position as a percentage of total market open interest. A commercial entity holding forty percent of total open interest commands significantly more influence than one holding five percent, making their positioning signals more meaningful.
Momentum and rate of change metrics reveal whether positions are building or contracting, providing early warning of potential regime shifts. Position velocity measures the rate of change in positioning changes, effectively a second derivative providing even earlier insight into inflection points.
Sentiment divergence highlights disagreements between commercial and speculative positioning. This metric calculates the absolute difference between normalized commercial and large speculator index values. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals.
The table also displays concentration metrics when available, showing how positioning is distributed among the largest handful of traders in each category. High concentration indicates a few dominant players controlling most of the positioning, while low concentration suggests broad-based participation across many traders.
THE ALERT SYSTEM AND MONITORING
The alert system, comprising five distinct alert conditions, enables systematic monitoring of dozens of futures markets without constant screen watching. The bullish and bearish COT signal alerts trigger when the index crosses user-defined thresholds, indicating the selected trader group has reached extreme positioning worthy of attention. These alerts fire in real-time as new weekly COT data publishes, typically Friday afternoon following the Tuesday measurement date.
Extreme positioning alerts fire at ninety and ten index levels, representing the top and bottom ten percent of the historical range, warning of particularly stretched readings that historically precede reversals with high probability. When commercials reach a COT Index reading below ten, they are expressing their most bearish stance in the entire lookback period.
The data staleness alert notifies users when COT reports have not updated for more than ten days, preventing reliance on outdated information for trading decisions. Government shutdowns or federal holidays can interrupt the normal Friday publication schedule. Using stale signals while believing them current creates dangerous false confidence.
The indicator's watermark information display positioned in the bottom right corner provides essential context at a glance. This persistent display shows the symbol and timeframe, the COT report date timestamp, days since last update, and the current signal state. A trader analyzing a potential short entry in crude oil can glance at the watermark to instantly confirm positioning context without interrupting analysis flow.
LIMITATIONS AND REALISTIC EXPECTATIONS
Practical application requires understanding both the indicator's considerable strengths and inherent limitations. COT data inherently lags price action by three days, as Tuesday positions are not published until Friday afternoon. This delay means the indicator cannot catch rapid intraday reversals or respond to surprise news events. Traders using the COT Index for timing entries must accept this latency and focus on swing trading and position trading timeframes where three-day lags matter less than in day trading or scalping.
The weekly publication schedule similarly makes the indicator unsuitable for short-term trading strategies requiring immediate feedback. The COT Index works best for traders operating on weekly or longer timeframes, where positioning shifts measured in weeks and months align with trading horizon.
Extreme COT readings can persist far longer than typical technical indicators suggest, testing the patience and capital reserves of traders attempting to fade them. When crude oil enters a sustained bull market driven by genuine supply disruptions, commercial hedgers may maintain bearish positioning for many months as prices grind higher. A commercial COT Index reading of fifteen indicating extreme bearishness might persist for three months while prices continue rallying before finally reversing. Traders without sufficient capital and risk tolerance to weather such drawdowns will exit prematurely, precisely when the signal is about to work (Irwin and Sanders, 2012).
Position sizing discipline becomes paramount when implementing COT-based strategies. Rather than risking large percentages of capital on individual signals, successful COT traders typically allocate modest position sizes across multiple signals, allowing some to take time to mature while others work more quickly.
The indicator also cannot overcome fundamental regime changes that alter the structural drivers of markets. If gold enters a true secular bull market driven by monetary debasement, commercial hedgers may remain persistently bearish as mining companies sell forward years of production at what they perceive as favorable prices. Their positioning indicates valuation concerns from a production cost perspective, but cannot stop prices from rising if investment demand overwhelms physical supply-demand balance.
Similarly, structural changes in market participation can alter the meaning of positioning extremes. The growth of commodity index investing in the two thousands brought massive passive long-only capital into futures markets, fundamentally changing typical positioning ranges. Traders relying on COT signals without recognizing this regime change would have generated numerous false bearish signals during the commodity supercycle from 2003 to 2008.
