Do you know what mistake most traders make when they try to copy Warren Buffett in TradingView?

They open a chart and start looking low P/E ratio stocks.

That is already the wrong frame.

Buffett’s edge was never “finding the low P/E stocks or breakout patterns” His edge was identifying businesses with exceptional economics, buying them at sensible prices, and then holding them long enough for the business to do the work.

That is why TradingView can still be useful here.

Not because it turns Buffett into a trader. Because it gives you one place to do the first pass:

• long-term charts

• stock screening

• core fundamental filters

• watchlists

Then AI does (hard, boring, time wasting stuff) the second pass:

• summarize filings

• check moat risk

• surface accounting red flags

• scan for scuttlebutt signals

• entry signals

• exit signals

• buy more

• sell more

• hold

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Let’s begin….

Berkshire Hathaway (CEO Warren Buffett) did not beat the market for decades because Buffett had a better oscillator. He focused on business quality, capital allocation, patience, and price discipline.

And if you look at Berkshire’s portfolio, the pattern is obvious: concentration in businesses with durable economics, not random ticker collecting.

Buffett In TradingView Is A Filter, Not A Signal

If you came here looking for a fast trading system, this is not that.

Buffett’s strategy is closer to a business selection framework than a traditional trading strategy. TradingView matters because it helps you narrow the field fast, not because it replaces business analysis.

That means three things:

• the chart is context, not the thesis

• the business comes before the ticker

• the hold period is measured in years, not hours

That is why a monthly chart matters more than a five-minute chart here.

If a business does not look holdable for years, it is the wrong candidate no matter how pretty the short-term setup looks.

The real edge in a chart like that was not one perfect entry. The edge was recognizing the quality of the business early enough to stay with it.

Buffett’s Contrarian Indicator

One Buffett line matters here more than any technical rule:

“Be fearful when others are greedy, and greedy when others are fearful.”

That is what this image is visualizing.

The lower pane is not a normal momentum tool. It is a fear and greed style contrarian indicator. When sentiment gets washed out and turns up from fear, the chart marks those areas as FEAR = WARREN BUY. When sentiment gets stretched and euphoric, it marks them as GREED = WARREN SELL.

That does not mean Buffett used one indicator as a mechanical trading system.

It means he understood that crowd psychology creates opportunity at the extremes. This is a practical TradingView way to see those extremes on a long-term chart.

Where AI Fits In

This is where the workflow becomes much more practical.

Buffett could spend days reading annual reports, shareholder letters, transcripts, and industry material on one company.

Most retail investors will not do that consistently.

AI helps because it can do the heavy first pass much faster:

• summarize the last 10-K and 10-Q

• flag receivables growing faster than revenue

• compare margins and leverage with peers

• surface recurring complaints from employees or customers

• check whether management sounds candid or promotional

That does not replace judgment.

It just means you stop wasting hours on companies that should have been rejected in the first ten minutes.

The Real Bottleneck Is Still Reading

Buffett’s edge was not just patience or IQ. A big part of it was the willingness to read far more than almost anyone else.

That is still the bottleneck now.

Most investors do not lose because they failed to find a chart. They lose because they never did enough work on the business behind the chart.

That is where AI (now) actually helps.

Not by replacing judgment. And not by magically finding winning stocks.

It helps by doing the heavy first-pass reading much faster.

If Buffett’s process meant working through huge piles of reports, filings, letters, transcripts, and outside commentary, the modern version is simpler:

• TradingView narrows the universe

• AI reads the business faster

• you make the final judgment

What Each Part Is For

1. The screener

This part saves time first.

Instead of opening random charts and trying to justify them afterwards, you begin with a shortlist of businesses that already meet Buffett-style quality conditions. That removes a lot of noise before the deeper work starts.

2. The indicator

This is not there to tell you what to buy.

It is there to add sentiment context after the business has already passed the quality filter. That makes it more useful for spotting “good business under temporary fear” and less useful for rationalizing weak businesses that just got cheaper.

3. The buy prompt

This is the part that compresses research time.

You use it after a stock passes the screen. The prompt tells AI to review the business the way a Buffett-style analyst would: profitability, margins, debt, cash generation, moat, management, valuation, and outside scuttlebutt.

The point is not to let AI think for you. The point is to turn hours of first-pass reading into one structured memo so you can move faster to a real buy, watch, or pass decision.

4. The exit prompt

This is the part that helps with loss avoidance.

Most investors spend almost all their time on entries and very little time on monitoring whether the thesis is still intact. The exit prompt keeps checking for the things that actually break long-term investments: moat erosion, accounting issues, management drift, and capital-allocation mistakes.

Used properly, it helps you notice deterioration earlier instead of sitting in a broken thesis because the chart still looks fine.

The Full Workflow Is Here

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On March 2nd Trump had made an announcement (us and iran war) over the weekend that was clearly bullish for oil.

I knew it and I thought about it.

And when Monday opened and OIL started moving hard, I watched from the sideline because I had no actual plan for how to get in.

I spent the next two days reading other people talk about how clean that trade was.

I knew and they knew I knew. And it didn’t matter because knowing something is coming and having a system for trading it are two completely different things.

That’s the thing about FOMO. It’s not about missing money but it’s really about knowing and still not acting. I started chasing setups that were already over just to feel like I wasn’t standing still. That made everything worse.

