Random Entries Work!" tHe MaRkEtS aRe RaNdOm ", say moron academics.
The purpose of this study is to show that most markets are NOT random! Most markets show a clear bias where we can make such easy money, that a random number generator can do it.
=== HOW THE INDICATOR WORKS ===
The study will randomly enter the market
The study will randomly exit the market if in a trade
You can choose a Long Only, Short Only, or Bidirectional strategy
=== DEFAULT VALUES AND THEIR LOGIC ===
Percent Chance to Enter Per Bar: 10%
Percent Chance to Exit Per Bar: 3%
Direction: Long Only
Commission: 0
Each bar has a 10% chance to enter the market. Each bar has a 3% to exit the market . It will only enter long.
I included zero commission for simplification. It's a good exercise to include a commission/slippage to see just how much trading fees take from you.
=== TIPS ===
Increasing "Percent Chance to Exit" will shorten the time in a trade. You can see the "Avg # Bars In Trade" go down as you increase. If "Percent Chance to Exit" is too high, the study won't be in the market long enough to catch any movement, possibly exiting on the same bar most of the time.
If you're getting the red screen, that means the strategy lost so much money it went broke. Try reducing the percent equity on the Properties tab.
Switch the start year to avoid/minimize black swan events like the covid drop in 2020.
=== FINDINGS ===
Most markets lose money with a "Random" direction strategy.
Most markets lose ALL money with a "Short Only" strategy.
Most markets make money with a "Long Only" strategy.
Try this strategy on: Bitcoin (BTCUSD) and the NASDAQ (QQQ).
There are two popular memes right now: "Bitcoin to the moon" and "Stocks only go up". Both are seemingly true. Bitcoin was the best performing asset of the 2010's, gaining several billion percent in gains. The stock market is on a 100 year long uptrend. Why? BECAUSE FIAT CURRENCIES ALWAYS GO DOWN! This is inflation. If we measure the market in terms of others assets instead of fiat, the Long Only strategy doesn't work anymore (or works less well).
Try this strategy on: Bitcoin/GLD (BTCUSD/GLD), the Eurodollar (EURUSD), and the S&P 500 measured in gold (SPY/GLD).
Bitcoin measured in gold (BTCUSD/GLD) still works with a Long Only strategy because Bitcoin increased in value over both USD and gold.
The Eurodollar (EURUSD) generally loses money no matter what, especially if you add any commission. This makes sense as they are both fiat currencies with similar inflation schedules.
Gold and the S&P 500 have gained roughly the same amount since ~2000. Some years will show better results for a long strategy, while others will favor a short strategy. Now look at just SPY or GLD (which are both measured in USD by default!) and you'll see the same trend again: a Long Only strategy crushes even when entering and exiting randomly.
=== " JUST TELL ME WHAT TO DO, YOU NERD! " ===
Bulls always win and Bears always lose because fiat currencies go to zero.
You're not underperforming a random number generator, are you?
Cari dalam skrip untuk "spy"
EMA_cumulativeVolume_crossover[Strategy V2]This is variation of EMA_cumulativeVolume_crossover strategy.
instead of cumulative volume crossover, I have added the EMA to cumulative volume of same EMA length.
when EMA crossover EMACumulativeVolume , BUY
when already in LONG position and price crossing over EMACumulativeVolume*2 (orange line in the chart) , Add more
Partial Exit , when RSI 5 crossdown 90
Close All when EMA cross down EMACumulativeVolume
Note
Black Line on the chart is the historical value of EMACumulativeVolume . when EMA area is green and price touch this line closes above it , you can consider consider BUY
I have tested it on SPY , QQQ and UDOW on hourly chart.
EMA setting 25 is working for all of these.
but SPY produces better results on EMA 35 setting
warning
This strategy is published educational purposes only.
Divergence of Stocks Above MA50 v.s. US-Stock MarketEnglish:
This indicator has been developed as an early warning tool to estimate the probability of correction in the US stock market. It works best in the daily chart.
Function:
1.) "Index-line"
The underlying stock index is converted to a scale between 0% and 100% based on its 52-week highs and lows. Where 100% is closing price at 52-week high and 0% is closing price at 52-week low.
2nd) "Stocks Above MA50".
For each major stock index, there is an index that determines the percentage of stocks above its 50 moving average. For example, for the S&P 500, this is the S5FI.
3) "Divergence
In an efficient market, both lines (index and number of stocks above the 50 MA) would run more or less in sync. A new high in the index would also mean a new high in the stocks trading above the 50 moving average. Often, however, a correction in the index is announced when the number of stocks trading above their 50 MA do not make a new, or even a lower, high while the underlying index marks a new high. The divergence signal measures this divergence of the indices. The higher the bar, the more pronounced the divergence.
How to read the indicator?
If a divergence occurs, then the stops should be tightened. As with any indicator, false signals can occur because a divergence does not automatically lead to a correction. The higher the divergence is indicated, the higher the probability. The strength of a correction cannot be predicted with the indicator.
For which symbols does the indicator work?
The indicator works exclusively for the following symbols:
S&P500: SPX, SPY, ES1!, US500 Index above MA50: S5FI
Russel2000: IWM, US2000, RTY1!, RUT, IWO Index above MA50: R2FI
NASDAQ100: NDX, NAS100, NQ1!, US100, QQQ Index above MA50: NDFI
NASDAQ: IXIC, ONEQ, QCN1!, NDAQ Index above MA50: NCFI
NYSE: XAX, NYA Index above MA50: MMFI
DowJones100: DJX, DJI, DIA, MYM1!, YM1! Index above MA50: DIFI
DowJonesComp: DOW, IYY Index above MA50: DCFI
Deutsch:
Dieser Indikator ist als Frühwarninstrument zur Einschätzung der Korrekturwahrscheinlichkeit im US-Aktienmarkt entwickelt worden. Er funktioniert am besten im Tages-Chart.
Funktion:
1.) „Index-line“
Der zugrunde liegende Aktienindex wird bezogen auf seine 52Wochen Hochs und Tiefs in eine Skala zwischen 0% und 100% umgerechnet. Dabei sind 100% Schlusskurs auf 52-Wochen Hoch und 0% Schlusskurs auf 52-Wochen Tief.
2.) „Stocks Above MA50“
Zu jedem Hauptaktienindex gibt es einen Index, der den Prozentwert der Aktien über Ihrem 50 gleitenden Durchschnitt ermittelt. Beim S&P 500 ist das z.B. der S5FI.
3.) „Divergence“
In einem effizienten Markt würden beide Linien (Index und Anzahl Aktien über dem 50 MA) mehr oder weniger synchron laufen. Ein neues Hoch im Index würde auch ein neues Hoch bei den Aktien, die über dem 50 gleitenden Durchschnitt notieren, bedeuten. Oft jedoch kündigt sich eine Korrektur im Index an, wenn die Anzahl der Aktien, die über ihrem 50 MA notieren kein neues, oder sogar ein niedrigeres Hoch machen, während der zu Grunde liegende Index ein neues Hoch markiert. Das Divergenz-Signal misst diese auseinanderlaufen der Indices. Je höher der Balken, umso stärker ist die Divergenz ausgeprägt.
Wie ist der Indikator zu lesen?