The research foundation supporting COT analysis derives primarily from commodity markets where the commercial hedger information advantage is most pronounced. Studies specifically examining financial futures like equity indices and bonds show weaker but still present effects. Traders should calibrate expectations accordingly, recognizing that COT analysis likely works better for crude oil, natural gas, corn, and wheat than for the S&P 500, Treasury bonds, or currency futures.
Another important limitation involves the reporting threshold structure. Not all market participants appear in COT data, only those holding positions above specified minimums. In markets dominated by a few large players, concentration metrics become critical for proper interpretation. A single large trader accounting for thirty percent of commercial positioning might skew the entire category if their individual circumstances are idiosyncratic rather than representative.
GOLD FUTURES DURING A HYPOTHETICAL MARKET CYCLE
Consider a practical example using gold futures during a hypothetical but realistic market scenario that illustrates how the COT Index indicator guides trading decisions through a complete market cycle. Suppose gold has rallied from fifteen hundred to nineteen hundred dollars per ounce over six months, driven by inflation concerns following aggressive monetary expansion, geopolitical uncertainty, and sustained buying by Asian central banks for reserve diversification.
Large speculators, operating primarily trend-following strategies, have accumulated increasingly bullish positions throughout this rally. Their COT Index has climbed progressively from forty-five to eighty-five. The table display shows that large speculators now hold net long positions representing thirty-two percent of total open interest, their highest in four years. Momentum indicators show positive readings, indicating positions are still building though at a decelerating rate. Position velocity has turned negative, suggesting the pace of position building is slowing.
Meanwhile, commercial hedgers have responded to the rally by aggressively selling forward production and inventory. Their COT Index has moved inversely to price, declining from fifty-five to twenty. This bearish commercial positioning represents mining companies locking in forward sales at prices they view as attractive relative to production costs. The table shows commercials now hold net short positions representing twenty-nine percent of total open interest, their most bearish stance in five years. Concentration metrics indicate this positioning is broadly distributed across many commercial entities, suggesting the bearish stance reflects collective industry view rather than idiosyncratic positioning by a single firm.
Small traders, attracted by mainstream financial media coverage of gold's impressive rally, have recently piled into long positions. Their COT Index has jumped from forty-five to seventy-eight as retail investors chase the trend. Television financial networks feature frequent segments on gold with bullish guests. Internet forums and social media show surging retail interest. This retail enthusiasm historically marks late-stage trend development rather than early opportunity.
The COT Index indicator, configured to monitor commercial positioning from a contrarian perspective, displays a clear bearish signal given the extreme commercial short positioning. The table displays multiple confirming metrics: positioning extremity shows commercials at the ninety-sixth percentile of bearishness, market power indicates they control twenty-nine percent of open interest, and sentiment divergence registers sixty-five, indicating massive disagreement between commercial hedgers and large speculators. This divergence, the highest in three years, places the market in the historically high-risk category for reversals.
The interpretation requires nuance and consideration of context beyond just COT data. Commercials are not necessarily predicting an imminent crash. Rather, they are hedging business operations at what they collectively view as favorable price levels. However, the data reveals they have sold unusually large quantities of forward production, suggesting either exceptional production expectations for the year ahead or concern about sustaining current price levels or combination of both. Combined with extreme speculator positioning indicating a crowded long trade, and small trader enthusiasm confirming retail FOMO, the confluence suggests elevated reversal risk even if the precise timing remains uncertain.
A prudent trader analyzing this situation might take several actions based on COT Index signals. Existing long positions could be tightened with closer stop losses. Profit-taking on a portion of long exposure could lock in gains while maintaining some participation. Some traders might initiate modest short positions as portfolio hedges, sizing them appropriately for the inherent uncertainty in timing reversals. Others might simply move to the sidelines, avoiding new long entries until positioning normalizes.