So I stopped and actually fixed it. I built an intraday momentum trading strategy specifically for these situations: news-driven moves, macro catalysts, the moments where real institutional money is moving in one direction and the chart is telling you exactly that. A process with rules, not gut feel…

Here’s what you get in this article:

• Why VWAP is the right tool for these moves (and what it’s actually measuring, not support/resistance)

• How Stochastic RSI times the entry within the move

• Two methods I use to find these symbols before they’re obvious

• The full custom TradingView indicator with buy/sell signals and alerts

• Step-by-step TradingView screener setup

These Moves Don’t Happen Every Day, That’s the Point

I’m not trying to trade every day.

These large intraday moves (driven by real catalysts, real institutional order flow) happen maybe three or four times a week. Some weeks fewer.

When they happen, they’re worth being in. The question is how.

Why VWAP and What It’s Actually Telling You

VWAP (the Volume Weighted Average Price) tells you who is in control of the session. It is the average price paid for every share traded, weighted by volume, recalculated continuously throughout the day.

Most traders use it as a support/resistance line.

Price comes near VWAP, does it bounce or break. That’s not wrong but it misses what VWAP is actually measuring.

When price is trading above VWAP, buyers have been more aggressive on average for the entire day. They’ve consistently paid above the volume-weighted average to get their position. That’s not a retail trader doing that. That’s size. When price is below VWAP, sellers are in control. The distance from VWAP shows how strong that pressure is.

This is why institutions use VWAP as an execution benchmark. When a stock holds above VWAP through the first 30 minutes of a news-driven move, institutional buyers are active and defending their position. When price can’t reclaim VWAP after a gap-up, the move has no real backing. Retail excitement getting absorbed by sellers.

For momentum trading this is the filter. VWAP tells you direction and pressure. You only take long setups when price is above it. Short setups only when price is below it. That one filter alone removes a huge portion of low-quality entries.

(I use VWAP differently in The Simple VWAP Strategy That Turns Pullbacks Into Consistent Gains, a mean reversion approach on SPY. Same indicator, completely different application.)

How Stochastic RSI Times Intraday Entries

Stochastic RSI measures where the current RSI reading sits relative to its own range over a lookback period. It moves between 0 and 100. Below 20 is oversold, price has pulled back sharply on a short-term basis. Above 80 is overbought.

Two Methods to Find News-Driven Setups Before They’re Obvious

The system only works if you’re watching the right symbols. Here’s how I find them.

Method 1: News-driven symbols

• This is the cleanest version. A stock is moving because of a specific real catalyst: earnings, an oil inventory report, a Fed announcement, a Trump weekend tweet that everyone saw coming. Real news, real directional order flow.

• When I see a symbol making a large move on real news, I open the chart and check two things: is price above or below VWAP, and is Stoch RSI showing an extreme reading after a pullback. If the conditions are there, I wait for the signal.

• This month OIL was this exact situation. The catalyst was known and very very clear. The direction was obvious. The system would have given me the entry. I just didn’t have it yet.

Method 2: TradingView Screener

• Not every strong move makes the news immediately. Some stocks gap up 8% on volume before anyone’s talking about them. I run a screener every morning to catch these.

• The screener filters for symbols with significant intraday moves and elevated volume, the exact conditions where this setup performs best. I’ll show you the exact screener setup in the paid section including which filters to use and how to save it as a template.

Now I will Give You:

1. The full entry and exit rules

2. The exact Stoch RSI settings and crossover trigger I use

3. The complete custom TradingView (PineScript) indicator with buy/sell signals

4. Alert conditions

5. And the step-by-step TradingView screener setup with exact filter values.

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I am back now. If you have been here since the start, I appreciate you. If you just found this subscribe now.

Now, the new trading & investing strategies will be released weekly!

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One more thing before we get into it. I am considering going deeper than just strategy posts, sharing which ones I actually trade myself, real position sizes, what I am currently in. If that kind of access interests you, send me a DM. If not, that is completely fine too. Either way, weekly strategies keep coming.

Now let’s get into it.

1.38% max drawdown, over 28 years, on gold, no bad at all.

I’ve backtested hundreds of strategies and I can count on one hand how many kept drawdown below 5%. This one barely touched 1.4%.

The strategy is dead simple.

• Buy gold when it breaks to a new high, but only when the trend already agrees with you.

• Exit when the trend cracks or when momentum gets too stretched.

• That’s it. No machine learning or AI. Three indicators doing one job each.

In this article you get:

1. GOLD Trading Strategy

2. PineScript Code to use in Tradingview

3. Full Strategy Rules

Gold is one of the hardest assets to trade with breakout systems. It trends for months, then chops sideways and eats your account alive with false signals. The standard approach is to buy when price breaks the 20-day high. Donchian channel style. Classic trend following.

On gold, raw Donchian breakouts are a coin flip.

You get chopped in the range, stopped out, re-enter, stopped out again. By the time gold actually runs, you’ve already burned through half your patience and a quarter of your risk budget.

This strategy fixes that with two filters.

I ran this on XAUUSD daily from April 1998 to March 2026. Starting capital $1,000,000. Position size 10% of equity per trade. Commission 0.05% (+Slippage) to keep it real.

104 trades in 28 years. That’s about 4 trades per year. The strategy spends most of its time doing nothing. Waiting. Watching the EMA alignment and the Donchian level. When it fires, it fires into confirmed momentum.

Win rate: 40.38%. Only 42 of 104 trades were profitable.

That number would scare off every indicator junkie looking for 70%+ hit rates. But look at the profit side. Gross profit $233,641. Gross loss $126,286. The winners are almost double the losers in dollar terms. Profit factor 1.85.

And the drawdown. $13,789 peak to trough, which on a million dollar account is 1.38%. You could run this in the background and barely notice the losing streaks. The equity curve looks like a slow escalator from 1998 to 2020, then it ramps. Gold’s post-2020 bull run sent this thing vertical.