Wenn eine Divergenz auftritt, dann sollten die Stopps enger herangezogen werden. Es kann wie bei jedem Indikator zu Fehlsignalen kommen, da eine Divergenz nicht automatisch zu einer Korrektur führen muss. Die Wahrscheinlichkeit ist um so höher, je höher die Divergenz angezeigt wird. Die Stärke einer Korrektur kann mit dem Indikator nicht prognostiziert werden.
Für welche Symbole funktioniert der Indikator?
Der Indikator funktioniert ausschließlich für folgende Symbole:
S&P500: SPX, SPY, ES1!, US500 Index über MA50: S5FI
Russel2000: IWM, US2000, RTY1!, RUT, IWO Index über MA50: R2FI
NASDAQ100: NDX, NAS100, NQ1!, US100, QQQ Index über MA50: NDFI
NASDAQ: IXIC, ONEQ, QCN1!, NDAQ Index über MA50: NCFI
NYSE: XAX, NYA Index über MA50: MMFI
DowJones100: DJX, DJI, DIA, MYM1!, YM1! Index über MA50: DIFI
DowJonesComp: DOW, IYY Index über MA50: DCFI
Indices trendsAccording to the Dow theory, indices must confirm each other. Based on this idea, I develop an indices trends indicator, including SPY, DIA, and QQQ. The indices trends were calculated based on the average of the short- (blue) and intermediate-term (orange) changes of indices moving average slopes. In addition, IWM trends are shown as a reference in gray color.
Use this indicator together with one of SPY, DIA, QQQ, or IWM to show the overall market conditions.
Trendflex - Another new Ehlers indicatorSource: Stocks and Commodities V38
Hooray! Another new John Ehlers indicator!
John claims this indicator is lag-less and uses the SPY on the Daily as an example.
This indicator is a slight modification of Reflex, which I have posted here
I think it's better for Stocks and ETFs than Reflex since it factors in long trends. It tends to keep you in winning trades for a long time.
I believe this indicator can be used for entries or exits, potentially both.
Entry
1. Entering Long positions at the pivot low points (Stocks and ETFs)
2. Entering Long when the Reflex crosses above the zero lines (Stocks, ETFs, Commodities )
Exit
1. Exiting Long positions at a new pivot high point (Stocks and ETFs)
2. Exiting Long when the Reflex crosses below the zero lines (Stocks, ETFs, Commodities )
In this example, I place a Long order on the SPY every time the Reflex crosses above the zero level and exit when it crosses below or pops my stop loss, set at 1.5 * Daily ATR.
2/3 Wins
+16.05%
Let me know in the comment section if you're able to use this in a strategy.
Reflex - A new Ehlers indicatorSource: Stocks and Commodities V38
Hooray! A new John Ehlers indicator!
John claims this indicator is lag-less and uses the SPY on the Daily as an example.
He states that drawing a line from peak to peak (or trough to trough) will correspond perfectly with the Asset.
I have to say I agree! There is typically one bar of lag or no lag at all!
I believe this indicator can be used for either entries or exits, but not both.
Entry
1. Entering Long positions at the pivot low points (Stocks and ETFs)
2. Entering Long when the Reflex crosses above the zero lines (Stocks, ETFs, Commodities)
Exit
1. Exiting Long positions at a new pivot high point (Stocks and ETFs)
2. Exiting Long when the Reflex crosses below the zero lines (Stocks, ETFs, Commodities)
In this example, I place a Long order on the SPY every time the Reflex crosses above the zero level and exit when it crosses below or pops my stop loss, set at 1.5 * Daily ATR.
4/6 Wins
+10.76%
For me, that's good enough to create a strategy and backtest on several Indices and ETFs, which is what I have a hunch this will work on.
I think there is a lot of promise from a single Indicator!
Let me know in the comment section if you're able to use this in a strategy.
Hide Extended Hours/non-intraday American BarsOnly works with American bar style.
Not works with Candles.
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This script can hide the extended hours/non-intraday bars and leave the intraday bars only, especially for future users, such as ES/NQ/RTY/YM, etc.,.
Now you can find the intraday support/resistance quite easily!
Example, as a ES investor, you can easily find the intraday support/resistance level ,which is almost equal to SPY / SPX , no longer need to check SPY / SPX separately again, saving your time a lot.
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IMPORTANT INSTRUCTION
In order to make the script work, you have to bring it to the most top visual layer.
Please do as the following steps:
Add the script to chart
Hover mouse on the script name, and tap the right-most 'more' button (which appears as 3 dots)
Select "Visual Order", then select "Bring to front".
Done!
Also, in order to have a better view effect and make the bars COMPLETELY "Hidden", you can adjust the hidden bar color in the "setting" menu to the exact color of your chart background.
Swing-Trade-Stocks SystemThis is a simple swing trade system inspired by sources on the internet. The rules are as follows:
Buy when first green arrow appears after 10ma above 30ma
Set stop-loss below most recent support
Set take-profit below most recent swing point high or wait until price closes below 30ma (red)
Short when first purple arrow appears after 10ma below 30ma
Set stop-loss above most recent resistance
Set take-profit above most recent swing point low or wait until price closes above 30ma (red)
The background color changes based on the direction of SPY. If SPY is going down (10ma < 30ma) the
background will be red and only short indicators (purple arrows) will appear. If SPY is going up (10ma > 30ma),
the background will be green and only long indicators (green arrows) will appear.
Happy trading!
Market Internals [Makit0] MARKET INTERNALS INDICATOR v0.5beta
Market Internals are suitable for day trade equity indices, named SPY or /ES, please do your own research about what they are and how to use them
This scripts plots the NYSE market internals charts as an indicator for an easy and full visualization of market internal structure all in one chart, useful for SPY and /ES trading
Description of the Market Internals
- TICK: NYSE stocks ticking up vs stocks ticking down, extreme values may point to trend continuation on trending days or reversal in non trending days, example of extreme values can be 800 and 1000
- ADD: NYSE stocks going up vs stocks going down, if price auctions around the zero line may be a non trend day, otherwise may be a trend day
- VOLD: NYSE volume of stocks up vs volume of stocks going down, identify clearly where the volume is going, as example if volume is flowing down may be a good idea no to place longs
- TRIN: NYSE up stocks vs down stocks ratio divided by up volume vs down volume ratio. A value of 1 indicates parity, below that the strength is on the long side, above the strength is in the short side.