The key lesson from case study analysis is that COT signals provide probabilistic edges rather than deterministic predictions. They work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five percent win rate with proper risk management produces substantial profits over time, yet still means forty-five percent of signals will be premature or wrong. Traders must embrace this probabilistic reality rather than seeking the impossible goal of perfect accuracy.
INTEGRATION WITH TRADING SYSTEMS
Integration with existing trading systems represents a natural and powerful use case for COT analysis, adding a positioning dimension to price-based technical approaches or fundamental analytical frameworks. Few traders rely exclusively on a single indicator or methodology. Rather, they build systems that synthesize multiple information sources, with each component addressing different aspects of market behavior.
Trend followers might use COT extremes as regime filters, modifying position sizing or avoiding new trend entries when positioning reaches levels historically associated with reversals. Consider a classic trend-following system based on moving average crossovers and momentum breakouts. Integration of COT analysis adds nuance. When large speculator positioning exceeds ninety or commercial positioning falls below ten, the regime filter recognizes elevated reversal risk. The system might reduce position sizing by fifty percent for new signals during these high-risk periods (Kaufman, 2013).
Mean reversion traders might require COT signal confluence before fading extended moves. When crude oil becomes technically overbought and large speculators show extreme long positioning above eighty-five, both signals confirm. If only technical indicators show extremes while positioning remains neutral, the potential short signal is rejected, avoiding fades of trends with underlying institutional support (Kaufman, 2013).
Discretionary traders can monitor the indicator as a continuous awareness tool, informing bias and position sizing without dictating mechanical entries and exits. A discretionary trader might notice commercial positioning shifting from neutral to progressively more bullish over several months. This trend informs growing positive bias even without triggering mechanical signals.
Multi-timeframe analysis represents another powerful integration approach. A trader might use daily charts for trade execution and timing while monitoring weekly COT positioning for strategic context. When both timeframes align, highest-probability opportunities emerge.
Portfolio construction for futures traders can incorporate COT signals as an additional selection criterion. Markets showing strong technical setups AND favorable COT positioning receive highest allocations. Markets with strong technicals but neutral or unfavorable positioning receive reduced allocations.
ADVANCED METRICS AND INTERPRETATION
The metrics table transforms simple positioning data into multidimensional market intelligence. Position extremity, calculated as the absolute deviation from the historical mean normalized by standard deviation, helps identify truly unusual readings versus routine fluctuations. A reading above two standard deviations indicates ninety-fifth percentile or higher extremity. Above three standard deviations indicates ninety-ninth percentile or higher, genuinely rare positioning that historically precedes major events with high probability.
Market power, expressed as a percentage of total open interest, reveals whose positioning matters most from a mechanical market impact perspective. Consider two scenarios in gold futures. In scenario one, commercials show a COT Index reading of fifteen while their market power metric shows they hold net shorts representing thirty-five percent of open interest. This is a high-confidence bearish signal. In scenario two, commercials also show a reading of fifteen, but market power shows only eight percent. While positioning is extreme relative to this category's normal range, their limited market share means less mechanical influence on price.
The rate of change and momentum metrics highlight whether positions are accelerating or decelerating, often providing earlier warnings than absolute levels alone. A COT Index reading of seventy-five with rapidly building momentum suggests continued movement toward extremes. Conversely, a reading of eighty-five with decelerating or negative momentum indicates the positioning trend is exhausting.
Position velocity measures the rate of change in positioning changes, effectively a second derivative. When velocity shifts from positive to negative, it indicates that while positioning may still be growing, the pace of growth is slowing. This deceleration often precedes actual reversal in positioning direction by several weeks.
Sentiment divergence calculates the absolute difference between normalized commercial and large speculator index values. When commercials show extreme bearish positioning at twenty while large speculators show extreme bullish positioning at eighty, the divergence reaches sixty, representing near-maximum disagreement. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals. The mechanism is intuitive. Extreme divergence indicates the informed hedgers and momentum-following speculators have positioned opposite each other with conviction. One group will prove correct and profit while the other proves incorrect and suffers losses. The resolution of this disagreement through price movement often involves volatility.
