Financial Trading Strategies
Quantitative models designed for volatility targeting and regime-based asset rotation.
AI Arena: The Construct
Enter the simulation. Test your own parameters against the advancing probability matrix in real-time.
Launch AI Arena →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: 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)
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.
# 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.
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