A basic use of market internals may be looking for divergences, for example:
- /ES is trading in a range but ADD and VOLD are trending up nonstop, may /ES will break the range to the upside
- /ES is trading in a range and ADD and VOLD are trading around the zero line but got an extreme reading on TICK, may be a non trending day and the TICK extreme reading is at one of the extremes of the /ES range, may be a good probability trade to fade that move
- /ES is trading in a trend to the downside, ADD and VOLD too, you catch a good portion of the move but are fearful to flat and miss more gains, you see in the TICK a lot of extreme values below -800 so your're confident in the continuation of the downtrend, until the TICK goes beyond -1000 and you use that signal to go flat
Market internals give you context and confirmation, price in /ES may be trending but if market internals do not confirm the move may a reversal is on its way
Price is an advertise, you can see the real move in the structure below, in the behavior of the individual components of the market, those are the real questions:
- How many stocks are going up/down (ADD)
- How many volume is flowing up/down (VOLD)
- How many stocks are ticking up/down (TICK)
- What is the overall volume breath of the market (TRIN)
FEATURES:
- Plot one of the four basic market internal indices: TICK, ADD, VOLD and TRIN
- Show labels with values beyond an user defined threshold
- Show ZERO line
- Show user defined Dotted and Dashed lines
- Show user defined moving average
SETTINGS:
- Market internal: ticker to plot in the indicator, four options to choose from (TICK, ADD, VOLD and TRIN)
- Labels threshold: all values beyond this will be ploted as labels
- Dot lines at: two dotted lines will be plotted at this value above and below the zero line
- Dash lines at: two dashed lines will be plotted at this value above and below the zero line
- MA type: two options avaiable SMA (Simple Moving Average) or EMA (Exponential Moving Average)
- MA length: number of bars to calculate the moving average
- Show zero line: show or hide zero line
- Show dot line: show or hide dotted lines
- Show dash line: show or hide dashed lines
- Show labels: show or hide labels
GOOD LUCK AND HAPPY TRADING
Hide extended hours/non-intraday barsEspecially for future users, such as ES/NQ/RTY/YM, etc., this script can hide the extended hours/non-intraday bars and leave the intraday bars only.
With this script , you can find the intraday support/resistance quite easily!
Example, if you are a ES investor, you can easily find the intraday support/resistance level ,which is almost equal to SPY, with this script, and no need to check SPY separately again , saving your time a lot.
Note: Please couple this script with American Bars. If you use candle charts, the upper/lower pins of the candle can't be hidden with the bars together, which is restricted by the code editor itself...
Kozlod - RSI Strategy - 1 minuteStarted to play with very simple strategies. Trying to find ones with optimal parameters which work well for certain symbols/timeframe.
Found that basic RSI strategy without any position management with high RSI length (65 in this script) works pretty good for 1m chart for few stocks.
It's also not bad for AAPL , SPY .
It might not work very good on it's not but can give you a pretty good base for more complicated indicators.
And remember:
Past performance does not guarantee future results.
Willams %RwEMAspy
Was looking for something else when surfed into an old question
wanting %R 21 period with EMA 13 period of the %R signal
and being a rookie at this, made this code to post for them.
Tried to comment the script in such a way that other rookies
like me could make better sense of what is being done. Hope
this helps someone. I find it useful as one of my indicators for
trading.
Pinescript for tradingview.com user Tom1trader
All time frames.
Interpretation:
%R (Red) crosses above it's average (Blue) - bull
%R crosses below it's average - bear. Background
color changes green-up red-down with above crossings.
Most but not all of serious price movement takes place
from the time the %R (red) goes into oversold (or bought) and
exits again.
%R centerline crosses can also be useful.
I use various indicators and want all of the confirmation
that I can get for expectations BUT I never know what the
next bar will do and define my risks accordingly.
Sectors Relative Strength Normal DistributionI wrote this indicator as an attempt to see the Relative Strengths of different sectors in the same scale, but there is also other ways to do that.
This indicator plots the normal distribution for the 10 sectors of the SPY for the last X bars of the selected resolution, based on the selected comparative security. It shows which sectors are outperforming and underperforming the SPY (or any other security) relatively to each other by the given deviation.
MarketRSThe strength of a stock relative to the market (SPY) is an import indicator accumulation of a stock by institutionan funds, especially during a market decline. This indicator plot the ratio of a security/SPY and plots a fast (5 period) and slow (21 period) EMA.
Volume Area 80 Rule Pro - Adaptive RTHSummary in one paragraph
Adaptive value area 80 percent rule for index futures large cap equities liquid crypto and major FX on intraday timeframes. It focuses activity only when multiple context gates align. It is original because the classic prior day value area traverse is fused with a daily regime classifier that remaps the operating parameters in real time.
Scope and intent
• Markets. ES NQ SPY QQQ large cap equities BTC ETH major FX pairs and other liquid RTH instruments
• Timeframes. One minute to one hour with daily regime context
• Default demo used in the publication. ES1 on five minutes
• Purpose. Trade only the balanced days where the 80 percent traverse has edge while standing aside or tightening rules during trend or shock
Originality and usefulness
• Unique fusion. Prior day value area logic plus a rolling daily regime classifier using percentile ranks of realized volatility and ADX. The regime remaps hold time end of window stop buffer and value area coverage on each session
• Failure mode addressed. False starts during strong trend or shock sessions and weak traverses during quiet grind
• Testability. All gates are visible in Inputs and debug flags can be plotted so users can verify why a suggestion appears
• Portable yardstick. The regime uses ATR divided by close and ADX percent ranks which behave consistently across symbols
Method overview in plain language
The script builds the prior session profile during regular trading hours. At the first regular bar it freezes yesterday value area low value area high and point of control. It then evaluates the current session open location the first thirty minute volume rank the open gap rank and an opening drive test. In parallel a daily series classifies context into Calm Balance Trend or Shock from rolling percentile ranks of realized volatility and ADX. The classifier scales the rules. Calm uses longer holds and a slightly wider value area. Trend and Shock shorten the window reduce holds and enlarge stop buffers.
Base measures
• Range basis. True Range smoothed over a configurable length on both the daily and intraday series
• Return basis. Not required. ATR over close is the unit for regime strength
Components
• Prior Value Area Engine. Builds yesterday value area low value area high and point of control from a binned volume profile with automatic TPO fallback and minimum integrity guards
• Opening Location. Detects whether the session opens above the prior value area or below it
• Inside Hold Counter. Counts consecutive bars that hold inside the value area after a re entry
• Volume Gate. Percentile of the first thirty minutes volume over a rolling sample
• Gap Gate. Percentile rank of the regular session open gap over a rolling sample
• Drive Gate. Opening drive check using a multiple of intraday ATR
• Regime Classifier. Percentile ranks of daily ATR over close and daily ADX classify Calm Balance Trend Shock and remap parameters
• Session windows optional. Windows follow the chart exchange time
Fusion rule
Minimum satisfied gates approach. A re entry must hold inside the value area for a regime scaled number of bars while the volume gap and drive gates allow the setup. The regime simultaneously scales value area coverage end minute time stop and stop buffer.
