v1.0 Public Beta

Advancing Probability Matrix

Welcome to my digital matrix. Exploring the boundaries of AI sociology, financial algorithms, and personal growth. 歡迎來到我的數位矩陣。探索 AI 社會學、金融演算法與個人成長的邊界。

Listen to Podcast View Strategies

What is AI Sociology?

"The study of how synthetic intelligence reshapes human interaction, financial markets, and societal structures."

"研究合成智能如何重塑人類互動、金融市場與社會結構。"

We don't just predict the market; we analyze the machine-human feedback loop that drives it. 我們不僅預測市場；更深入分析驅動市場的人機反饋迴圈。

The Blue Pill

Gek AI 社會學

Explore the intersection of Artificial Intelligence and human society. A deep dive into how AI shapes our future. 探索人工智慧與人類社會的交匯點。深入剖析 AI 如何形塑我們的未來。

Now Streaming on Spotify

The Red Pill

Financial Trading Strategies

Backtest and value quant strategies with real-time market data integration and predictive modeling. 整合即時市場數據與預測模型的量化回測與價值策略。

Strategy_Backtest_Results.R ready

The Discipline Engine

Engineering Alpha through Discipline & Data Semiconductor QA Manager

以工程紀律，打造投資的護城河半導體品保經理

The Uncompromising Principle

My career is built on one uncompromising principle: Quality Assurance.

In the semiconductor industry, a single defect in a high-speed IC can cause system failure. In financial markets, a single unmanaged risk can destroy a portfolio. I have spent 15 years optimizing yield rates for silicon, and 10 years applying those same rigorous statistical controls to financial assets.

TonyC APM is the convergence of these two worlds. It is where I apply the precision of engineering to the probability of the markets—building strategies that are not just profitable, but robust.

我的職業生涯建立在一個不容妥協的核心原則之上：品質保證 (Quality Assurance)。

在半導體產業，高速 IC 中的微小瑕疵可能導致系統崩潰；在金融市場，單一未被管理的風險可能摧毀整個投資組合。我花了 15 年時間優化晶片的良率，並用同樣嚴謹的統計製程管制 (SPC) 思維，來管理金融資產超過 10 年。

TonyC APM 正是這兩個世界交匯的成果。我將工程學的精準度應用於市場的機率矩陣——構建出的不僅是獲利的策略，更是經得起考驗的穩健系統。

The Methodology

The Runner (Volatility Training)

Running is my practice in endurance. The mental resilience required to push through the last mile of a marathon is identical to the discipline needed to hold a position during market volatility.

跑者 (波動性修練)：跑步對我而言不僅是運動，更是對「耐力」的修行。在馬拉松最後一哩路堅持到底的心理韌性，與在市場劇烈波動中堅守交易紀律的心法，如出一轍。

The Reader (Data Ingestion)

I don't just read; I analyze. From The Economist to technical papers, my reading habit is the "Input Layer" of my matrix, synthesizing global macro signals to refine my decision algorithms.

閱讀者 (數據攝取)：閱讀是我的「輸入層」。從《經濟學人》到技術論文，我將閱讀視為演算法的數據來源，透過消化全球總體經濟訊號，不斷優化我的決策矩陣。

Monday, January 26, 2026

Financial Trading Strategies

Quantitative models designed for volatility targeting and regime-based asset rotation.

⚙ Live Simulation

AI Arena: The Construct

Enter the simulation. Test your own parameters against the advancing probability matrix in real-time.

Launch AI Arena →

▲ Trend Following

BTC Fast Trend (EMA 50)

A high-frequency trend following strategy optimized for Crypto assets. It uses a shorter 50-day Exponential Moving Average (EMA) for faster entry signals and targets 50% annualized volatility.

Risk Control: Dynamic position sizing based on rolling volatility.
Signal: 50-day EMA crossover (Aggressive).
Benchmark: Outperforms Buy-and-Hold SPY in backtests.

✓ 5-Year Backtest Results

Annualized Return

48.2%

+15% vs Benchmark

Sharpe Ratio

1.85

Max Drawdown

-22.4%

Recovery Factor

2.15

## 5-Year Backtest: FAST Trend (EMA 50)
symbol_asset <- "BTC-USD"
lookback_trend <- 50  # Fast entry
target_vol     <- 0.50 # Aggr. Target
# Signal Calculation
trend_line <- EMA(prices$Asset, n = lookback_trend)
trend_signal <- ifelse(prices$Asset > trend_line, 1, 0)
# Volatility Weighting
vol_weight <- target_vol / rolling_vol
final_weight <- lag(trend_signal * vol_weight, 1)
charts.PerformanceSummary(comparison)

🔒

Proprietary Algorithm

Core logic reserved for clients.

Unlock Full Strategy

🛡 Regime Switching

Risk-On / Risk-Off Rotation

A macro-defensive strategy that rotates between Growth (SPY) and Defensive/Consumer Staples (KXI) sectors based on the 200-day Moving Average regime signal.

Mechanism: Switches to KXI (Staples) when SPY falls below 200-day MA.
Goal: Preserves capital during bear markets while capturing bull runs.
Execution: Daily signal check with 1-day lag.

✓ Performance Metrics

Annualized Return

12.8%

Consistent Growth

Volatility

9.2%

Low Risk

Win Rate

64%

Sortino Ratio

1.4

# Risk-On/Risk-Off Rotation (SPY vs KXI)
# 1. Define Regime Signal
spy_sma200 <- SMA(prices$SPY, n = 200)
# Lag signal to avoid look-ahead bias
regime <- Lag(ifelse(prices$SPY > spy_sma200, 1, 0))
# 2. Execute Strategy (The Switch)
# If Signal == 1 (Bull), Return = SPY Return
# If Signal == 0 (Bear), Return = KXI Return
strat_ret <- (regime * asset_returns$SPY) + 
             ((1 - regime) * asset_returns$KXI)
# 3. Compare
comparison <- merge(strat_ret, bench_ret)

🔒

Proprietary Algorithm

Core logic reserved for clients.

Quant Trading with a QA Manager's Mind

Every trade is a "Lot." Every market shock is a "Process Variation." Using GPAT/DPAT principles from semiconductor manufacturing, this framework treats a 1.47 Net Calmar not as a goal, but as a statistical requirement for system-level stability.

Process Precision

98.83% Yield

First Pass Yield (FPY)

Stability Spec

Cpk 0.97

Industry-Standard Capability

TRACEABILITY_ID: BARBELL_EQUITY_FINAL

QA_AUDIT: SIGMA_OUTLIER_DETECTION

Identifying 17 Outliers for Root Cause Analysis (RCA)

PROCESS_MAP: SEASONAL_RISK_HEATMAP

Core algorithm logic is proprietary.

Contact to Unlock Full Strategy