The table also displays concentration metrics when available. High concentration indicates a few dominant players controlling most of the positioning within a category, while low concentration suggests broad-based participation. Broad-based positioning more reliably reflects collective market intelligence and industry consensus. If mining companies globally all independently decide to hedge aggressively at similar price levels, it suggests genuine industry-wide view about price valuations rather than circumstances specific to one firm.
DATA QUALITY AND RELIABILITY
The CFTC has maintained COT reporting in various forms since the nineteen twenties, providing nearly a century of positioning data across multiple market cycles. However, data quality and reporting standards have evolved substantially over this long period. Modern electronic reporting implemented in the late nineteen nineties and early two thousands significantly improved accuracy and timeliness compared to earlier paper-based systems.
Traders should understand that COT reports capture positions as of Tuesday's close each week. Markets remain open three additional days before publication on Friday afternoon, meaning the reported data is three days stale when received. During periods of rapid market movement or major news events, this lag can be significant. The indicator addresses this limitation by including timestamp information and staleness warnings.
The three-day lag creates particular challenges during extreme volatility episodes. Flash crashes, surprise central bank interventions, geopolitical shocks, and other high-impact events can completely transform market positioning within hours. Traders must exercise judgment about whether reported positioning remains relevant given intervening events.
Reporting thresholds also mean that not all market participants appear in disaggregated COT data. Traders holding positions below specified minimums aggregate into the non-reportable or small trader category. This aggregation affects different markets differently. In highly liquid contracts like crude oil with thousands of participants, reportable traders might represent seventy to eighty percent of open interest. In thinly traded contracts with only dozens of active participants, a few large reportable positions might represent ninety-five percent of open interest.
Another data quality consideration involves trader classification into categories. The CFTC assigns traders to commercial or non-commercial categories based on reported business purpose and activities. However, this process is not perfect. Some entities engage in both commercial and speculative activities, creating ambiguity about proper classification. The transition to Disaggregated reports attempted to address some of these ambiguities by creating more granular categories.
COMPARISON WITH ALTERNATIVE APPROACHES
Several alternative approaches to COT analysis exist in the trading community beyond the normalization methodology employed by this indicator. Some analysts focus on absolute position changes week-over-week rather than index-based normalization. This approach calculates the change in net positioning from one week to the next. The emphasis falls on momentum in positioning changes rather than absolute levels relative to history. This method potentially identifies regime shifts earlier but sacrifices cross-market comparability (Briese, 2008).
Other practitioners employ more complex statistical transformations including percentile rankings, z-score standardization, and machine learning classification algorithms. Ruan and Zhang (2018) demonstrated that machine learning models applied to COT data could achieve modest improvements in forecasting accuracy compared to simple threshold-based approaches. However, these gains came at the cost of interpretability and implementation complexity.
The COT Index indicator intentionally employs a relatively straightforward normalization methodology for several important reasons. First, transparency enhances user understanding and trust. Traders can verify calculations manually and develop intuitive feel for what different readings mean. Second, academic research suggests that most of the predictive power in COT data comes from extreme positioning levels rather than subtle patterns requiring complex statistical methods to detect. Third, robust methods that work consistently across many markets and time periods tend to be simpler rather than more complex, reducing the risk of overfitting to historical data. Fourth, the complexity costs of implementation matter for retail traders without programming teams or computational infrastructure.
PSYCHOLOGICAL ASPECTS OF COT TRADING
Trading based on COT data requires psychological fortitude that differs from momentum-based approaches. Contrarian positioning signals inherently mean betting against prevailing market sentiment and recent price action. When commercials reach extreme bearish positioning, prices have typically been rising, sometimes for extended periods. The price chart looks bullish, momentum indicators confirm strength, moving averages align positively. The COT signal says bet against all of this. This psychological difficulty explains why COT analysis remains underutilized relative to trend-following methods.