Signal rule
• Long suggestion appears when price opens below yesterday value area then re enters and holds for the required bars while all gates allow the setup
• Short suggestion appears when price opens above yesterday value area then re enters and holds for the required bars while all gates allow the setup
• WAIT shows implicitly when any required gate is missing
• Exit labels mark target touch stop touch or a time based close
Inputs with guidance
Setup
• Signal timeframe. Uses the chart by default
• Session windows optional. Start and end minutes inside regular trading hours
• Invert direction is not used. The logic is symmetric
Logic
• Hold bars inside value area. Typical range 3 to 12. Raising it reduces trades and favors better traverses. Lowering it increases frequency and risk of false starts
• Earliest minute since RTH open and Latest minute since RTH open. Typical range 0 to 390. Reducing the latest minute cuts late session trades
• Time stop bars after entry. Typical range 6 to 30. Larger values give setups more room
Filters
• Value area coverage. Typical range 0.70 to 0.85. Higher coverage narrows the traverse but accepts fewer days
• Bin size in ticks. Typical range 1 to 8. Larger bins stabilize noisy profiles
• Stop buffer ticks beyond edge. Typical range 2 to 20. Larger buffers survive noise
• First thirty minute volume percentile. Typical range 0.30 to 0.70. Higher values require more active opens
• Gap filter percentile. Typical range 0.70 to 0.95. Lower values block more gap days
• Opening drive multiple and bars. Higher multiple or longer bars block strong directional opens
Adaptivity
• Lookback days for regime ranks. Typical 150 to 500
• Calm RV percentile. Typical 25 to 45
• Trend ADX percentile. Typical 55 to 75
• Shock RV percentile. Typical 75 to 90
• End minute ratio in Trend and Shock. Typical 0.5 to 0.8
• Hold and Time stop scales per regime. Use values near one to keep behavior close to static settings
Realism and responsible publication
• No performance claims. Past results never guarantee future outcomes
• Shapes can move while a bar forms and settle on close
• Sessions use the chart exchange time
Honest limitations and failure modes
• Economic releases and thin liquidity can break the balance premise
• Gap heavy symbols may work better with stronger gap filters and a True Range focus
• Very quiet regimes reduce signal contrast. Consider longer windows or higher thresholds
Legal
Education and research only. Not investment advice. Test in simulation before any live use.
Yang-Zhang Volatility (YZVol) by CoryP1990 – Quant ToolkitThe Yang-Zhang Volatility (YZVol) estimator measures realized volatility using both overnight gaps and intraday moves. It combines three components: overnight returns, open-to-close returns, and the Rogers–Satchell term, weighted by Zhang’s k to reduce bias.
How to read it
Line color: Green when YZVol is rising (volatility expansion), Red when falling (volatility compression).
Background: Green tint = above High-vol threshold (active regime). Red tint = below Low-vol threshold (quiet regime).
Units: Displays Daily % by default on any timeframe (values are normalized to daily). An optional toggle shows Annualized % (√252 × Daily %).
Typical uses
Spot transitions between quiet and active regimes.
Compare realized vol vs implied vol or a risk-target.
Adapt position sizing to volatility clustering.
Defaults
Length = 20
High-vol threshold = 5% (Daily)
Low-vol threshold = 1% (Daily)
Optional: Annualized % display
Example — SPY (1D)
During the 2020 crash, YZVol surged to 5.8 % per day, capturing the height of pandemic-era volatility before compressing into a calm regime through 2021. Volatility re-expanded in 2022 due to reinflamed COVID fears and gradually stabilized through 2023. A sharp, liquidity-driven volatility event in August 2024 caused another brief YZVol surge, reflecting the historic one-day VIX spike triggered by market-wide risk-off flows and thin pre-market liquidity. A second, policy-driven expansion followed in April–May 2025, coinciding with the renewed U.S.–China tariff conflict and a sharp equity pullback. Since mid-2025, YZVol has settled near 1 % per day, with the red background confirming that realized volatility has once again compressed into a quiet, low-risk regime.
Part of the Quant Toolkit — transparent, open-source indicators for modern quantitative analysis. Built by CoryP1990.
Tristan's Tri-band StrategyTristan's Tri-band Strategy - Confluence Trading System
Strategy Overview:
This strategy combines three powerful technical indicators - RSI, Williams %R, and Bollinger Bands - into a single visual trading system. Instead of cluttering your chart with separate indicator panels, all signals are displayed directly on the price chart using color-coded gradient overlays, making it easy to spot high-probability trade setups at a glance.
How It Works:
The strategy identifies trading opportunities when multiple indicators align (confluence), suggesting strong momentum shifts:
📈 Long Entry Signals:
RSI drops to 30 or below (oversold)
Williams %R reaches -80 to -100 range (oversold)
Price touches or breaks below the lower Bollinger Band
All three conditions must align during your selected trading session
📉 Short Entry Signals:
RSI rises to 70 or above (overbought)
Williams %R reaches 0 to -20 range (overbought)
Price touches or breaks above the upper Bollinger Band
All three conditions must align during your selected trading session
Visual Indicators:
(faint) Green gradients below candles = Bullish oversold conditions (buying opportunity)
(faint) Red/Orange gradients above candles = Bearish overbought conditions (selling opportunity)
Stacked/brighter gradients = Multiple indicators confirming the same signal (higher probability) will stack and show brighter / less faint
Blue Bollinger Bands = Volatility boundaries and mean reversion zones
Exit Strategy:
Long trades exit when price reaches the upper Bollinger Band OR RSI becomes overbought (≥70)
Short trades exit when price reaches the lower Bollinger Band OR RSI becomes oversold (≤30)
Key Features:
✅ Session Filters - Trade only during NY (9:30 AM-4 PM), London (3 AM-11:30 AM), or Asia (7 PM-1 AM EST) sessions
✅ No Repainting - Signals are confirmed on candle close for realistic backtesting and live trading
✅ Customizable Parameters - Adjust RSI levels, BB standard deviations, Williams %R periods, and gradient visibility
✅ Visual Clarity - See all three indicators at once without switching between panels
✅ Built-in Alerts - Get notified when entry and exit conditions are met
How to Use Effectively:
Choose Your Trading Session - For day trading US stocks, enable only the NY session. For forex or 24-hour markets, select the sessions that match your schedule.
Look for Gradient Stacking - The brightest, most visible gradients occur when both RSI and Williams %R signal together. These are your highest-probability setups.
Confirm with Price Action - Wait for the candle to close before entering. The strategy enters on the next bar's open to prevent repainting.
Respect the Bollinger Bands - Entries occur at the outer bands (price extremes), and exits occur at the opposite band or when momentum reverses.
Backtest First - Test the strategy on your preferred instruments and timeframes. Works best on liquid assets with clear trends and mean reversion patterns (stocks, major forex pairs, indices).
Adjust Gradient Visibility - Use the "Gradient Strength" slider (lower = more visible) to make signals stand out on your chart style.
Best Timeframes: 5-minute to 1-hour charts for intraday trading; 4-hour to daily for swing trading (I have also found the 3 hour timeframe to work really well for some stocks / ETFs.)
Best Markets: Liquid instruments with volatility - SPY, QQQ, major stocks, EUR/USD, GBP/USD, major indices
Risk Management: This is a mean reversion strategy that works best in ranging or choppy markets. In strong trends, signals may appear less frequently. Always use proper position sizing and stop losses based on your risk tolerance.
----------------------------------------------
Note: Past performance does not guarantee future results. This strategy is provided for educational purposes. Always backtest thoroughly and practice proper risk management before live trading.RetryClaude can make mistakes. Please double-check responses. Sonnet 4.5
v2.0—Tristan's Multi-Indicator Reversal Strategy🎯 Multi-Indicator Reversal Strategy - Optimized for High Win Rates
A powerful confluence-based strategy that combines RSI, MACD, Williams %R, Bollinger Bands, and Volume analysis to identify high-probability reversal points . Designed to let winners run with no stop loss or take profit - positions close only when opposite signals occur.
Also, the 3 hour timeframe works VERY well—just a lot less trades.
