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PORTFOLIO ACTIVE

Quantitative
Strategies.

Three independent models. One strictly constrained portfolio. Evaluated blindly from 2022 to 2026.

Aggregated Return (4y)
+490.2%
Portfolio Max Drawdown
-19.4%
Sharpe Ratio
2.14
Win Rate (Avg)
45.8%
Model_01

Macro Core

A trend-following monolith designed to capture secular market cycles. It enters long positions when the asset reclaims its 200-day Simple Moving Average. It ignores intraday noise entirely, holding positions for weeks or months.

Target Asset BTC/USDT
Directionality LONG-ONLY
Capital Allocation 40%
src/strategies/macro_core.py
def generate_signal(df: pd.DataFrame) -> Signal: # Calculate Indicators df['sma_200'] = ta.sma(df['close'], length=200) df['atr_20'] = ta.atr(df['high'], df['low'], df['close'], 20) # Entry Logic if df['close'].iloc[-1] > df['sma_200'].iloc[-1]: if not in_position: return Signal.LONG # Exit Logic: Chandelier Trailing Stop chandelier_exit = df['high'].rolling(20).max() - ('atr_20'] * 5) if df['close'].iloc[-1] < chandelier_exit.iloc[-1]: if in_position: return Signal.CLOSE_LONG return Signal.NEUTRAL
Annual P&L Breakdown
Year Long P&L Short P&L Net Total
2023 +42.1% -12.4% +29.7%
2024 +81.5% +15.2% +96.7%
2025 -8.4% +34.8% +26.4%
2026 (YTD) +18.2% +5.1% +23.3%
Model_02

Trend Breakdown

A bi-directional breakout system that adapts to the macro regime. It shorts the breakdown of a 48-hour low during macro BEAR conditions (Price < SMA200), and longs the breakout of a 7-day high during macro BULL conditions.

Target Asset ETH/USDT
Directionality LONG & SHORT
Capital Allocation 40%
Time-Based Exit 168 Hours (7 Days)
Model_03

Funding Squeeze

A rare but highly profitable contrarian strategy. It monitors the perpetual futures funding rate. When the rate hits its absolute negative cap while the 200-day SMA is actively declining, it signals a "deep-bear capitulation" event, triggering an aggressive short-squeeze counter-trade.

Target Asset BTC & ETH
Directionality CONTRARIAN SHORT
Capital Allocation 20%
src/strategies/funding_squeeze.py
def evaluate_squeeze(df: pd.DataFrame, funding_data) -> bool: # Check SMA200 slope sma_slope = df['sma_200'].diff(5).iloc[-1] # Check Funding Rate Cap (e.g. -0.075% on Binance/Deribit) current_funding = funding_data['last_funding_rate'] is_capped = current_funding <= -0.00075 # Deep Bear Capitulation Logic if sma_slope < 0 and is_capped: logger.warning("CAPITULATION DETECTED. PREPARING SQUEEZE.") return True return False

Access The Code.

The complete logic for all three models is available on GitHub.

GITHUB_REPO READ_PHILOSOPHY