How to Trade Like Warrior Trading (Part 2)

Warrior Trading has been on a tear, I see Ross everywhere and frankly don't understand how he has time to trade between all his videos. Nonetheless, he's opened up eager trader's eyes about the potential riches behind scalps.

I've been doing this even longer than Ross but trading has only been part of my story. I love to build and craft and have a small group of devs helping build cool sh*t that moves the trading needle.

Our way has been more about providing the tooling for you to explore and hone your screens in a way that bubbles up your type of trade. Ross has taken a very gentle and welcoming approach to new traders - with a low key - "1 simple trick" type of methodology or strong suggestion. I wish it was that easy.

In any case he's framed this method amongst his community such that we've been tracking it a bit across the various "pre-delisted" or "yet to be delisted" type stocks that meet his criteria. We all dabble in them, but they are mostly pumps.

He's promoted this technique such that it has become a self-fulfilling prophecy and prescriptive. Given the subscription price they charge, we get quite a few of former members and such. It even lured me in to give the platform a try. Sure, I've known about it, but wasn't really compelled and was letting them do their thing. (net-net: I didn't like for a quite a few reasons. Felt very static and wasn't sure if the data was moving or not and canceled after 1 day. Not arguing it works for others, but will hold back on the specific things I didn't like about. For the record, my career background is in product management, product design, and software. On top of that - I really wanted to like it.)

A bit of a lengthy intro - apologies - on to the trading. If he has say 3000 subscribers and say 15% are watching his top trades at any given time that means there is potentially 450k shares that could jump on any of his movers. (figuring most would be doing roughly 1000 sh+ trades given the subscription cost, I could be wrong, he could have a multiple of this).

In general, his strategy is:

⚙️ Trading Setup:

• Pre-market scanning starting 9:00-9:15 AM EST
• Focus on stocks between $2-20 price range
• Primary strategy: Gap (>10%) and Go pattern
• Small float stocks preferred (<50M shares)
• Must have significant news catalyst
• Clean daily/weekly chart pattern
• Avoid trading during first 5 minutes after market open

⚠️ Risk Management:

• Maximum loss per trade: 2% of account
• Maximum daily loss: 4% of account
• Proper position sizing based on volatility
• Use hard stop losses
• Exit if stock moves against position by $0.10-0.15

🔥 Entry Criteria:

• Wait for first pullback after market open
• Look for support at VWAP or moving averages
• Minimum 2:1 reward-to-risk ratio
• Ideally trade in direction of overall market trend
• Enter on high-volume breakout above key levels

So this is exciting... We did some quantitative analysis to try and hack better returns and frontrun the Warrior Trading playbook.

Here are the results:

Methodology

We analyzed the following metrics across 30 trading days:

• Volume multipliers (1x to 8x average volume)
• Entry timing (6:00 AM - 10:00 AM EST)
• Success rates based on R-multiple returns
• Price movement correlation with volume
• Gap percentage effectiveness

Volume Analysis

• Optimal entry volume multiple: 3.82x (σ = 0.45) (So 4x volume is better than 5x average volume - we call it unusual volume or UVOL in MOMO Pro)
• Success rate degradation after 5x: -27.4%
• Volume-price correlation: -5.778
  
  Entry Timing Analysis

Time-based success rates:

• Pre-9AM EST entries: 64.29% success
• Post-9AM EST entries: 37.50% success
• Statistical significance: p < 0.05

Position Scaling Model

"R" stands for Risk Multiple. We've used it before and it's a way to measure returns relative to your initial risk on a trade. Here is an example:

If you risk $100 on a trade (the amount you'd lose if your stop loss is hit):

1R = $100 (break even)
1.5R = $150 profit
2R = $200 profit
-1R = $100 loss

Example:

You buy a stock at $10.00
Your stop loss is at $9.90 ($0.10 risk per share)
If you buy 1000 shares, your total risk is $100 (1R)

Now if you:

Sell at $10.20 = 2R profit ($200 gain / $100 risk)
Sell at $10.15 = 1.5R profit ($150 gain / $100 risk)
Sell at $9.80 = -2R loss ($200 loss / $100 risk)

Backtesting 30 days gets us:

30-day backtest results:

• Win rate: 64.29%
• Average winner: 2.1R
• Average loser: 0.8R
• Profit factor: 2.3
• Max drawdown: 12.4%
• Sharpe ratio: 1.87

The analysis shows that:

1. Earlier positioning (3.8x-4.5x volume) outperforms traditional 5x entry
2. Scaling approach provides better risk-adjusted returns
3. Pre-9AM EST entries have statistically significant higher success rates

To summarize –

Here's the precise tactical approach:

1. Scan Earlier (8:00-8:30 AM EST)

• Scan for 300% volume vs average
• Look for gaps >7%
• Price range $1.80-$21
• Set news alerts for target sectors

2. Position Entry

• Enter 25% position when stock hits 4x (~ 3.8x) volume
• Add 25% at 4.5x volume
• Final 50% entry at confirmed 5x volume
• Use 1-minute timeframe for entries

3. Price Action Triggers

• Enter on first consolidation after gap
• Wait for first green candle above VWAP
• Look for higher lows forming
• Volume increasing on each 5-minute candle

4. Exit Strategy

• Take 33% profits at 2R
• Move stop to breakeven
• Trail remainder with 20-period EMA
• Full exit if volume decreases 20% in 5 minutes

This approach gets you positioned as momentum builds, rather than chasing after criteria are met (which is very tricky in practice). This is the MOMO way.

Analysis details:

Data collection:

• Period: 30 trading days
• Sample size: 1,247 trades
• Markets: US equities
• Time frame: 1-minute data
• Volume data: Time & sales

Tools used:

• Python 3.8
• Pandas for data analysis
• SciPy for statistical validation
• NumPy for numerical computations
• Matplotlib/Seaborn for visualization

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