📈 Proven Performance
This strategy has been backtested and optimized on multiple blue-chip stocks with 80-90%+ win rates on 1-hour timeframes from Aug 2025 through Oct 2025:
✅ V (Visa) - Payment processor
✅ MSFT (Microsoft) - Large-cap tech
✅ WMT (Walmart) - Retail leader
✅ IWM (Russell 2000 ETF) - Small-cap index
✅ NOW (ServiceNow) - Enterprise software
✅ WM (Waste Management) - Industrial services
These stocks tend to mean-revert at extremes, making them ideal candidates for this reversal-based approach. I only list these as a way to show you the performance of the script. These values and stock choices may change over time as the market shifts. Keep testing!
🔑 How to Use This Strategy Successfully
Step 1: Apply to Chart
Open your desired stock (V, MSFT, WMT, IWM, NOW, WM recommended)
Set timeframe to 1 Hour
Apply this strategy
Check that the Williams %R is set to -20 and -80, and "Flip All Signals" is OFF (can flip this for some stocks to perform better.)
Step 2: Understand the Signals
🟢 Green Triangle (BUY) Below Candle:
Multiple indicators (RSI, Williams %R, MACD, Bollinger Bands) show oversold conditions
Enter LONG position
Strategy will pyramid up to 10 entries if more buy signals occur
Hold until red triangle appears
🔴 Red Triangle (SELL) Above Candle:
Multiple indicators show overbought conditions
Enter SHORT position (or close existing long)
Strategy will pyramid up to 10 entries if more sell signals occur
Hold until green triangle appears
🟣 Purple Labels (EXIT):
Shows when positions close
Displays count if multiple entries were pyramided (e.g., "Exit Long x5")
Step 3: Let the Strategy Work
Key Success Principles:
✅ Be Patient - Signals don't occur every day, wait for quality setups
✅ Trust the Process - Don't manually close positions, let opposite signals exit
✅ Watch Pyramiding - The strategy can add up to 10 positions in the same direction
✅ No Stop Loss - Positions ride through drawdowns until reversal confirmed
✅ Session Filter - Only trades during NY session (9:30 AM - 4:00 PM ET)
⚙️ Winning Settings (Already Set as Defaults)
INDICATOR SETTINGS:
- RSI Length: 14
- RSI Overbought: 70
- RSI Oversold: 30
- MACD: 12, 26, 9 (standard)
- Williams %R Length: 14
- Williams %R Overbought: -20 ⭐ (check this! And adjust to your liking)
- Williams %R Oversold: -80 ⭐ (check this! And adjust to your liking)
- Bollinger Bands: 20, 2.0
- Volume MA: 20 periods
- Volume Multiplier: 1.5x
SIGNAL REQUIREMENTS:
- Min Indicators Aligned: 2
- Require Divergence: OFF
- Require Volume Spike: OFF
- Require Reversal Candle: OFF
- Flip All Signals: OFF ⭐
RISK MANAGEMENT:
- Use Stop Loss: OFF ⭐⭐⭐
- Use Take Profit: OFF ⭐⭐⭐
- Allow Pyramiding: ON ⭐⭐⭐
- Max Pyramid Entries: 10 ⭐⭐⭐
SESSION FILTER:
- Trade Only NY Session: ON
- NY Session: 9:30 AM - 4:00 PM ET
**⭐ = Critical settings for success**
## 🎓 Strategy Logic Explained
### **How It Works:**
1. **Multi-Indicator Confluence**: Waits for at least 2 out of 4 technical indicators to align before generating signals
2. **Oversold = Buy**: When RSI < 30, Williams %R < -80, price below lower Bollinger Band, and/or MACD turning bullish → BUY signal
3. **Overbought = Sell**: When RSI > 70, Williams %R > -20, price above upper Bollinger Band, and/or MACD turning bearish → SELL signal
4. **Pyramiding Power**: As trend continues and more signals fire in the same direction, adds up to 10 positions to maximize gains
5. **Exit Only on Reversal**: No arbitrary stops or targets - only exits when opposite signal confirms trend change
6. **Session Filter**: Only trades during liquid NY session hours to avoid overnight gaps and low-volume periods
### **Why No Stop Loss Works:**
Traditional reversal strategies fail because they:
- Get stopped out too early during normal volatility
- Miss the actual reversal that happens later
- Cut winners short with tight take profits
This strategy succeeds because it:
- ✅ Rides through temporary noise
- ✅ Captures full reversal moves
- ✅ Uses multiple indicators for confirmation
- ✅ Pyramids into winning positions
- ✅ Only exits when technical picture completely reverses
---
## 📊 Understanding the Display
**Live Indicator Counter (Top Corner / end of current candles):**
Bull: 2/4
Bear: 0/4
(STANDARD)
Shows how many indicators currently align bullish/bearish
"STANDARD" = normal reversal mode (buy oversold, sell overbought)
"FLIPPED" = momentum mode if you toggle that setting
Visual Indicators:
🔵 Blue background = NY session active (trading window)
🟡 Yellow candle tint = Volume spike detected
💎 Aqua diamond = Bullish divergence (price vs RSI)
💎 Fuchsia diamond = Bearish divergence
⚡ Advanced Tips
Optimizing for Different Stocks:
If Win Rate is Low (<50%):
Try toggling "Flip All Signals" to ON (switches to momentum mode)
Increase "Min Indicators Aligned" to 3 or 4
Turn ON "Require Divergence"
Test on different timeframe (4-hour or daily)
If Too Few Signals:
Decrease "Min Indicators Aligned" to 2
Turn OFF all requirement filters
Widen Williams %R bands to -15 and -85
If Too Many False Signals:
Increase "Min Indicators Aligned" to 3 or 4
Turn ON "Require Divergence"
Turn ON "Require Volume Spike"
Reduce Max Pyramid Entries to 5
Stock Selection Guidelines:
Best Suited For:
Large-cap stable stocks (V, MSFT, WMT)
ETFs (IWM, SPY, QQQ)
Stocks with clear support/resistance
Mean-reverting instruments
Avoid:
Ultra low-volume penny stocks
Extremely volatile crypto (try traditional settings first)
Stocks in strong one-directional trends lasting months
🔄 The "Flip All Signals" Feature
If backtesting shows poor results on a particular stock, try toggling "Flip All Signals" to ON:
STANDARD Mode (OFF):
Buy when oversold (reversal strategy)
Sell when overbought
May work best for: V, MSFT, WMT, IWM, NOW, WM
FLIPPED Mode (ON):
Buy when overbought (momentum strategy)
Sell when oversold
May work best for: Strong trending stocks, momentum plays, crypto
Test both modes on your stock to see which performs better!
📱 Alert Setup
Create alerts to notify you of signals:
📊 Performance Expectations
With optimized settings on recommended stocks:
Typical results we are looking for:
Win Rate: 70-90%
Average Winner: 3-5%
Average Loser: 1-3%
Signals Per Week: 1-3 on 1-hour timeframe
Hold Time: Several hours to days
Remember: Past performance doesn't guarantee future results. Always use proper risk management.
Sector Relative StrengthThis indicator measures a stock's Real Relative Strength against its sector benchmark, helping you identify stocks that are outperforming or underperforming their sector peers.
The concept is based on the Real Relative Strength methodology popularized by the r/realdaytrading community.
Unlike traditional relative strength calculations that simply compare price ratios, this indicator uses a more sophisticated approach that accounts for volatility through ATR (Average True Range), providing a normalized view of true relative performance.
