66-Day Quant Training Journal: From RSI Divergence to VWAP — Seven Days Crossing the Volume-Price Threshold

1. The Seven-Day Learning Map

2. Deep Dive into Each Indicator

Day 11–12: RSI Divergence — When Price and Momentum Disagree

The RSI (Relative Strength Index), introduced by J. Welles Wilder in 1978, is a momentum oscillator that measures the relative strength of recent gains versus losses, not the direction of price itself.

Where $RS = \frac{\text{Average Gain}}{\text{Average Loss}}$ (typically calculated over 14 periods).

The classic RSI application is overbought (>70) and oversold (<30) zone identification. But where RSI truly shines is in divergence — when price action and the RSI reading tell conflicting stories.

The Four Types of Divergence

Day 11: Bullish Divergence in Practice

The strategy logic: use scipy.signal.argrelextrema to identify swing lows. When price prints a Lower Low but RSI prints a Higher Low, a bullish divergence signal fires, and we enter at the next bar's open.

The core experiment was comparing three exit rules:

1. RSI Overbought Take-Profit: Exit when RSI ≥ 70
2. Fixed Percentage Stop-Loss: Exit when close drops below entry × (1 − 5%)
3. Time Stop: Forced exit after 20 trading days

Different exit mechanisms produced dramatically different risk-return profiles from the same entry signal — a lesson often overlooked in quantitative backtesting.

Day 12: Bearish Divergence — Catching Bull Exhaustion

Day 12 flipped the perspective: detecting bearish divergence (price making higher highs while RSI prints lower highs) and entering with short-side logic.

The key technical difference was swing high detection using argrelextrema(order=5), with constraints on the distance between the two highs to filter out too-close false signals and too-distant stale signals.

Day 13: The Hammer — Defining a "Reversal Candle" with Math

Candlestick pattern analysis dates back centuries to 18th-century Japanese rice markets. But most textbooks only teach you to "eyeball" a hammer shape — that doesn't work in programmatic backtesting. We need precise mathematical definitions.

Quantitative Hammer Conditions

For a bar to qualify as a Hammer, it must satisfy all of the following:

body = abs(close - open)
lower_shadow = min(open, close) - low
upper_shadow = high - max(open, close)

Condition 1: lower_shadow >= body × 2    (lower shadow at least 2× the body)
Condition 2: upper_shadow <= body × 0.3  (minimal upper shadow)
Condition 3: body > 0                     (exclude doji candles)

But entering on a single candle pattern alone is too risky. Day 13's strategy added two layers of confirmation:

1. RSI Oversold Confirmation: RSI ≤ 40 (a relaxed threshold balancing signal frequency with quality)
2. Downtrend Confirmation: Close < SMA(20), ensuring the reversal signal appears within an actual downtrend

All three conditions must fire simultaneously — better to miss a trade than to take a bad one.

Day 14: Bullish Engulfing — The Buyers' Complete Takeover

The Bullish Engulfing is considered one of the most powerful bullish reversal patterns in candlestick analysis. It consists of two bars where the second bullish candle's body completely "engulfs" the first bearish candle's body.

Quantitative Detection Conditions

Condition 1: prev_close < prev_open         (prior bar is bearish)
Condition 2: curr_close > curr_open          (current bar is bullish)
Condition 3: curr_open <= prev_close         (current open ≤ prior close)
Condition 4: curr_close >= prev_open         (current close ≥ prior open)
Condition 5: body > 0                        (exclude doji candles)

Visually, the second candle "swallows" the first entirely, representing buyers overpowering sellers within a single session. In backtest research, the Bullish Engulfing near support levels with high volume confirmation ranks among the most reliable candlestick patterns.

The SMA(50) Trend Filter

Day 14 introduced SMA(50) as a trend filter: long signals are only allowed when close > SMA(50). The underlying logic:

We only want to catch short-term pullback rebounds within a medium-term uptrend, not "catch falling knives" in a downtrend.

Parameter optimization tested 36 combinations (SMA period × stop-loss percentage × holding days), which led us to a crucial lesson:

Day 15: Volume Spike Reversal — Volume Is the Real Star

From Day 4 through Day 14, we relied exclusively on price-derived indicators (SMA, RSI, candlestick patterns) for entry signals. Day 15 marked a turning point — we formally brought volume into the strategy framework.

Defining a Volume Spike

The quantitative definition is straightforward:

Where $k$ is the spike multiplier (default $k = 2$, meaning today's volume exceeds the 20-day average by at least 2×).

This signals an abnormally high level of trading activity — potentially institutional accumulation, panic selling, or a collective reaction to major news.

Triple Entry Conditions

Day 15's strategy required all three conditions simultaneously:

1. Volume Spike: Volume > 20-day average volume × 2
2. Reversal Candlestick: Hammer or Bullish Engulfing (integrating the detection logic from Days 13 and 14)
3. Trend Filter: Close > SMA(50)

This was the first time we assembled knowledge modules from previous days into a complete strategy. Progress in quantitative trading often works this way — it's not about learning new indicators, but learning to combine existing tools.

