[Ultimate] The USIC Era: Modern EP & VCP Masterclass with Oliver Asmus & J Law

[INTRO: THE NEW AGE SWING DOMINATORS]
Does the classical CAN SLIM momentum engine still function in the high-frequency regime of the modern market? The answer is a resounding yes, provided it undergoes rigorous, data-driven optimization. Enter the modern dominators of the U.S. Investing Championship (USIC): Oliver Asmus and J Law (Law Wai-Sum). By blending the foundational geometry of Mark Minervini’s Volatility Contraction Pattern (VCP) with the systematic, catalyst-driven triggers of Kristjan Qullamäe’s Episodic Pivot (EP), these traders have redefined superperformance. This masterclass deconstructs their execution methodologies and translates their strategies into a quantitative, rule-based algorithm for the Sovereign Trader.

1. EXECUTIVE SUMMARY (TL;DR)

The strategy of Oliver Asmus and J Law focuses on capturing high-energy breakout candles followed by immediate, tight volatility contraction. They target stocks experiencing massive gaps up (5%+) on at least 500% of their average volume, backed by structural fundamental catalysts. Instead of allowing the price to revert and fill the gap, these leaders hold the upper range of the breakout bar. Over 3 to 5 trading days, the price geometry forms a tight flag or a narrow pennant, drying up volume and establishing a low-risk pivot point. Entry is executed on intraday breakouts or support at the opening print.

To bypass manual scanning bottlenecks, we formulate an early morning Relative Volume (\(RVOL_{30min}\)) projection algorithm. This mathematical filter identifies institutional order flows within the first 30 minutes of trading (9:30 AM – 10:00 AM EST), ensuring we capture the true market leaders in real-time.

2. THE NEW LEGENDS: OFFICIAL RECORDS

The credibility of this strategy lies in its audited, real-money track record in the U.S. Investing Championship.

• J Law (Law Wai-Sum, @JLawStock): A former secondary school teacher from Hong Kong who transitioned into professional trading. Competing in the $1 Million+ Money Manager Stock Division, Law won the 2024 championship with a +353.9% return and defended his title in 2025 with a +252.3% return. This back-to-back performance generated a record-breaking two-year cumulative return of 1,499%.
• Oliver Asmus (@Oliver_Asmus): A premier momentum swing trader actively standing at the top tier of the Money Manager Stock Division. Asmus is renowned for sharing his daily verified trade logs, annotated chart entries, and execution points on X (formerly Twitter), offering a transparent view of modern swing trading logic.

3. TECHNICAL CORE: MODERN EP & VCP CONVERGENCE

The technical edge combines the macro impulse of an Episodic Pivot with the micro consolidation of a VCP.

3.1. The Modern Episodic Pivot (EP)

An Episodic Pivot is the price manifestation of a major shift in a company’s structural earnings power.

1. The Catalyst: Massive earnings surprises, upgraded multi-year guidance, major product innovations, or paradigm-shifting regulatory approvals.
2. The Price Action: A gap up of at least 5% that clears multi-month resistance levels.
3. The Volume Signature: Daily volume exploding to 500%+ of the 50-day moving average.
4. The Close: Price must close near the absolute daily high (within the top 25% of the day’s trading range).

3.2. The 3-to-5 Day Volatility Contraction Pattern (VCP) Flag

Traditional momentum swing models look for multi-week consolidations. In the modern US equity regime, the most explosive moves undergo micro-consolidations at the gap high.

• Gap Holding: The stock holds its breakout range, refusing to drift down and fill the gap. This indicates that institutional sellers are absent, and buyers are absorbing supply at premium prices.
• 3-5 Day Coil: The price action forms a narrow flag or a “chicken comb” shape. Daily trading range and daily volume dry up to near-zero levels. This represents the absolute exhaustion of supply—the line of least resistance.