Key Features
Automatic Sector Detection
Automatically detects your stock's sector using TradingView's built-in sector classification
Maps to the appropriate SPDR Sector ETF (XLK, XLF, XLV, XLY, XLP, XLI, XLE, XLU, XLB, XLC)
Supports all 20 TradingView sectors
Sector ETF Mappings
The indicator automatically compares your stock against:
Technology: XLK (Technology Services, Electronic Technology)
Financials: XLF (Finance sector)
Healthcare: XLV (Health Technology, Health Services)
Consumer Discretionary: XLY (Retail Trade, Consumer Services, Consumer Durables)
Consumer Staples: XLP (Consumer Non-Durables)
Industrials: XLI (Producer Manufacturing, Industrial Services, Transportation, Commercial Services)
Energy: XLE (Energy Minerals)
Utilities: XLU
Materials: XLB (Non-Energy Minerals, Process Industries)
Communications: XLC
Default: SPY (for Miscellaneous or unclassified sectors)
Customizable Settings
Comparison Mode: Choose between automatic sector comparison or custom symbol
Length: Adjustable lookback period (default: 12)
Smoothing: Apply moving average to reduce noise (default: 3)
Visual Clarity
Green line: Stock is outperforming its sector
Red line: Stock is underperforming its sector
Zero baseline: Clear reference point for performance
Clean info box: Shows which ETF you're comparing against
How It Works
The indicator calculates relative strength using the following methodology:
Rolling Price Change: Measures the price movement over the specified length for both the stock and its sector ETF
ATR Normalization: Uses Average True Range to normalize for volatility differences
Power Index: Calculates the sector's strength relative to its volatility
Real Relative Strength: Compares the stock's performance against the sector's power index
Smoothing: Applies a moving average to reduce single-candle spikes
Formula:
Power Index = (Sector Price Change) / (Sector ATR)
RRS = (Stock Price Change - Power Index × Stock ATR) / Stock ATR
Smoothed RRS = SMA(RRS, Smoothing Length)
(FTD) Follow-Through Day SignalFollow-Through Day (FTD) Signal
This indicator detects potential Follow-Through Days (FTDs) — a concept popularized by William O’Neil — to help identify possible market trend confirmations.
A Follow-Through Day occurs when an index shows strong upside action on higher volume several days after a market low, suggesting institutional buying rather than short covering.
How it works:
The indicator checks for a session where the price gains a defined minimum percentage from the prior close (default: 1.2% or more).
Volume must be greater than the previous day’s volume.
The rally must occur at least three days after a recent low, determined by the lookback period (default: 20 days).
Additional safeguards require that recent bars are not making new lows and that the bar three days prior either closed positive or was not at a new low — filtering out false signals from oversold bounces.
When all conditions are met, a blue up arrow is plotted beneath the bar, and an optional “FTD” label appears if enabled.
Inputs:
Min % Gain from Previous Close (%): Sets the minimum daily percentage gain to qualify as a Follow-Through Day.
Lookback Period for Lowest Low Checks: Defines how many bars back to search for a recent market low (default: 20).
Show Signal Label: Toggles the on-chart “FTD” label display.
Usage:
This indicator is intended for use on daily charts of major market indexes — such as the Nasdaq Composite (symbol: IXIC) or broad index ETFs including QQQ, SPY, and DIA — where Follow-Through Day signals are most relevant for confirming potential trend reversals.
Rolling Correlation vs Another Symbol (SPY Default)This indicator visualizes the rolling correlation between the current chart symbol and another selected asset, helping traders understand how closely the two move together over time.
It calculates the Pearson correlation coefficient over a user-defined period (default 22 bars) and plots it as a color-coded line:
• Green line → positive correlation (move in the same direction)
• Red line → negative correlation (move in opposite directions)
• A gray dashed line marks the zero level (no correlation).
The background highlights periods of strong relationship:
• Light green when correlation > +0.7 (strong positive)
• Light red when correlation < –0.7 (strong negative)
Use this tool to quickly spot diversification opportunities, confirm hedges, or understand how assets interact during different market regimes.
J.P. Morgan Efficiente 5 IndexJ.P. MORGAN EFFICIENTE 5 INDEX REPLICATION
Walk into any retail trading forum and you'll find the same scene playing out thousands of times a day: traders huddled over their screens, drawing trendlines on candlestick charts, hunting for the perfect entry signal, convinced that the next RSI crossover will unlock the path to financial freedom. Meanwhile, in the towers of lower Manhattan and the City of London, portfolio managers are doing something entirely different. They're not drawing lines. They're not hunting patterns. They're building fortresses of diversification, wielding mathematical frameworks that have survived decades of market chaos, and most importantly, they're thinking in portfolios while retail thinks in positions.
This divide is not just philosophical. It's structural, mathematical, and ultimately, profitable. The uncomfortable truth that retail traders must confront is this: while you're obsessing over whether the 50-day moving average will cross the 200-day, institutional investors are solving quadratic optimization problems across thirteen asset classes, rebalancing monthly according to Markowitz's Nobel Prize-winning framework, and targeting precise volatility levels that allow them to sleep at night regardless of what the VIX does tomorrow. The game you're playing and the game they're playing share the same field, but the rules are entirely different.
The question, then, is not whether retail traders can access institutional strategies. The question is whether they're willing to fundamentally change how they think about markets. Are you ready to stop painting lines and start building portfolios?
THE INSTITUTIONAL FRAMEWORK: HOW THE PROFESSIONALS ACTUALLY THINK
When Harry Markowitz published "Portfolio Selection" in The Journal of Finance in 1952, he fundamentally altered how sophisticated investors approach markets. His insight was deceptively simple: returns alone mean nothing. Risk-adjusted returns mean everything. For this revelation, he would eventually receive the Nobel Prize in Economics in 1990, and his framework would become the foundation upon which trillions of dollars are managed today (Markowitz, 1952).
Modern Portfolio Theory, as it came to be known, introduced a revolutionary concept: through diversification across imperfectly correlated assets, an investor could reduce portfolio risk without sacrificing expected returns. This wasn't about finding the single best asset. It was about constructing the optimal combination of assets. The mathematics are elegant in their logic: if two assets don't move in perfect lockstep, combining them creates a portfolio whose volatility is lower than the weighted average of the individual volatilities. This "free lunch" of diversification became the bedrock of institutional investment management (Elton et al., 2014).
But here's where retail traders miss the point entirely: this isn't about having ten different stocks instead of one. It's about systematic, mathematically rigorous allocation across asset classes with fundamentally different risk drivers. When equity markets crash, high-quality government bonds often rally. When inflation surges, commodities may provide protection even as stocks and bonds both suffer. When emerging markets are in vogue, developed markets may lag. The professional investor doesn't predict which scenario will unfold. Instead, they position for all of them simultaneously, with weights determined not by gut feeling but by quantitative optimization.
This is what J.P. Morgan Asset Management embedded into their Efficiente Index series. These are not actively managed funds where a portfolio manager makes discretionary calls. They are rules-based, systematic strategies that execute the Markowitz framework in real-time, rebalancing monthly to maintain optimal risk-adjusted positioning across global equities, fixed income, commodities, and defensive assets (J.P. Morgan Asset Management, 2016).