Human psychology strongly predisposes us toward extrapolation and recency bias. When prices rally for months, our pattern-matching brains naturally expect continued rally. The recent price action dominates our perception, overwhelming rational analysis about positioning extremes and historical probabilities. The COT signal asking us to sell requires overriding these powerful psychological impulses.
The indicator design attempts to support the required psychological discipline through several features. Clear threshold markers and signal states reduce ambiguity about when signals trigger. When the commercial index crosses below twenty, the signal is explicit and unambiguous. The background shifts to red, the signal label displays bearish, and alerts fire. This explicitness helps traders act on signals rather than waiting for additional confirmation that may never arrive.
The metrics table provides analytical justification for contrarian positions, helping traders maintain conviction during inevitable periods of adverse price movement. When a trader enters short positions based on extreme commercial bearish positioning but prices continue rallying for several weeks, doubt naturally emerges. The table display provides reassurance. Commercial positioning remains extremely bearish. Divergence remains high. The positioning thesis remains intact even though price action has not yet confirmed.
Alert functionality ensures traders do not miss signals due to inattention while also not requiring constant monitoring that can lead to emotional decision-making. Setting alerts for COT extremes enables a healthier relationship with markets. When meaningful signals occur, alerts notify them. They can then calmly assess the situation and execute planned responses.
However, no indicator design can completely overcome the psychological difficulty of contrarian trading. Some traders simply cannot maintain short positions while prices rally. For these traders, COT analysis might be better employed as an exit signal for long positions rather than an entry signal for shorts.
Ultimately, successful COT trading requires developing comfort with probabilistic thinking rather than certainty-seeking. The signals work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five or sixty percent win rate with proper risk management produces substantial profits over years, yet still means forty to forty-five percent of signals will be premature or wrong. COT analysis provides genuine edge, but edge means probability advantage, not elimination of losing trades.
EDUCATIONAL RESOURCES AND CONTINUOUS LEARNING
The indicator provides extensive built-in educational resources through its documentation, detailed tooltips, and transparent calculations. However, mastering COT analysis requires study beyond any single tool or resource. Several excellent resources provide valuable extensions of the concepts covered in this guide.
Books and practitioner-focused monographs offer accessible entry points. Stephen Briese published The Commitments of Traders Bible in two thousand eight, offering detailed breakdowns of how different markets and trader categories behave (Briese, 2008). Briese's work stands out for its empirical focus and market-specific insights. Jack Schwager includes discussion of COT analysis within the broader context of market behavior in his book Market Sense and Nonsense (Schwager, 2012). Perry Kaufman's Trading Systems and Methods represents perhaps the most rigorous practitioner-focused text on systematic trading approaches including COT analysis (Kaufman, 2013).
Academic journal articles provide the rigorous statistical foundation underlying COT analysis. The Journal of Futures Markets regularly publishes research on positioning data and its predictive properties. Bessembinder and Chan's earlier work on systematic risk, hedging pressure, and risk premiums in futures markets provides theoretical foundation (Bessembinder, 1992). Chang's examination of speculator returns provides historical context (Chang, 1985). Irwin and Sanders provide essential skeptical perspective in their two thousand twelve article (Irwin and Sanders, 2012). Wang's two thousand three article provides one of the most empirical analyses of COT data across multiple commodity markets (Wang, 2003).
Online resources extend beyond academic and book-length treatments. The CFTC website provides free access to current and historical COT reports in multiple formats. The explanatory materials section offers detailed documentation of report construction, category definitions, and historical methodology changes. Traders serious about COT analysis should read these official CFTC documents to understand exactly what they are analyzing.
Commercial COT data services such as Barchart provide enhanced visualization and analysis tools beyond raw CFTC data. TradingView's educational materials, published scripts library, and user community provide additional resources for exploring different approaches to COT analysis.
The key to mastering COT analysis lies not in finding a single definitive source but rather in building understanding through multiple perspectives and information sources. Academic research provides rigorous empirical foundation. Practitioner-focused books offer practical implementation insights. Direct engagement with data through systematic backtesting develops intuition about how positioning dynamics manifest across different market conditions.
