66-Day Quant Training Journal: From SMA to Ichimoku — Building a Solid Technical Indicator Foundation in Seven Days

1. The Seven-Day Learning Map

2. Deep Dive into Each Indicator

Day 4–5: SMA — Where Every Trend Strategy Begins

The Simple Moving Average (SMA) is the most classic tool in technical analysis. Its logic is beautifully straightforward: compute the arithmetic mean of the past $N$ closing prices.

What We Learned in Practice

• Day 4: We used the IBKR API to extract historical data for the MAG7 stocks (MSFT, AAPL, TSLA, NVDA, META, GOOGL, AMZN) and found the optimal SMA window for each stock individually. This taught us that different assets have different optimal rhythms — one-size-fits-all rarely works.
• Day 5: We converted a daily SMA 200 strategy into a 1-minute SMA 5 intraday script. Shortening the timeframe made signals more responsive but dramatically increased false signals — the double-edged sword of short-term trading.

Day 6: Bollinger Bands — Measuring Volatility with Statistics

Bollinger Bands consist of three lines:

• Middle Band: SMA (typically 20-day)
• Upper Band: SMA + $k \times \sigma$ (typically $k = 2$)
• Lower Band: SMA - $k \times \sigma$

Where $\sigma$ is the standard deviation of closing prices.

Backtest Results and Reflection

Using AAPL with a "buy at lower band, sell at upper band" mean-reversion logic over ten years:

• The grid search's best parameters (SMA=20, σ=3.0) produced a Sharpe 2.05× above the mean — a classic overfitting red flag.
• The 3σ bandwidth was rarely touched (only 1 trade out-of-sample), meaning the strategy sat in cash most of the time — low drawdown but also low returns.
• AAPL exhibited a strong uptrend over 2016–2026; a pure mean-reversion strategy sold too early at the upper band, missing extended trend gains.

Day 7: MACD — Your Ticket into the World of Momentum

MACD stands for Moving Average Convergence Divergence. It measures momentum through the relationship between two exponential moving averages.

Advanced Technique: Histogram Slope Filter

Pure crossover signals are prone to whipsaws in ranging markets. Adding a histogram slope filter:

• Only allow buy entries when the histogram is rising
• Only allow sell entries when the histogram is falling

This extra confirmation layer effectively reduces false signals, but it also delays your entry — an inherent characteristic of lagging indicators. Trading is always a tradeoff between confirmation and timeliness.

Day 8: ADX — It Doesn't Tell You Where, It Tells You Whether It's Worth Going

The ADX (Average Directional Index) is a uniquely specialized indicator. Unlike SMA or MACD, which generate buy/sell signals, ADX answers a more fundamental question: "Is there a trend right now? Is it strong enough to trade?"

How ADX Is Calculated

ADX isn't derived directly from price — it goes through multiple layers of processing. The full calculation chain breaks down into five steps:

Step 1: Directional Movement (+DM / -DM)

For each bar, compare the current high/low against the previous bar:

In plain terms: +DM captures the magnitude of "upward breakouts," while -DM captures "downward breakouts."

Step 2: True Range (TR)

TR measures each bar's actual volatility range, accounting for gap openings.

Step 3: Wilder's Smoothing

Apply Wilder's smoothed moving average (typically $N = 14$) to +DM, -DM, and TR separately:

Step 4: Directional Indicators (+DI / -DI) and DX

+DI represents the proportion of upward momentum relative to total volatility; -DI represents the downward proportion. DX measures the "divergence" between the two.

Step 5: ADX = Smoothed DX

The final ADX applies one more round of smoothing to DX, making the trend-strength reading more stable and less reactive to single-day spikes.

ADX Value Interpretation

ADX works alongside +DI (Positive Directional Indicator) and -DI (Negative Directional Indicator):

• Buy: ADX > 25 and +DI crosses above -DI
• Sell: ADX > 25 and -DI crosses above +DI

Day 9: Ichimoku Cloud — An Entire Trading System on One Chart

The Ichimoku Kinko Hyo is one of the few indicators that simultaneously displays trend direction, momentum strength, and support/resistance levels on a single chart. It consists of five lines:

Compared to a Single MA Strategy

While SMA only tells you "where price is relative to the average," Ichimoku simultaneously provides:

1. Forward-looking perspective: The cloud extends into the future, letting you anticipate support and resistance zones
2. Multi-dimensional confirmation: Tenkan/Kijun crossover + cloud position + Chikou Span — triple confirmation reduces false signals
3. Built-in momentum reading: The distance between price and cloud indicates momentum strength; convergence signals weakening momentum

The tradeoff: signals are more conservative, trading frequency is lower, and opportunities in choppy markets are often missed.

Day 10: A Million-Dollar Lesson — The Risk Management Mindset

No code on this day. Just a real story: a trader built a quantitative strategy called Imhotep. His account ballooned to $1.88 million. Then, because his stop-loss code was untested, monitoring was absent, he was trading 100% of his capital with leverage, it all vanished overnight in a single liquidation event.

My Safety Net Framework

Drawing from The Trading Bible's core philosophy, I distilled three survival principles:

Principle 1: Drive Single-Trade Risk Toward Zero

• Maximum risk per trade ≤ 1% of total capital
• Calculate leverage based on actual exposure, not nominal position size

Principle 2: Maximize Your Survival in the Market

• Even after 20 consecutive stop-losses, the account retains 80%+ of its capital
• Separate "trading capital" from "reserve capital" — the reserve is never touched

Principle 3: Ensure Positive Expected Value

Where $W$ = win rate, $\bar{G}$ = average gain, $L$ = loss rate, $\bar{L}$ = average loss. Only strategies with positive expected value are worth executing.

3. Common Findings Across Seven Days of Backtesting

Overfitting Is Your Greatest Enemy

Every parameter optimization is a battle against overfitting. Across Day 6 (Bollinger Bands), Day 7 (MACD), Day 8 (ADX), and Day 9 (Ichimoku), we repeatedly confirmed the same truth:

The best parameters in-sample are almost never the best parameters out-of-sample.

How to cope:

1. Always split data into in-sample (training) and out-of-sample (validation) periods
2. When reading Sharpe heatmaps, choose the "plateau" over the "peak" — a stable parameter region is more reliable than a single optimal point
3. When in-sample Sharpe dramatically exceeds out-of-sample, that's the overfitting red flag

The R.E.A.L. Backtesting Framework

All backtests followed the R.E.A.L. principles:

• Realistic: Include commissions ($0.005/share) and slippage (0.05%)
• Evidence-based: Validate phase by phase, never skip steps
• Avoiding Bias: Apply signal.shift(1) — execute on the next bar's open to prevent look-ahead bias
• Logged: Document observations in Markdown at every phase for reproducibility

4. Indicator Selection Quick Reference

5. Mindset Takeaways to Remember

1. Simple strategies tend to be more robust. The more complex the system, the more likely it breaks in live trading.
2. Backtests are maps, not territories. Historical data is perfect; markets are not.
3. Risk management comes before everything. Survive first, profit second.
4. Lagging isn't a flaw — it's a feature. An extra layer of confirmation means fewer false signals.
5. Don't chase "optimal" — chase "robust." Peaks on a Sharpe heatmap are traps; plateaus are where you want to be.