graph TD
    Catalyst["Structural News or Earnings Catalyst"] --> GapUp["Phase 1: Gap Up >= 5% & RVOL >= 500%"]
    GapUp --> GapHold{"Phase 2: Does it hold the Gap?
(Closes in top 25% of daily range)"}
    GapHold -- "No (Gap Fill)" --> Reject["Discard Candidate"]
    GapHold -- "Yes" --> VCP["Phase 3: 3-5 Day Volatility Contraction
(Volume Dry-up & Tight Flag)"]
    VCP --> Entry{"Phase 4: Intraday Breakout or
Open Line Support"}
    Entry -- "Triggered" --> Position["Enter Position with tight Stop Loss"]
    
    style GapUp fill:#1a1b26,stroke:#7aa2f7,color:#fff
    style VCP fill:#1a1b26,stroke:#a8e6cf,color:#fff
    style Position fill:#a8e6cf,stroke:#000,color:#000

3.3. Entry Tactics: Intraday Breakout vs. Open Line Hold

• Intraday Breakout: Once the daily VCP is set, the trader monitors the 1-minute, 5-minute, or 15-minute chart. The moment the price clears the high of the flag on rising intraday volume, an entry is executed.
• Open Line Hold (OLH): If the stock gaps up slightly on day 4 or 5, pulls back to test the daily open price or the previous day’s close, and immediately bounces to print a lower-wick rejection, an entry is taken near the open line. This allows for an exceptionally tight stop loss.

4. QUANT ARCHITECTURE: 9:30 – 10:00 AM VOLUME PROJECTION

From an algorithmic perspective, waiting for the market close to confirm a 500% volume breakout introduces execution lag. We must project the daily volume during the high-volatility opening window.

4.1. Formalizing Early Relative Volume (\(RVOL_{30min}\))

Typically, the first 30 minutes of the US market session (9:30 AM – 10:00 AM EST) represent approximately 20% to 25% of the total daily volume for high-velocity momentum stocks. We calculate the Projected Daily Volume at 10:00 AM:

$$ProjectedVolume_{Day} = Volume_{30min} \times \frac{1}{\beta_{30min}}$$

Where \(\beta_{30min}\) is the historical average ratio of [first 30 minutes volume / total daily volume] calculated over the last 50 trading days for that specific asset.

Alternatively, we calculate the Relative Volume Intensity (\(RVOL_t\)) at interval \(t = 30min\):

$$RVOL_{30min} = \frac{Volume_{Today, 30min}}{SMA(Volume_{30min}, 50)}$$

If \(RVOL_{30min} \ge 5.0\), the algorithm registers an institutional volume spike, flags the ticker, and prepares the execution modules.

5. SYSTEM IMPLEMENTATION: PYTHON SCANNER

The following Python script scans real-time intraday data (at 10:00 AM EST) to identify potential EP setups showing tight VCP characteristics.

import pandas as pd
import numpy as np

def detect_early_episodic_pivot(intraday_df, daily_df, min_gap_pct=5.0, min_rvol_ratio=5.0):
    """
    Scans intraday data at 10:00 AM EST and daily candles to find Modern EP & VCP setups.
    """
    candidates = []
    
    # Calculate daily indicators
    daily_df['volume_sma50'] = daily_df['Volume'].rolling(window=50).mean()
    daily_df['close_sma200'] = daily_df['Close'].rolling(window=200).mean()
    
    for symbol in intraday_df['Symbol'].unique():
        ticker_intra = intraday_df[intraday_df['Symbol'] == symbol].sort_values('Datetime')
        ticker_daily = daily_df[daily_df['Symbol'] == symbol].sort_values('Date')
        
        if len(ticker_intra) < 30 or len(ticker_daily) < 50:
            continue
            
        prev_day = ticker_daily.iloc[-1]
        avg_vol_50d = prev_day['volume_sma50']
        sma_200 = prev_day['close_sma200']
        
        # Isolate 9:30 AM to 10:00 AM bracket
        intra_30m = ticker_intra.between_time('09:30', '10:00')
        if intra_30m.empty:
            continue
            
        current_price = intra_30m.iloc[-1]['Close']
        open_price = intra_30m.iloc[0]['Open']
        high_30m = intra_30m['High'].max()
        low_30m = intra_30m['Low'].min()
        accumulated_vol = intra_30m['Volume'].sum()
        