Parameter Optimization Observations

Testing 48 parameter combinations (spike multiplier × SMA period × holding days):

• Spike multiplier had the largest impact on strategy quality. Too low (1.5×) included too many "non-event" days; too high (3.0×) generated almost no signals
• The most robust zone was 2.0–2.5×, which confirms an intuition: meaningful volume changes typically need to exceed at least twice the normal level

Day 16: VWAP — An Institutional-Grade Intraday Benchmark

VWAP (Volume Weighted Average Price) is the gateway from retail-level analysis to institutional-grade intraday trading. It isn't just another moving average — it is the volume-weighted consensus price that market participants have agreed upon.

The Formula

A critical property: VWAP resets automatically at the start of each trading day, accumulating from zero. Early in the session the denominator is small, making VWAP highly sensitive to price changes; as the day progresses, the growing cumulative volume makes it increasingly stable.

The VWAP Pullback Strategy

The idea: in an uptrend (close > SMA(20)), when price pulls back from above VWAP to VWAP, treat it as a buying opportunity.

Entry Conditions:

• Previous bar close > VWAP, current bar close ≤ VWAP (pullback detected)
• Close > SMA(20) (trend filter)
• Exclude the first 30 minutes after open (09:30–10:00, excessive volatility)

Exit Rule Comparison (three approaches):

• Exit A: Hold for a fixed number of bars (e.g., 12 bars × 5 min = 1 hour)
• Exit B: Fixed percentage take-profit (+2%)
• Exit C: Exit when SMA(20) is breached

Backtest Performance and Key Findings

Day 17: Pausing the Code, Listening to a Year of Lessons

Day 17 was a "no code" day. We read QFX Research's article "1 Year in Quant Trading — 24 Lessons I've Learned" and reflected deeply on three lessons that resonated most.

Lesson #9: Make Risk Management Your Top Priority

Your strategy should be able to handle waking up one morning to find the market has crashed 50%.

Losing 40% of your capital requires a 66.7% gain just to break even — the math is fundamentally asymmetric. Over the first 16 days of training, we'd generated impressive backtest numbers but never seriously asked: if the worst-case scenario hit, would the account survive?

A Risk Management Framework:

The primary goal isn't "to make money" — it's "to not go bankrupt." Survival comes first; compounding follows.

Lesson #10: Use Fewer Parameters, but Understand Them Deeply

My best-performing strategy had only 3 parameters. The worst mistake was probably letting an optimization script auto-generate parameter combinations.

During training, we often ran grid searches across SMA periods, RSI thresholds, and holding durations, finding the "best-looking" combination — without knowing why those parameters worked. This is essentially overfitting to historical data, not discovering genuine market structure.

Before adding any parameter, answer three questions:

1. What market behavior does this parameter represent? (e.g., SMA 200 = long-term trend direction)
2. Why should it affect strategy performance? (logic-driven, not data-driven)
3. Does the strategy collapse if you change this value slightly? (robustness test)

Only when all three answers are clear does a parameter deserve a place in your strategy.

Lesson #20: The Derivative of the Derivative Is Where the Value Lies

If you strategically transform an indicator into a second-order derivative, strategy performance often improves significantly.

Don't rush to learn a new indicator. First ask: what is the second-order behavior of the indicator I already have?

• RSI → Rate of change of RSI (momentum acceleration) → Consecutive positive slopes (trend confirmation)
• MACD → Cumulative histogram area → Rate of area sign change
• VWAP deviation → Standard deviation of deviation (intraday volatility rhythm) → Deviation mean-reversion speed

3. Common Findings Across Seven Days of Backtesting

Exits Matter More Than Entries

If there's only one takeaway from this week, it's this: exit rules have a greater impact on strategy performance than entry signals.

Day 11's three exit rule comparisons, Day 14's holding period sensitivity analysis, Day 16's three exit logic comparisons — each validated the same truth: the same entry signal paired with different exit mechanisms can produce Sharpe Ratio differences of 2× or more.

Ninety percent of strategy tutorials focus on "when to buy," but what truly determines your survival is "when to sell."

Multiple Conditions > Single Indicators

From Day 11's pure RSI divergence, to Day 13's triple conditions (pattern + RSI + trend), to Day 15's triple conditions (volume + pattern + trend), the design trajectory is clear:

Each meaningful filter layer reduces false signals, but also reduces signal frequency.

This is an eternal trade-off: confirmation vs. trading frequency. There's no right answer — only the answer that fits your capital size and risk tolerance.

From Daily Bars to Minute Bars

Day 16's VWAP strategy took us from daily charts into the 5-minute bar world for the first time. Intraday trading presents entirely different challenges:

• Data volume explodes (78 five-minute bars per day)
• Must account for "opening volatility periods" and "end-of-day forced liquidation"
• VWAP resets daily — indicator logic is fundamentally different from daily indicators
• Transaction costs consume a larger share (because expected profit per trade is smaller)

4. Strategy Toolbox Quick Reference

5. Mindset Takeaways

1. Exits determine survival. Entry is just the beginning of the story; exit is the ending. Time spent designing exit rules should at least match time spent on entry conditions.
2. Multiple confirmations are your seatbelt. Single indicators are rarely reliable, but meaningful multi-condition combinations significantly reduce false signals.
3. Risk management isn't optional — it's foundational. Losing 40% requires gaining 66.7% to recover. That math never changes.
4. Less is more. Three parameters you understand beat ten parameters you've merely optimized.
5. Go deep, not wide. Understanding one indicator's second-order behavior is worth more than learning ten new ones.
6. Volume is the soul of price. A breakout without volume is a fake breakout; a reversal with volume deserves trust.