Expected payoff per trade: $1,032. Across 104 trades.

Strategy includes alerts too:

The full PineScript code is below. Copy it into TradingView, apply it to XAUUSD on the daily chart.

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Do you have a trading plan.

Do you buy on emotion. Do you hold losers too long or cut winners too short?

Today, I’m sharing a simple trend following strategy that solves these problems with one elegant rule: let your winners run, and cut your losers at exactly 1%.

The results? Testing on SPY:

• +19.31% total return

• Only 2.4% maximum drawdown

• Positive profit factor of 1.84

Let’s break it down.

Why Trend Following Works

Trend following is one of the oldest and most reliable trading approaches. The logic is simple:

“The trend is your friend until it ends.”

Markets tend to move in persistent directions. When a stock or index starts moving up, it often continues up. When it starts moving down, it often continues down.

The challenge isn’t identifying trends but it’s actually knowing when to get out.

This strategy solves that with a dual-stop system:

1. Fixed 1% Stop Loss: Protects you from catastrophic losses

2. Trailing Stop: Locks in profits as the trend extends

The strategy in action on SPY daily chart. Notice how the trailing stop (red line) follows price up during the uptrend.

The Backtest Results

I tested this strategy on SPY (S&P 500 ETF) from January 1993 to January 2026—over 30 years of market data.

Key Metrics

Metric value

• Total P&L +$19,359.23 (+19.31%)

• Max Drawdown $2,496.85 (2.40%)

• Total Trades256

• Win Rate 41.80% (107/256)

• Profit Factor 1.84

The equity curve shows steady growth over 30+ years with minimal drawdowns.

What These Numbers Mean

Win Rate of 41.80% - You might think this is low, but trend following strategies typically win less than 50% of the time. The key is that winners are much bigger than losers.

Profit Factor of 1.84 - For every $1 lost, the strategy makes $1.84. This is a healthy ratio that indicates a robust edge.

Max Drawdown of 2.40% - This is exceptionally low. Most strategies experience 20-40% drawdowns. The tight stops keep losses small.

How the Strategy Works (High Level)

The core logic is straightforward:

1. Enter Long when price crosses above a Moving Average

2. Enter Short when price crosses below a Moving Average

3. Exit when either the fixed stop or trailing stop is hit

The genius is in the stop loss system:

• The 1% fixed stop ensures you never lose more than 1% from your entry price

• The trailing stop moves up as price moves in your favor, locking in profits

• Whichever stop is tighter (closer to current price) is the active stop

This means:

• Small losses are capped at 1%

• Big winners are allowed to run until the trend reverses

A winning trade showing entry, trailing stop movement, and exit.

Who Is This Strategy For?

This strategy is ideal for:

✅ Beginners - Simple rules, easy to follow
✅ Swing Traders - Works on daily timeframe, no day trading required
✅ Risk-Averse Traders - Tiny 2.4% max drawdown
✅ Busy Professionals - Only need to check once per day

This strategy is NOT ideal for:

❌ Scalpers looking for intraday action
❌ Traders who live in the dream and want 100% win-rate scam
❌ Those seeking 100%+ annual returns (this is steady, not explosive)

Setting Up Alerts

One powerful feature is you can set up alerts to notify you when entry signals occur:

Setting up alerts in TradingView for the strategy signals.

This means you don’t need to watch charts all day - the strategy will notify you when it’s time to act.

The Full Strategy Rules + Pine Script code

Now below I’m sharing the complete strategy rules and ready-to-use TradingView PineScript code with paid subscribers.

What you’ll get:

✓ Exact entry and exit rules
✓ All configurable parameters explained
✓ Complete PineScript v6 code (copy-paste ready)
✓ Parameter optimization tips
✓ Best timeframes and markets

Subscribe now to unlock everything!

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I tested this approach on SPY 1-day charts using price-volume correlation signals.

The strategy identifies reversal setups when price and volume move in opposite directions. When correlation turns strongly negative and price is near session lows, it signals potential mean reversion opportunities.

The edge comes from catching institutional selling pressure followed by capitulation.

Problem

How do you identify when smart money is done selling?

What happens when volume spikes during price declines?

Can volume divergences predict short-term reversals?

I tested a method that correlates price action with volume flow. The correlation between these two tells a story about market sentiment.

Strategy Overview

This approach monitors the relationship between price and volume over a 10-day lookback period.

When the correlation becomes strongly negative (below -0.7) and price is near the low of the bar (IBS < 0.2), it enters long positions. The strategy exits when a 2-period RSI reaches overbought levels above 70.

• Calculates 10-day correlation between closing price and volume

• Computes Internal Bar Strength (IBS) to measure price position within the bar

• Enters long when correlation < -0.7 and IBS < 0.2

• Exits when 2-period RSI exceeds 70

• Uses 10% of equity per trade with no pyramiding

• Tracks correlation and IBS levels visually

The full rules and Pine Script code come later.

Backtest Setup

• Symbol: SPY (SPDR S&P 500 ETF Trust)

• Timeframe: 1 day

• Date Range: 1993 - 2025 (32+ years)

• Slippage: 1

• Commissions: 0.05% per trade

• Position Sizing: 10% of equity per trade

• Execution: Market orders on signal

• Initial Capital: $10,000

Results and Metrics

The strategy delivered solid performance over three decades of market data:

• Total trades: 167

• Win rate: 73%

• Maximum drawdown: -21%

• Tested period: 1993-2025

• Timeframe: Daily bars

• Asset: SPY (S&P 500 ETF)

These results show the strategy maintained profitability through multiple market cycles, including the dot-com bubble, financial crisis, and recent volatility periods.