THE EFFICIENTE 5 STRATEGY: DECONSTRUCTING INSTITUTIONAL METHODOLOGY
The Efficiente 5 Index, specifically, targets a 5% annualized volatility. Let that sink in for a moment. While retail traders routinely accept 20%, 30%, or even 50% annual volatility in pursuit of returns, institutional allocators have determined that 5% volatility provides an optimal balance between growth potential and capital preservation. This isn't timidity. It's mathematics. At higher volatility levels, the compounding drag from large drawdowns becomes mathematically punishing. A 50% loss requires a 100% gain just to break even. The institutional solution: constrain volatility at the portfolio level, allowing the power of compounding to work unimpeded (Damodaran, 2008).
The strategy operates across thirteen exchange-traded funds spanning five distinct asset classes: developed equity markets (SPY, IWM, EFA), fixed income across the risk spectrum (TLT, LQD, HYG), emerging markets (EEM, EMB), alternatives (IYR, GSG, GLD), and defensive positioning (TIP, BIL). These aren't arbitrary choices. Each ETF represents a distinct factor exposure, and together they provide access to the primary drivers of global asset returns (Fama and French, 1993).
The methodology, as detailed in replication research by Jungle Rock (2025), follows a precise monthly cadence. At the end of each month, the strategy recalculates expected returns and volatilities for all thirteen assets using a 126-day rolling window. This six-month lookback balances responsiveness to changing market conditions against the noise of short-term fluctuations. The optimization engine then solves for the portfolio weights that maximize expected return subject to the 5% volatility target, with additional constraints to prevent excessive concentration.
These constraints are critical and reveal institutional wisdom that retail traders typically ignore. No single ETF can exceed 20% of the portfolio, except for TIP and BIL which can reach 50% given their defensive nature. At the asset class level, developed equities are capped at 50%, bonds at 50%, emerging markets at 25%, and alternatives at 25%. These aren't arbitrary limits. They're guardrails preventing the optimization from becoming too aggressive during periods when recent performance might suggest concentrating heavily in a single area that's been hot (Jorion, 1992).
After optimization, there's one final step that appears almost trivial but carries profound implications: weights are rounded to the nearest 5%. In a world of fractional shares and algorithmic execution, why round to 5%? The answer reveals institutional practicality over mathematical purity. A portfolio weight of 13.7% and 15.0% are functionally similar in their risk contribution, but the latter is vastly easier to communicate, to monitor, and to execute at scale. When you're managing billions, parsimony matters.
WHY THIS MATTERS FOR RETAIL: THE GAP BETWEEN APPROACH AND EXECUTION
Here's the uncomfortable reality: most retail traders are playing a different game entirely, and they don't even realize it. When a retail trader says "I'm bullish on tech," they buy QQQ and that's their entire technology exposure. When they say "I need some diversification," they buy ten different stocks, often in correlated sectors. This isn't diversification in the Markowitzian sense. It's concentration with extra steps.
The institutional approach represented by the Efficiente 5 is fundamentally different in several ways. First, it's systematic. Emotions don't drive the allocation. The mathematics do. When equities have rallied hard and now represent 55% of the portfolio despite a 50% cap, the system sells equities and buys bonds or alternatives, regardless of how bullish the headlines feel. This forced contrarianism is what retail traders know they should do but rarely execute (Kahneman and Tversky, 1979).
Second, it's forward-looking in its inputs but backward-looking in its process. The strategy doesn't try to predict the next crisis or the next boom. It simply measures what volatility and returns have been recently, assumes the immediate future resembles the immediate past more than it resembles some forecast, and positions accordingly. This humility regarding prediction is perhaps the most institutional characteristic of all.
Third, and most critically, it treats the portfolio as a single organism. Retail traders typically view their holdings as separate positions, each requiring individual management. The institutional approach recognizes that what matters is not whether Position A made money, but whether the portfolio as a whole achieved its risk-adjusted return target. A position can lose money and still be a valuable contributor if it reduced portfolio volatility or provided diversification during stress periods.
THE MATHEMATICAL FOUNDATION: MEAN-VARIANCE OPTIMIZATION IN PRACTICE
At its core, the Efficiente 5 strategy solves a constrained optimization problem each month. In technical terms, this is a quadratic programming problem: maximize expected portfolio return subject to a volatility constraint and position limits. The objective function is straightforward: maximize the weighted sum of expected returns. The constraint is that the weighted sum of variances and covariances must not exceed the volatility target squared (Markowitz, 1959).
The challenge, and this is crucial for understanding the Pine Script implementation, is that solving this problem properly requires calculating a covariance matrix. This 13x13 matrix captures not just the volatility of each asset but the correlation between every pair of assets. Two assets might each have 15% volatility, but if they're negatively correlated, combining them reduces portfolio risk. If they're positively correlated, it doesn't. The covariance matrix encodes these relationships.
True mean-variance optimization requires matrix algebra and quadratic programming solvers. Pine Script, by design, lacks these capabilities. The language doesn't support matrix operations, and certainly doesn't include a QP solver. This creates a fundamental challenge: how do you implement an institutional strategy in a language not designed for institutional mathematics?
The solution implemented here uses a pragmatic approximation. Instead of solving the full covariance problem, the indicator calculates a Sharpe-like ratio for each asset (return divided by volatility) and uses these ratios to determine initial weights. It then applies the individual and asset-class constraints, renormalizes, and produces the final portfolio. This isn't mathematically equivalent to true mean-variance optimization, but it captures the essential spirit: weight assets according to their risk-adjusted return potential, subject to diversification constraints.
For retail implementation, this approximation is likely sufficient. The difference between a theoretically optimal portfolio and a very good approximation is typically modest, and the discipline of systematic rebalancing across asset classes matters far more than the precise weights. Perfect is the enemy of good, and a good approximation executed consistently will outperform a perfect solution that never gets implemented (Arnott et al., 2013).
RETURNS, RISKS, AND THE POWER OF COMPOUNDING
The Efficiente 5 Index has, historically, delivered on its promise of 5% volatility with respectable returns. While past performance never guarantees future results, the framework reveals why low-volatility strategies can be surprisingly powerful. Consider two portfolios: Portfolio A averages 12% returns with 20% volatility, while Portfolio B averages 8% returns with 5% volatility. Which performs better over time?
The arithmetic return favors Portfolio A, but compound returns tell a different story. Portfolio A will experience occasional 20-30% drawdowns. Portfolio B rarely draws down more than 10%. Over a twenty-year horizon, the geometric return (what you actually experience) for Portfolio B may match or exceed Portfolio A, simply because it never gives back massive gains. This is the power of volatility management that retail traders chronically underestimate (Bernstein, 1996).
Moreover, low volatility enables behavioral advantages. When your portfolio draws down 35%, as it might with a high-volatility approach, the psychological pressure to sell at the worst possible time becomes overwhelming. When your maximum drawdown is 12%, as might occur with the Efficiente 5 approach, staying the course is far easier. Behavioral finance research has consistently shown that investor returns lag fund returns primarily due to poor timing decisions driven by emotional responses to volatility (Dalbar, 2020).