SYNTHESIZING KNOWLEDGE INTO PRACTICE
The COT Index indicator represents the synthesis of academic research, trading experience, and software engineering into a practical tool accessible to retail traders equipped with nothing more than a TradingView account and willingness to learn. What once required expensive data subscriptions, custom programming capabilities, statistical software, and institutional resources now appears as a straightforward indicator requiring only basic parameter selection and modest study to understand. This democratization of institutional-grade analysis tools represents a broader trend in financial markets over recent decades.
Yet technology and data access alone provide no edge without understanding and discipline. Markets remain relentlessly efficient at eliminating edges that become too widely known and mechanically exploited. The COT Index indicator succeeds only when users invest time learning the underlying concepts, understand the limitations and probability distributions involved, and integrate signals thoughtfully into trading plans rather than applying them mechanically.
The academic research demonstrates conclusively that institutional positioning contains genuine information about future price movements, particularly at extremes where commercial hedgers are maximally bearish or bullish relative to historical norms. This informational content is neither perfect nor deterministic but rather probabilistic, providing edge over many observations through identification of higher-probability configurations. Bessembinder and Chan's finding that commercial positioning explained modest but significant variance in future returns illustrates this probabilistic nature perfectly (Bessembinder and Chan, 1992). The effect is real and statistically significant, yet it explains perhaps ten to fifteen percent of return variance rather than most variance. Much of price movement remains unpredictable even with positioning intelligence.
The practical implication is that COT analysis works best as one component of a trading system rather than a standalone oracle. It provides the positioning dimension, revealing where the smart money has positioned and where the crowd has followed, but price action analysis provides the timing dimension. Fundamental analysis provides the catalyst dimension. Risk management provides the survival dimension. These components work together synergistically.
The indicator's design philosophy prioritizes transparency and education over black-box complexity, empowering traders to understand exactly what they are analyzing and why. Every calculation is documented and user-adjustable. The threshold markers, background coloring, tables, and clear signal states provide multiple reinforcing channels for conveying the same information.
This educational approach reflects a conviction that sustainable trading success comes from genuine understanding rather than mechanical system-following. Traders who understand why commercial positioning matters, how different trader categories behave, what positioning extremes signify, and where signals fit within probability distributions can adapt when market conditions change. Traders mechanically following black-box signals without comprehension abandon systems after normal losing streaks.
The research foundation supporting COT analysis comes primarily from commodity markets where commercial hedger informational advantages are most pronounced. Agricultural producers hedging crops know more about supply conditions than distant speculators. Energy companies hedging production know more about operating costs than financial traders. Metals miners hedging output know more about ore grades than index funds. Financial futures markets show weaker but still present effects.
The journey from reading this documentation to profitable trading based on COT analysis involves several stages that cannot be rushed. Initial reading and basic understanding represents the first stage. Historical study represents the second stage, reviewing past market cycles to observe how positioning extremes preceded major turning points. Paper trading or small-size real trading represents the third stage to experience the psychological challenges. Refinement based on results and personal psychology represents the fourth stage.
Markets will continue evolving. New participant categories will emerge. Regulatory structures will change. Technology will advance. Yet the fundamental dynamics driving COT analysis, that different market participants have different information, different motivations, and different forecasting abilities that manifest in their positioning, will persist as long as futures markets exist. While specific thresholds or optimal parameters may shift over time, the core logic remains sound and adaptable.
The trader equipped with this indicator, understanding of the theory and evidence behind COT analysis, realistic expectations about probability rather than certainty, discipline to maintain positions through adverse volatility, and patience to allow signals time to develop possesses genuine edge in markets. The edge is not enormous, markets cannot allow large persistent inefficiencies without arbitraging them away, but it is real, measurable, and exploitable by those willing to invest in learning and disciplined application.
REFERENCES
Bessembinder, H. (1992) Systematic risk, hedging pressure, and risk premiums in futures markets, Review of Financial Studies, 5(4), pp. 637-667.