Does It Works?

Strong negative correlation between price and volume often signals institutional capitulation. When large players are selling heavily (high volume) but price isn’t dropping as much as expected, it creates a correlation below -0.7.

The IBS indicator ensures we’re entering near the bottom of the bar, catching price at support levels. IBS below 0.2 means price closed very close to the session low, indicating selling exhaustion.

This combination catches the moment when panic selling peaks and smart money starts accumulating. The short 2-period RSI exit takes profits quickly when momentum shifts, avoiding the risk of giving back gains in extended moves.

The strategy works well on SPY because it captures the broader market sentiment shifts that affect the entire index. Daily timeframe provides enough signal stability while allowing frequent trading opportunities.

Takeaways & Next Steps

Here are adjustments you can try to improve performance:

• Test different correlation lookbacks. Default is 10 days. Try 7, 14, or 20 to match different market cycles

• Adjust correlation threshold. Default is -0.7. Test -0.6 or -0.8 for different sensitivity

• Modify IBS threshold. Default is 0.2. Try 0.1 or 0.3 for different entry aggressiveness

• Change RSI exit level. Default is 70. Test 75 or 80 for different profit targets

• Test different RSI periods. Default is 2. Try 3 or 4 for slower exits

• Adjust position sizing. Default is 10% of equity. Reduce during high volatility periods

• Add trend filters to avoid counter-trend entries during strong trends

Strategy Rules + TradingView (PineScript) Code

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The strategy uses RSI crossovers for entries and ATR-based trailing stops for exits. Gold’s break trends work well with mean reversion signals.

There’s a twist in how the trailing stop updates.

Problem

How do you protect profits without exiting too early?

What happens when volatility changes during a trade?

Can mean reversion work on trending instruments like gold?

I tested a method that uses ATR to adjust stops dynamically. The trailing mechanism matters more than I expected.

Strategy Overview

This approach watches for RSI extreme readings.

When RSI crosses above 60, it enters long. When RSI crosses below 35, it enters short. The strategy uses ATR-based stops that trail with price action.

• Enters long when RSI crosses above upper threshold (default: 60)

• Enters short when RSI crosses below lower threshold (default: 35)

• Sets initial stop at entry ± (ATR × 4)

• Trails stop using ATR calculation each bar

• Optional take profit at entry ± (ATR × 7)

The full rules and Pine Script code come later.

Backtest Setup

• Symbol: XAUUSD (Gold)

• Timeframe: 1 hour

• Date Range: Configurable start date (default: January 1, 2023)

• RSI Length: 40 periods

• RSI Upper: 60

• RSI Lower: 35

• ATR Period: 200

• Trailing Stop: 4x ATR

• Take Profit: 7x ATR (optional)

• Commissions: 0.5% per trade

• Position Sizing: 10% of equity per trade

• Execution: Orders on bar close

• Initial Capital: $10,000

Results and Metrics

Why It Works

Gold experiences frequent trend breaks on 1-hour charts. Extended moves reverse when they reach extremes. RSI identifies these extremes when it crosses 60 or 35.

The 200-period ATR measures volatility over a long lookback. Multiplying by 4 creates stops that scale with current volatility. During calm periods, stops are tighter. During volatile periods, stops widen.

The trailing stop updates each bar. For longs, it uses max(current stop, low - ATR × 4). For shorts, it uses min(current stop, high + ATR × 4). This means the stop only moves in your favor, never against you.

When price moves favorably, the trailing stop locks in gains. When price reverses, the stop triggers an exit. The ATR calculation keeps stops at appropriate distances from price action.

Gold’s break trends create clear reversal points. RSI crossovers catch these reversals. ATR-based stops handle gold’s volatility swings. Trailing stops capture extended moves after trend breaks.

Takeaways & Next Steps

Here are adjustments you can try to improve performance:

• Test different RSI periods. The default uses 40. Try 30, 50, or 60 to match gold’s cycles

• Adjust RSI thresholds. Default is 60/35. Test 65/30 or 55/40 for different sensitivity

• Change trailing stop multiplier. Default is 4x ATR. Try 3x for tighter stops or 5x for wider stops

• Modify take profit multiplier. Default is 7x ATR. Test 5x, 6x, or 8x to balance profit capture

• Test different ATR periods. Default is 200. Try 100 or 300 to adjust volatility sensitivity

• Adjust position sizing. Default is 10% of equity. Reduce if drawdowns are too high

• Add time filters to avoid low-liquidity periods

• Test on other timeframes to see if 1-hour is optimal

Strategy Rules + Pine Script Code

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Let’s talk about mean reversion in the forex markets.

Forex markets move in waves. Price stretches too far, then snaps back. Most traders chase breakouts and miss the real opportunity.

What if you could spot those moments when price is about to reverse?

Mean reversion strategies are common in Forex. Every trader has tried them. Most fail because they enter at the wrong time.

I’ve been testing mean reversion setups on hourly charts for years. Some work. Most don’t. This one caught my attention because it filters out the noise that kills other reversal strategies.

The setup uses two moving averages and an pullback reading. The combination creates an edge that’s worth sharing.

Here’s what makes this different: it only trades reversals when they’re likely to succeed. Not every oversold reading is a buy signal. Not every overbought reading is a sell signal.

Problem To Solve

Mean reversion sounds simple: buy low, sell high. But when is price actually low?

How do you avoid catching falling knives in a downtrend?

When should you ignore oversold signals and wait for a better setup?

The answer lies in a simple filter that most traders overlook.

Strategy Overview

This strategy trades short-term reversals in Forex markets using 1-hour charts. It combines pullback extremes with moving average trend filters to enter only when price is temporarily stretched within a larger trend.

