Strategy Quant X -

While SQX has improved its internal backtesting engine, many professional users pair it with Birt’s Tick Data Suite (TDS) or use SQX’s own high-quality data features. This allows for variable spread simulation and commission modeling, ensuring the backtest is as close to reality as possible.

Standard risk metrics (VaR, CVaR) look backward. Strategy Quant X uses counterfactual reasoning. For every trade, the system asks: "If I had done the opposite, would I have made money?" This creates a dynamic hedging overlay that reduces tail risk without sacrificing upside.

A robust strategy must not be sensitive to slight changes in parameters. SQX tests the "Strategy Surface" by varying parameters (e.g., changing a Moving Average from 20 to 21). If a 1% change in parameter causes a 50% drop in profit, the strategy is overfitted and rejected. strategy quant x

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To appreciate Strategy Quant X, one must acknowledge the decay of traditional quant factors. The Fama-French five-factor model has been arbitraged away. Momentum crashes during regime switches. Mean reversion fails during systemic liquidations.

The problem is signal crowding. When every Renaissance Technologies clone buys the same breakout pattern, the pattern ceases to work. Strategy Quant X solves this by moving from linear factors to non-linear strategic games. Instead of asking, "Will this stock go up?" it asks, "What will the other quants do when I place this order?" While SQX has improved its internal backtesting engine,

This is second-order thinking automated. It treats the market as a predator-prey ecosystem, not a random walk.

SQX comes with a visual drag-and-drop editor. If you have a specific idea (e.g., "Buy when RSI is oversold and price is above the 200 MA"), you can build it manually using blocks. This is incredibly powerful for traders who understand logic but do not know programming syntax. To appreciate Strategy Quant X, one must acknowledge

class QuantX:
    def __init__(self, capital, lookback=60):
        self.capital = capital
        self.lookback = lookback
def regime(self, df):
    aroon_up = (df['high'].rolling(25).apply(lambda x: x.argmax()) / 25) * 100
    if aroon_up.iloc[-1] > 70: return 'trend'
    elif aroon_up.iloc[-1] < 30: return 'revert'
    else: return 'neutral'
def signal(self, df):
    rsi_z = (df['rsi'] - df['rsi'].rolling(60).mean()) / df['rsi'].rolling(60).std()
    mom_z = (df['momentum'] - df['momentum'].rolling(60).mean()) / df['momentum'].rolling(60).std()
    return 0.6*rsi_z + 0.4*mom_z
def size(self, df, raw_signal):
    atr = df['atr'].iloc[-1]
    var = df['returns'].rolling(20).quantile(0.05)
    max_units = (0.02 * self.capital) / (atr * np.sqrt(var))
    return np.clip(raw_signal, -max_units, max_units)

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Choose 3 to 5 non-correlated data sources.