        # Calculate gap percentage
        gap_pct = ((open_price - prev_day['Close']) / prev_day['Close']) * 100.0
        
        # Project full day volume (assuming historical 20% average ratio)
        projected_daily_vol = accumulated_vol / 0.20
        rvol_ratio = projected_daily_vol / avg_vol_50d
        
        # Verify initial EP criteria
        if gap_pct >= min_gap_pct and rvol_ratio >= min_rvol_ratio and current_price > sma_200:
            
            # Check 4-day daily price consolidation (VCP tightness check)
            recent_4d = ticker_daily.tail(4)
            recent_amplitudes = ((recent_4d['High'] - recent_4d['Low']) / recent_4d['Low']) * 100.0
            is_coiled_vcp = recent_amplitudes.mean() < 3.5  # tight range constraint
            
            candidates.append({
                'Symbol': symbol,
                'Gap_Pct': round(gap_pct, 2),
                'Projected_RVOL': round(rvol_ratio, 2),
                'Current_Price': current_price,
                'Is_Coiled_VCP': is_coiled_vcp,
                'Intraday_Range_Pct': round(((high_30m - low_30m) / low_30m) * 100.0, 2)
            })
            
    return pd.DataFrame(candidates)

6. PRACTICE: VIBE CODING PROMPT CHAIN

Use these prompt sequences to instruct your AI coding agent (e.g., Antigravity) to construct your own automated momentum terminal.

STEP 1: Real-time Data Ingestion & Volume Projection Engine

"Write a Python FastAPI service that connects to the Alpaca WebSocket market data stream. 
Calculate the cumulative volume for SPY, QQQ, and top momentum tickers during the first 30 minutes of the session (9:30 - 10:00 AM EST). 
Implement a dynamic beta logic that estimates the daily volume by taking the ticker's average intraday 30-minute volume ratio from the past 10 sessions."

STEP 2: Intraday Invalidation & Open Line Support Triggers

"Build an execution trigger script in Python. 
If a ticker identified in Step 1 pulls back to test its daily opening price or yesterday's close, prints a hammer candlestick (lower wick >= 2.0x body) on the 1-minute chart, and breaks the high of that hammer within 5 minutes, generate a BUY order. 
Set a hard stop loss exactly 0.5% below the hammer's low."

STEP 3: Risk Sizing & Portfolio Guardrails

"Integrate a risk-budgeting module inspired by J Law's USIC portfolio sizing. 
Limit the risk per trade to exactly 0.5% of total account equity. 
Calculate the position size dynamically using: Position Size = (Account Equity * 0.005) / (Entry Price - Stop Loss Price). 
Add a global circuit breaker that blocks new entries if QQQ drops more than 1% from its daily open."

7. RISK ARMOR & PORTFOLIO MATRICES

Superperformance does not require a high win rate; it is a mathematical consequence of asymmetric payoff ratios.

7.1. Expected Value (EV) Formulation

High-energy breakout setups typically achieve a 35% to 45% win rate. By keeping losses extremely tight and letting winners run, the expected value remains highly positive.

$$EV = (P_{win} \times W_{avg}) - (P_{loss} \times L_{avg})$$

Championship Payoff Profile (J Law & Oliver Asmus):

• Win Rate: 40%
• Loss Rate: 60%
• Average Win: +15% to +25%
• Average Loss: -2% to -3.5%
• Expected Value (EV): +6.2% expected value per trade

$$EV = (0.40 \times 0.20) - (0.60 \times 0.03) = 0.08 - 0.018 = 0.062 \quad \text{(+6.2\% Expected Value per trade)}$$

7.2. Modern Momentum Setup Audit Table

8. CONCLUSION: RIDING THE INSTITUTIONAL WAVES

Oliver Asmus and J Law have proven that the stock market is not a predictive puzzle, but a responsive execution loop. By tracking institutional footprint signatures (EP) and waiting for supply exhaustion (VCP), they eliminate cognitive speculation and execute solely on mathematical edge.

By implementing the 9:30 - 10:00 AM volume projection models, you build a digital harness that scans, sizes, and manages momentum with structural precision.

Respect the volume. Hold the open. Keep the stops tight.