• Trade both long and short sides on major Forex pairs (Only mean reverting fx pairs)

• Use “pullback“ to identify extreme readings

• Filter entries with SMA(50) and SMA(20) to stay aligned with trend

• And limit exit rules

The key is entering oversold conditions in uptrends and overbought conditions in downtrends. Not the other way around.

Backtest Setup

• Symbols: Mean Reverting Forex pairs

• Timeframe: 1 hour

• Date Range: Varies by pair availability

• Position Sizing: 100% of equity per trade

• Starting Capital: $10,000

Results and Metrics

This strategy performs best in ranging markets where mean reversion occurs regularly. Results vary by currency pair and market conditions.

I’ve tested this across multiple pairs and time periods. Here’s what I found:

• Works consistently on major pairs during normal volatility

• Requires patience during strong trending periods; signals are less frequent but often higher quality

• Simple rules make it easy to follow and test; no complex calculations needed

• Not every signal is a winner; but the filters improve the win rate significantly

The key is consistency. Follow the rules exactly. Don’t skip the filters. They’re there for a reason.

Why It Works

Forex markets spend more time ranging than trending. Even in trends, price rarely moves in straight lines. It pulls back, consolidates, then continues.

This behavior creates opportunities. But only if you know when to take them.

The pullback indicator is sensitive. It reaches extreme readings quickly when price moves fast. But extreme readings alone aren’t enough. I’ve lost money trading pullback extremes without filters. Many traders have.

The SMA(50) acts as a trend filter. For longs, we only enter when price is above this line, ensuring we’re buying dips in uptrends, not catching falling knives. This single rule eliminates most losing trades.

The SMA(20) provides a short-term reference. Entering when price is below it in an uptrend means we’re buying weakness. Exiting when price returns to it captures the mean reversion move.

The same logic applies in reverse for shorts. We only sell when price is below SMA(50) and above SMA(20), ensuring we’re selling strength in downtrends.

This combination filters out the dangerous setups while keeping the profitable ones. It’s not perfect. No strategy is. But it’s better than trading pullback extremes blindly.

Takeaways & Next Steps

Mean reversion works in Forex, but only when you filter entries properly. The filters are what separate this from other reversal strategies.

• Test this on multiple pairs to find which ones show the best mean reversion behavior; not all pairs are created equal

• Consider adding a volatility filter to avoid trading during news events; high volatility can trigger false signals

• Track which sessions produce the best results; Forex pairs behave differently during different market hours

• Consider position sizing adjustments based on volatility; not all pairs move the same amount per pip

• Keep a trading journal; track which setups work and which don’t; patterns will emerge

Full Rules and TradingView (Pine Script) Code

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I’ve watched every classic Wall Street movie, and I’ve realized that every big winner does things differently from the average person.

Today, I’m going to show you an ETF strategy that caught 72% of trades correctly (460 out of 637) and turned every $1 risked into $1.80 in profit.

I’ve even read on trading forums that people using a similar system, especially SPY investors who’ve followed it since 1993 are now sitting on life-changing wealth.

This strategy works on any major ETF. It buys dips in strong uptrends and sells rallies in downtrends. (Video below)

And that’s exactly what I’m going to show you today, the full set of rules and the TradingView Pine Script code!

Why This Strategy Works So Well

New traders chase big moves. They buy when prices breakouts They sell when fear hits.

This strategy does the opposite.

It waits for calm moments in strong uptrends. Then it buys the small dips that always come back.

Think of it like buying discounted shoes during a sale. The store stays open. Customers keep coming. You get the shoes cheaper.

Same with ETFs. Strong trends bring buyers. Small dips create bargains. This strategy buys those bargains.

The Numbers Don’t Lie

Backtested on SPY from 1993 to 2025:

• 637 total trades

• 72.21% win rate

• Profit factor of 1.796

• Daily timeframe

That means 7 out of 10 trades made money. And winners beat losers by nearly 2-to-1.

Compare that to buy-and-hold SPY returns.

How The Strategy Spots Opportunities

The system uses three simple checks:

1. Is the trend strong?
It looks at a slow moving average. This confirms the ETF moves higher over time.

2. Is this dip small?
It compares price to a fast trend line. This finds brief pullbacks in strong moves.

3. Does the setup look right?
It checks if the day’s action fits the pattern.

When all three align, it buys the dip.

It sells when price hits recent highs or shows strength signals.

Watch How It Performs Across ETFs

I tested this strategy on different ETFs. The results surprised me.

Some ETFs work better than others. But the pattern holds across most liquid ones.

Watch this video to see the strategy in action across multiple ETFs:

Ready To Try It Yourself?

Trading strategies are one of the most valuable assets in the world, because if you keep following your system, you can achieve exponential growth.

The full strategy rules and code come next. ⬇️

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I’ve spent years (and still pending) looking for a “low-risk” entry that capitalizes on a simple truth of the market, institutional order flow matters.

A trading technique that captures this edge is one I found inspired by the teachings of Brian Shannon, a well-known technical analyst and pioneer of using the Volume Weighted Average Price (VWAP).

The concept is simple. The price near the VWAP is a magnetic area of high trading volume and institutional activity. Large money managers use the VWAP as a benchmark to execute huge orders without moving the market too much. When price comes back to this level, it’s coming back to where a lot of capital was recently traded, and this makes it a natural point of support or resistance.

The question then becomes…

How do we know if the VWAP will hold, or if the pullback is a fake-out?

