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。

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

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.

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▲ 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.

Core algorithm logic is proprietary.

Contact to Unlock Full Strategy