The indicator displays not just target and actual portfolio weights, but also tracks total return, portfolio value, and realized volatility. This isn't just data. It's feedback. Retail traders can see, in real-time, whether their actual portfolio volatility matches their target, whether their risk-adjusted returns are improving, and whether their allocation discipline is holding. This transparency transforms abstract concepts into concrete metrics.
WHAT RETAIL TRADERS MUST LEARN: THE MINDSET SHIFT
The path from retail to institutional thinking requires three fundamental shifts. First, stop thinking in positions and start thinking in portfolios. Your question should never be "Should I buy this stock?" but rather "How does this position change my portfolio's expected return and volatility?" If you can't answer that question quantitatively, you're not ready to make the trade.
Second, embrace systematic rebalancing even when it feels wrong. Perhaps especially when it feels wrong. The Efficiente 5 strategy rebalances monthly regardless of market conditions. If equities have surged and now exceed their target weight, the strategy sells equities and buys bonds or alternatives. Every retail trader knows this is what you "should" do, but almost none actually do it. The institutional edge isn't in having better information. It's in having better discipline (Swensen, 2009).
Third, accept that volatility is not your friend. The retail mythology that "higher risk equals higher returns" is true on average across assets, but it's not true for implementation. A 15% return with 30% volatility will compound more slowly than a 12% return with 10% volatility due to the mathematics of return distributions. Institutions figured this out decades ago. Retail is still learning.
The Efficiente 5 replication indicator provides a bridge. It won't solve the problem of prediction no indicator can. But it solves the problem of allocation, which is arguably more important. By implementing institutional methodology in an accessible format, it allows retail traders to see what professional portfolio construction actually looks like, not in theory but in executable code. The the colorful lines that retail traders love to draw, don't disappear. They simply become less central to the process. The portfolio becomes central instead.
IMPLEMENTATION CONSIDERATIONS AND PRACTICAL REALITY
Running this indicator on TradingView provides a dynamic view of how institutional allocation would evolve over time. The labels on each asset class line show current weights, updated continuously as prices change and rebalancing occurs. The dashboard displays the full allocation across all thirteen ETFs, showing both target weights (what the optimization suggests) and actual weights (what the portfolio currently holds after price movements).
Several key insights emerge from watching this process unfold. First, the strategy is not static. Weights change monthly as the optimization recalibrates to recent volatility and returns. What worked last month may not be optimal this month. Second, the strategy is not market-timing. It doesn't try to predict whether stocks will rise or fall. It simply measures recent behavior and positions accordingly. If volatility has risen, the strategy shifts toward defensive assets. If correlations have changed, the diversification benefits adjust.
Third, and perhaps most importantly for retail traders, the strategy demonstrates that sophistication and complexity are not synonyms. The Efficiente 5 methodology is sophisticated in its framework but simple in its execution. There are no exotic derivatives, no complex market-timing rules, no predictions of future scenarios. Just systematic optimization, monthly rebalancing, and discipline. This simplicity is a feature, not a bug.
The indicator also highlights limitations that retail traders must understand. The Pine Script implementation uses an approximation of true mean-variance optimization, as discussed earlier. Transaction costs are not modeled. Slippage is ignored. Tax implications are not considered. These simplifications mean the indicator is educational and analytical, not a fully operational trading system. For actual implementation, traders would need to account for these real-world factors.
Moreover, the strategy requires access to all thirteen ETFs and sufficient capital to hold meaningful positions in each. With 5% as the rounding increment, practical implementation probably requires at least $10,000 to avoid having positions that are too small to matter. The strategy is also explicitly designed for a 5% volatility target, which may be too conservative for younger investors with long time horizons or too aggressive for retirees living off their portfolio. The framework is adaptable, but adaptation requires understanding the trade-offs.
CAN RETAIL TRULY COMPETE WITH INSTITUTIONS?
The honest answer is nuanced. Retail traders will never have the same resources as institutions. They won't have Bloomberg terminals, proprietary research, or armies of analysts. But in portfolio construction, the resource gap matters less than the mindset gap. The mathematics of Markowitz are available to everyone. ETFs provide liquid, low-cost access to institutional-quality building blocks. Computing power is essentially free. The barriers are not technological or financial. They're conceptual.
If a retail trader understands why portfolios matter more than positions, why systematic discipline beats discretionary emotion, and why volatility management enables compounding, they can build portfolios that rival institutional allocation in their elegance and effectiveness. Not in their scale, not in their execution costs, but in their conceptual soundness. The Efficiente 5 framework proves this is possible.
What retail traders must recognize is that competing with institutions doesn't mean day-trading better than their algorithms. It means portfolio-building better than their average client. And that's achievable because most institutional clients, despite having access to the best managers, still make emotional decisions, chase performance, and abandon strategies at the worst possible times. The retail edge isn't in outsmarting professionals. It's in out-disciplining amateurs who happen to have more money.
The J.P. Morgan Efficiente 5 Index Replication indicator serves as both a tool and a teacher. As a tool, it provides a systematic framework for multi-asset allocation based on proven institutional methodology. As a teacher, it demonstrates daily what portfolio thinking actually looks like in practice. The colorful lines remain on the chart, but they're no longer the focus. The portfolio is the focus. The risk-adjusted return is the focus. The systematic discipline is the focus.
Stop painting lines. Start building portfolios. The institutions have been doing it for seventy years. It's time retail caught up.
REFERENCES
Arnott, R. D., Hsu, J., & Moore, P. (2013). Fundamental Indexation. Financial Analysts Journal, 61(2), 83-99.
Bernstein, W. J. (1996). The Intelligent Asset Allocator. New York: McGraw-Hill.
Dalbar, Inc. (2020). Quantitative Analysis of Investor Behavior. Boston: Dalbar.
Damodaran, A. (2008). Strategic Risk Taking: A Framework for Risk Management. Upper Saddle River: Pearson Education.
Elton, E. J., Gruber, M. J., Brown, S. J., & Goetzmann, W. N. (2014). Modern Portfolio Theory and Investment Analysis (9th ed.). Hoboken: John Wiley & Sons.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
Jorion, P. (1992). Portfolio optimization in practice. Financial Analysts Journal, 48(1), 68-74.
J.P. Morgan Asset Management. (2016). Guide to the Markets. New York: J.P. Morgan.
Jungle Rock. (2025). Institutional Asset Allocation meets the Efficient Frontier: Replicating the JPMorgan Efficiente 5 Strategy. Working Paper.
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291.
Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91.
Markowitz, H. (1959). Portfolio Selection: Efficient Diversification of Investments. New York: John Wiley & Sons.
Swensen, D. F. (2009). Pioneering Portfolio Management: An Unconventional Approach to Institutional Investment. New York: Free Press.
S&P Trading System with PivotsThe S&P Trading System with Pivots is a TradingView indicator designed for the 30-minute SPX chart to guide SPY options trading. It uses a trend-following strategy with:
10 SMA and 50 SMA: Plots a 10-period (blue) and 50-period (red) Simple Moving Average. A bullish crossover (10 SMA > 50 SMA) signals a potential buy (green triangle below bar), while a bearish crossunder (10 SMA < 50 SMA) signals a sell or exit (red triangle above bar).
Trend Bias: Colors the background green (bullish) or red (bearish) based on SMA positions.
Pivot Points: Marks recent highs (orange circles) and lows (purple circles) as potential resistance and support levels, using a 5-bar lookback period.