The Strategy Overview

The strategy I tested combines the institutional anchor of the VWAP with a fast-acting Relative Strength Index (RSI) to find deep, temporary price movements that are likely to snap back towards the VWAP and then continue the current trend.

Here’s the core logic:

• We use the VWAP as our line in the sand; the price must be above it for long trades and below it for short trades.

• We only enter when the price comes very close to the VWAP, which shows a strong attraction back to the average trading price for the session.

• To filter for a strong, temporary pullback, we use a short-length RSI, specifically, RSI(2). When the price is above the VWAP but the RSI(2) is extremely oversold (below 30), it suggests the small counter-trend move is overdone and a bounce is due. This helps us avoid “catching a falling knife.”

This approach is about buying low in an uptrend and selling high in a downtrend, right at the point where the institutions are most likely to be trading.

Why This Works

Brian Shannon teaches that the Anchored VWAP (AVWAP) represents the “absolute truth of the relationship between a stock’s supply and demand” because it combines the three most important components: Price, Time, and Volume.

The VWAP’s power comes from volume. The price near the VWAP acts like a strong center of gravity because the most shares exchanged hands around that level. When a stock price moves away from VWAP, it is considered a departure from “fair value” for the day. Institutional traders love this indicator because it allows them to prove that they executed their large orders at or better than the market’s average price for the day.

When we see the price briefly undercut the VWAP and a short-term momentum indicator like RSI(2) screams “oversold” inside a bullish trend, we are catching the final flush of retail sellers before the big money steps back in to defend their average entry price.

Backtest Setup and Results

To ensure this strategy was strong and not a one-time fluke on a single chart, I tested it across multiple timeframes, including the:

15-minute

30-minute

1-hour

2-hour

and 3-hour charts.

I mainly used the 1-hour timeframe, which is often favored in Brian Shannon’s multi-timeframe analysis approach.

The goal of testing multiple timeframes was to prove the logic holds regardless of the chart setting, making the signal reliable.

The backtest results below use the 1-hour timeframe on the SPY ETF (the S&P 500 tracking fund) from January 2017 to November 2025.

Metric Result

• Total P&L:+5.10%

• Max Drawdown: 0.53%

• Total Trades: 254

• Profitable Trades: 45.67%

• Profit Factor: 1.692

• Instrument/Timeframe: SPY, 1-hour

The win rate is under 50%, which is completely fine, because the Profit Factor is 1.692, which is considered very strong. A high Profit Factor means that the winning trades are much larger than the losing trades. This shows a positive expectation, which is the most important component of any strategy.

Takeaways & Next Steps

This simple combination of VWAP and RSI(2) provides a low-risk, high-probability entry for trading pullbacks. The key is in the timing and staying close to the price where big players operate.

To make this strategy even better, here are a few steps you can take:

• Add a Trend Filter: Brian Shannon often uses the 5-day Simple Moving Average (SMA) as a powerful short-term trend filter. You should only take long signals if the price is above the 5-day SMA, and short signals if the price is below it.

• Manual Market Sentiment Check: Before taking a trade, check the overall market sentiment. If you get a long signal but the overall market (like the S&P 500) is clearly in a deep, aggressive sell-off, you should skip the signal. Only take trades that align with the higher-timeframe market flow.

• Multiple Timeframe Confirmation: Before entering on a 1-hour chart, check the 4-hour or Daily chart to ensure the trade is in the direction of the dominant trend, as taught in Brian Shannon’s work on multiple timeframes.

Full Rules + TradingView (Pinescript) Code

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Hello folks,

I tested this approach across three crypto markets.

The results show consistent returns with controlled drawdowns. Win rates sit below 50%, but profit factors remain strong.

There’s a twist in how the ranges get defined.

Problem

Can you catch breakouts without false signals?

How do you know when a small range is ready to break?

What happens if you wait too long?

I tested a method that counts consecutive price bars to build ranges. The entry timing matters more than I expected.

Strategy overview

This approach watches for short price consolidation periods.

When price moves in the same direction for three to five bars in a row, it draws a range box. The strategy enters when price breaks out of that range on the same bar.

• Identifies 3-5 consecutive bar streaks in one direction

• Marks the range from the streak start to end

• Enters long on upward breakout or short on downward breakout

• Uses 1:3 risk-reward ratio by default

• Moves stop to breakeven after price reaches 1R profit

• Exits all positions after two days on 15-minute charts

The full rules and Pine Script code come later.

Backtest setup

• Symbols: BTCUSDT, ETHUSDT, SOLUSDT

• Timeframe: 15 minutes

• Date Range: January 1, 2025 to August 29, 2025

• Commissions / Slippage: TradingView default settings

• Position Sizing: $100 USD risk per trade

• Execution Assumptions: Market orders on confirmed bar close

Results and metrics

All three markets delivered solid returns with drawdowns under 7%.

• BTCUSDT: +28.46% total return, 6.65% max drawdown, 65 trades, 36.92% win rate, 1.694 profit factor

  

• SOLUSDT: +29.96% total return, 6.31% max drawdown, 46 trades, 41.30% win rate, 2.105 profit factor

  

• ETHUSDT: +33.04% total return, 6.95% max drawdown, 79 trades, 35.44% win rate, 1.648 profit factor

Why it works

Short directional streaks often signal temporary consolidation. Three to five bars moving one way compress price into a tight zone.

When that zone breaks, it can show momentum starting. The breakout bar closes outside the range boundaries, confirming the move.

The 1:3 risk-reward setup means you need about 33% wins to break even. Win rates around 36-41% exceed that threshold, which explains the positive profit factors.

Moving stops to breakeven at 1R locks in profits early. This protects capital when trades reverse after initial gains.