Bessembinder, H. and Chan, K. (1992) The profitability of technical trading rules in the Asian stock markets, Pacific-Basin Finance Journal, 3(2-3), pp. 257-284.
Briese, S. (2008) The Commitments of Traders Bible: How to Profit from Insider Market Intelligence. Hoboken: John Wiley & Sons.
Chang, E.C. (1985) Returns to speculators and the theory of normal backwardation, Journal of Finance, 40(1), pp. 193-208.
Commodity Futures Trading Commission (CFTC) (2009) Explanatory Notes: Disaggregated Commitments of Traders Report. Available at: www.cftc.gov (Accessed: 15 January 2025).
Commodity Futures Trading Commission (CFTC) (2020) Commitments of Traders: About the Report. Available at: www.cftc.gov (Accessed: 15 January 2025).
Irwin, S.H. and Sanders, D.R. (2012) Testing the Masters Hypothesis in commodity futures markets, Energy Economics, 34(1), pp. 256-269.
Kaufman, P.J. (2013) Trading Systems and Methods. 5th edn. Hoboken: John Wiley & Sons.
Ruan, Y. and Zhang, Y. (2018) Forecasting commodity futures prices using machine learning: Evidence from the Chinese commodity futures market, Applied Economics Letters, 25(12), pp. 845-849.
Sanders, D.R., Boris, K. and Manfredo, M. (2004) Hedgers, funds, and small speculators in the energy futures markets: an analysis of the CFTC's Commitments of Traders reports, Energy Economics, 26(3), pp. 425-445.
Schwager, J.D. (2012) Market Sense and Nonsense: How the Markets Really Work and How They Don't. Hoboken: John Wiley & Sons.
Tharp, V.K. (2008) Super Trader: Make Consistent Profits in Good and Bad Markets. New York: McGraw-Hill.
Wang, C. (2003) The behavior and performance of major types of futures traders, Journal of Futures Markets, 23(1), pp. 1-31.
Williams, L.R. and Noseworthy, M. (2009) The Right Stock at the Right Time: Prospering in the Coming Good Years. Hoboken: John Wiley & Sons.
FURTHER READING
For traders seeking to deepen their understanding of COT analysis and futures market positioning beyond this documentation, the following resources provide valuable extensions:
Academic Journal Articles:
Fishe, R.P.H. and Smith, A. (2012) Do speculators drive commodity prices away from supply and demand fundamentals?, Journal of Commodity Markets, 1(1), pp. 1-16.
Haigh, M.S., Hranaiova, J. and Overdahl, J.A. (2007) Hedge funds, volatility, and liquidity provision in energy futures markets, Journal of Alternative Investments, 9(4), pp. 10-38.
Kocagil, A.E. (1997) Does futures speculation stabilize spot prices? Evidence from metals markets, Applied Financial Economics, 7(1), pp. 115-125.
Sanders, D.R. and Irwin, S.H. (2011) The impact of index funds in commodity futures markets: A systems approach, Journal of Alternative Investments, 14(1), pp. 40-49.
Books and Practitioner Resources:
Murphy, J.J. (1999) Technical Analysis of the Financial Markets: A Guide to Trading Methods and Applications. New York: New York Institute of Finance.
Pring, M.J. (2002) Technical Analysis Explained: The Investor's Guide to Spotting Investment Trends and Turning Points. 4th edn. New York: McGraw-Hill.
Federal Reserve and Research Institution Publications:
Federal Reserve Banks regularly publish working papers examining commodity markets, futures positioning, and price discovery mechanisms. The Federal Reserve Bank of San Francisco and Federal Reserve Bank of Kansas City maintain active research programs in this area.
Online Resources:
The CFTC website provides free access to current and historical COT reports, explanatory materials, and regulatory documentation.
Barchart offers enhanced COT data visualization and screening tools.
TradingView's community library contains numerous published scripts and educational materials exploring different approaches to positioning analysis.
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