Time-based exits limit exposure to overnight gaps and reduce the chance of giving back profits.

Takeaways & Next Steps

Here are adjustments you can try to improve performance:

• Test different streak lengths beyond 3-5 bars to see if longer consolidations work better

• Add volume filters to confirm breakouts happen with higher participation

• Experiment with tighter risk-reward ratios on shorter timeframes

• Use trend filters to avoid trading breakouts against larger market direction

• Adjust the max bars wait time to invalidate ranges faster in volatile markets

• Backtest on additional symbols to check robustness across different assets

Strategy Rules

1. Count consecutive bars where price moves in the same direction. An up bar has close greater than open. A down bar has close less than open.

2. When you see 3 to 5 consecutive bars in the same direction, mark a range. The range low is the minimum of the streak start open and the streak end close. The range high is the maximum of those two prices.

3. Draw a box around this range from the first bar of the streak to the current bar.

4. If the streak length goes above 5 bars, cancel the range and delete the box.

5. If 10 bars pass without a breakout, cancel the range and delete the box.

6. Wait for a breakout bar. On a breakout bar, price opens inside the range but closes outside the range.

7. Enter long if the bar breaks above the range high. Enter short if the bar breaks below the range low.

8. Set the stop loss at the midpoint between range high and range low.

9. Set the take profit at entry plus 3 times the risk distance for longs, or entry minus 3 times the risk distance for shorts.

10. Position size based on $100 USD risk. Calculate the dollar risk per point, then divide $100 by that value to get quantity.

11. When price reaches entry plus 1R for longs or entry minus 1R for shorts, move the stop loss to the entry price.

12. Close all positions after 192 bars on 15-minute charts, which equals 2 days.

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First I want to thank all of you, because I started last Wednesday and right now I have gained 84 subscribers. I never believed I can gain almost a 100 per one week.

But I’ve been watching stock traders chase breakouts for years. They swear by names like Mark Minervini and Qullamaggie, guys who built their reputations on tight stops and momentum bursts. But does that edge exist in crypto?

So… I decided to stop guessing and test it myself on Bitcoin. What I found surprised me.

Problem

Stock breakout strategies rely on a simple idea:

1. buy strength,

2. cut losses fast,

3. and let winners run.

But crypto is a different. (No earning/FDA/news catalysts)

Does the same logic hold when markets trade 24/7 with wild volatility?

Can you beat buy-and-hold with a systematic approach?

Or do whipsaws and overnight gaps destroy your account before you catch a move? I needed answers.

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Strategy Overview

The Qullamaggie Breakout strategy buys when price breaks above recent swing highs, but only when the bigger trend is intact. It uses moving averages to confirm direction and trailing stops to lock in profits. The goal is to catch explosive moves while avoiding dead zones.

• Buy breakouts above pivot highs in the direction of the trend

• Use multiple moving averages (10, 20, 50) to filter trades and trail stops

• Stop out fast when price breaks structure

• Trail stops using moving averages once profitable

But there’s more to the setup than that.

Backtest Setup

• Symbols: BTCUSDT

• Timeframe: Daily (1D)

• Date Range: August 17, 2017 to October 22, 2025

• Commissions / Slippage: 0.1% per trade

• Position Sizing: 100% of equity per trade

• Execution Assumptions: Trades filled at breakout level with stop orders

Results and Metrics

The strategy generated 87 trades over 8 years and turned $100,000 into over $5.6 million.

• Total P&L: +5,592.76%

• Max Drawdown: 34.90%

• Total Trades: 87

• Win Rate: 49.43%

• Profit Factor: 2.52

• Trade Side: Long only

For context, buying and holding Bitcoin over the same period would have given you strong returns, but this strategy managed to deliver similar or better results while being actively managed with defined risk on every trade.

Why It Works

Breakouts work when they capture the start of a trend before the crowd piles in. In crypto, those moves can be violent.

The strategy filters out weak setups by requiring price to be above the 50-day moving average, which keeps you out during bear markets.

Once in a trade, the trailing stop gives the position room to breathe while protecting capital if momentum fades.

The edge comes from asymmetry: small losses, big wins. When you win less than half the time but your average winner is much larger than your average loser, you make money.

Takeaways and Next Steps

This test shows that breakout trading can work in crypto, not in spite of volatility but because of it.

• Consider scaling position sizes based on distance to stop loss.

Strategy Rules

Here’s how the strategy works, step by step:

1. Identify Pivot Points: Use a 3-bar lookback on both sides to mark swing highs and swing lows on the daily chart.

2. Define Breakout Level: The buy level is the most recent pivot high.

3. Check Trend Filter: Only take the trade if the current price is above the 50-period moving average. This keeps you out of bear markets.

4. Optional ADR Filter: If using the ADR filter, make sure the distance between the breakout level and your stop is less than 120% of the 21-day Average Daily Range. This ensures you’re buying consolidation, not extension.

5. Enter on Breakout: Place a stop buy order at the pivot high level. When price touches or breaks above it, you’re in.

6. Set Initial Stop Loss: Your stop is the most recent pivot low. If price falls below that swing low, exit the trade.

7. Trail Stops When Profitable: Once your entry price is below the current pivot low (meaning you’re in profit), start watching the 10-period moving average. If price closes below the 10 MA, move your stop to the low of that candle.

8. Exit on Stop Hit: The strategy exits when price hits either the initial stop (pivot low) or the trailing stop (MA breach level), whichever is higher.

9. Repeat: After exit, wait for the next breakout setup that meets all filters.

Download this exact strategy on TradingView:

https://www.tradingview.com/script/5bTajWQM-Qullamaggie-Breakout-V2/

The original author, millerrh, is a well-known systematic trader who has shared this publicly. Test it on your own charts and see if it fits your style.

Summary

This has a potential.

I tested whether stock breakout strategies translate to crypto and found they can work when applied with discipline. The Qullamaggie approach delivered over 5,500% returns on Bitcoin across 8 years, with a profit factor above 2.5 and manageable drawdowns. The key is combining trend filters with tight risk management. Stock strategies aren’t plug-and-play in crypto, but the principles hold. If you’re willing to cut losses fast and let winners ride, breakouts can give you an edge.

There is a pattern that dips for three straight sessions, then springs back.

I tested this version on SPY.

The count uses bearish candles, not lower closes. (it sounds weird but there is an interesting anomaly.)

The full setup/rules are included.

Problem to solve

Can a simple three down days trigger deliver a repeatable edge?

Does counting bearish candles beat naive lower-close rules?

How stable is it across three decades of SPY?

What about drawdowns and win rate?

The answers surprised me, the entry logic matters more than people expect.

Strategy overview

This approach is a mean reversion entry that waits for three consecutive bearish daily candles on SPY.

No need for lower lows or closes below prior candles.

The definition is simple: close below open for three days in a row. Exits are close>open.

• Counts three consecutive bearish candles (not three lower closes)

• Long-only on SPY daily bars.

• Cash between signals

• Position sizing uses a fixed percent of equity.

The full rules and Pine Script come later.

Backtest setup

• Symbols: SPY

• Timeframe: 1 day

• Date Range: Jan 29, 1993 to Oct 16, 2025

• Commissions: 0.05% per side

• Position Sizing: 100% of equity per entry

• Execution Assumptions: market orders on signal bar close

Results and metrics

Across more than three decades, the pattern delivered a strong return with moderate drawdowns and a solid win rate.

• Total return: +1,056.9%

• Max drawdown: 22.52%

• Total trades: 603

• Profitable trades: 66.78%

• Profit factor: 1.8

• Side: long only

Why it works

Three consecutive bearish candles often reflect short-term pressure from hedging, rebalancing, and reactive selling.

That sequence pulls price away from short-term value, when selling pressure fades, mean reversion kicks in.

Counting bearish candles captures participation intensity, not just price levels.

This avoids false negatives where closes are choppy but intraday selling is broad. The exit takes quick strength and hands the baton back to the trend.

TradingView (Pine Script) code

Summary

The three down days idea buys a three-candle downswing defined by bearish candles, not lower closes. On SPY since 1993 it produced a large absolute return, a controlled drawdown, and a strong win rate. The rules are short and the holding time is brief, which helps reduce exposure to shocks. Execution is simple and repeatable. The full rules and Pine Script are provided for paid readers.

Read more:

While I’m new to Substack, I’ve been in the trading world for a long time.

I’m excited to share the strategies I’ve found interesting to try.

I have bunch of strategies to share with you guys, but first I’ll start with the most simplest ones.

Subscribe now

But today…

Daily SPY pullbacks can be more orderly than they look.

A simple filter plus a calm entry can compound faster than many expect.

I will show the setup and the exact code.

First, let us set the stage.

Problem To Solve

Buy the dip sounds easy but is it really?

How do you buy dips without catching knives?

When do you stand aside?

The answer hinges on a small alignment you will see in the results next.

Strategy Overview

The idea is to buy brief mean reversion in the direction of an established uptrend.

We use a long Simple Moving Average as a trend filter and a linear regression line as a short-horizon reference, entering on shallow weakness and exiting on strength.

• Trade long side only on SPY daily data.

• Trend filter with a long SMA to stay aligned with higher-timeframe direction.

• Enter on modest weakness versus a short regression line while price respects the trend filter.

• Exit on strength to the upside, keeping exposure focused on the rebound.

Using linear reg sounds weird but there is actually something interesting that holds up.

Backtest Setup

• Symbols: SPY

• Timeframe: 1 day

• Date range: Jan. 29 1993 to Oct. 13 2025

• Slippage: 1 tick

• Position Sizing: Percent of equity per order at 100% (All in or cash)

• Execution Assumptions: Orders processed on close

• Starting Capital: 10,000

Results and Metrics

Across the full sample, the equity curve is smooth with contained downside and consistent rebounds.

• Total P&L: +947.49%

• Max Drawdown: 16.13%

• Total Trades: 516

• Profitable Trades: 73.45%

• Profit factor: 1.956

• Trade side: long only

Why It Works

SPY trends often advance in a stair-step pattern where brief weakness resets positioning without breaking structure.

A long SMA screens for the dominant trend so we avoid pullbacks in deteriorating regimes.

A short regression line captures local slope, buying below it inside an uptrend accesses temporary dislocations created by mean reversion and liquidity seeking flows.

Exiting into strength harvests the rebound while reducing exposure to reversal risk.

The edge comes from combining a slow filter with a fast mean-reversion trigger.

Strategy Rules

1. Instrument and timeframe: SPY, 1-day bars.

2. Compute SMA_fast = SMA(close, 2) and SMA_slow = SMA(close, 50).

3. Compute LinReg = linear regression of close over the last 14 bars.

4. Trend filter: only allow longs when close > SMA_slow.

5. Entry condition (long): open > SMA_fast, high > open, low < SMA_fast, and close < LinReg.

6. When entry condition is true, open one long position.

7. Exit condition (close long): close > LinReg and close > high[1] and close > open.

8. Order timing: process orders on bar close; slippage = 1 tick.

9. Position sizing: 100% of equity per trade.

TradingView (PineScript) Code (Copy & Paste rules)

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