Overview
The Z-Score Dynamic Moving Average Crossover Quantitative Trading Strategy is a comprehensive trading system that combines the statistical Z-score principle with moving average crossover signals. This strategy calculates the standardized deviation (Z-Score) of price and generates trading signals based on the crossover between short-term and long-term smoothed Z-Score lines. This approach not only considers absolute price changes but also focuses on the relative position of price in statistical distribution, providing a market entry and exit mechanism based on probability and statistical principles.

Strategy Principle
The core of this strategy is based on the Z-Score statistical indicator for trading decisions. Z-Score measures how many standard deviations a data point is from the mean, calculated as Z = (X - μ) / σ, where X is the current price, μ is the mean, and σ is the standard deviation.

The strategy implementation includes the following steps:

1. Calculate the raw Z-Score value based on closing price using a user-defined base period (default 3)
2. Apply short-term smoothing (default period 3) and long-term smoothing (default period 5) to the raw Z-Score
3. Generate a buy signal when the short-term Z-Score line crosses above the long-term Z-Score line
4. Generate a sell signal when the short-term Z-Score line crosses below the long-term Z-Score line
5. Implement a signal spacing mechanism to avoid overtrading, requiring a minimum number of bars (default 5) between identical signals

Additionally, the strategy provides traditional moving averages (MA) as auxiliary references, including short-term (5-period), medium-term (21-period), and long-term (60-period) lines. These moving averages help traders observe price trend changes more intuitively.

Strategy Advantages

1. Statistical Foundation: The Z-Score indicator is based on statistical principles, standardizing price fluctuations to identify abnormal price movements. When Z-Score is extremely high or low, it indicates significant deviation from the mean, suggesting potential mean reversion opportunities.

2. Dual Filtering Mechanism: The strategy uses both Z-Score indicators and moving averages, forming a dual confirmation mechanism. Z-Score crossovers provide primary signals, while the moving average system serves as an auxiliary tool for trend confirmation.

3. Flexible Parameter Settings: Users can adjust the Z-Score calculation period, smoothing parameters, and signal spacing according to different markets and trading instruments, enabling personalized strategy configuration.

4. Real-time Trading Feedback: The strategy visually displays buy and sell signals through the graphical interface and provides real-time feedback on position status and profit/loss, allowing traders to quickly evaluate strategy performance.

5. Alert Function: An integrated alert system can issue real-time reminders when buy or sell signals are triggered, helping traders capture trading opportunities promptly.

Strategy Risks

1. Parameter Sensitivity: Z-Score calculation and smoothing parameters significantly impact strategy performance. Improper parameter settings may lead to overtrading or missing important signals. It is recommended to find the most suitable parameter combination for specific markets through historical backtesting.

2. Weakness in Ranging Markets: In sideways markets, Z-Score may frequently cross the mean, generating excessive trading signals, increasing transaction costs, and potentially leading to consecutive losses. Consider adding trend filtering conditions to the strategy or pausing trading when a ranging market is identified.

3. Statistical Assumption Risk: Z-Score assumes price fluctuations follow a normal distribution, but actual markets may have tail risks and abnormal fluctuations. The strategy may fail in extreme market environments.

4. Lagging Issue: Due to the use of moving averages for smoothing, signals have a certain lag, which may result in less than ideal entry and exit timing in volatile markets.

5. Lack of Stop-Loss Mechanism: The current version of the strategy does not include explicit stop-loss mechanisms, which may face significant losses during major adverse market movements. Adding stop-loss conditions is recommended in practical applications to control risk.

Optimization Directions

1. Add Trend Filters: Introduce additional trend indicators (such as ADX or Bollinger Band width) to identify market states and adopt different strategy parameters or trading logic in strong trend markets versus ranging markets.

2. Implement Adaptive Parameters: Dynamically adjust Z-Score calculation periods and signal spacing based on market volatility, allowing the strategy to better adapt to different market environments.

3. Improve Risk Management: Introduce stop-loss mechanisms based on ATR or fixed percentages, and design reasonable position management rules to control per-trade risk.

4. Multi-timeframe Analysis: Consider Z-Score signals across different timeframes, executing trades only when signals align across multiple timeframes to increase signal reliability.

5. Combine with Other Indicators: Consider combining Z-Score with volume, Relative Strength Index (RSI), or Bollinger Bands to build more comprehensive trading conditions.

6. Optimize Backtesting Framework: Expand strategy evaluation criteria to consider not only total returns but also maximum drawdown, Sharpe ratio, profit/loss ratio, and other comprehensive indicators for a complete assessment of strategy performance.

Conclusion
The Z-Score Dynamic Moving Average Crossover Quantitative Trading Strategy standardizes price through statistical methods and combines moving average crossover signals to provide a systematic approach to trading decisions. This strategy is particularly suitable for finding trading opportunities when prices deviate and then revert to the mean, with the advantages of solid theoretical foundation and clear signals.

However, traders need to pay attention to parameter optimization and risk control when applying this strategy, especially as strategy performance may vary in different market environments. By adding trend filters, improving risk management, and combining multiple indicators, the stability and adaptability of the strategy can be further enhanced.

Ultimately, any trading strategy needs to be rigorously validated and continuously optimized in actual market environments. The Z-Score strategy, as a quantitative trading tool, provides traders with a framework for market analysis and decision-making based on statistical principles, worthy of exploration and in-depth study in practice.

Strategy source code

/*backtest
start: 2024-07-13 18:40:00
end: 2025-06-25 08:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"ETH_USDT","balance":50000000}]
*/

//@version=6
strategy("Z Score Main image strategy — v1.02", overlay=true)

// Parameters
enableZScore = input.bool(true, title="Enable Z-score strategy")
zBaseLength  = input.int(3, minval=1, title="Z-score base period")
shortSmooth  = input.int(3, title="Short-term smoothing")
longSmooth   = input.int(5, title="Long-term smoothing")
gapBars      = input.int(5, minval=1, title="Number of K lines with the same signal interval")

// Z score
f_zscore(src, len) =>
    mean = ta.sma(src, len)
    std = ta.stdev(src, len)
    (src - mean) / std

zRaw   = f_zscore(close, zBaseLength)
zShort = ta.sma(zRaw, shortSmooth)
zLong  = ta.sma(zRaw, longSmooth)

baseLongCond = zShort > zLong
baseExitCond = zShort < zLong

// Signal interval
var int lastEntryBar = na
var int lastExitBar  = na

// Strategy logic
if enableZScore
    if baseLongCond and (na(lastEntryBar) or bar_index - lastEntryBar > gapBars)
        strategy.entry("Z Score", strategy.long)
        lastEntryBar := bar_index
        alert("The Z-score strategy triggers a buy signal and it is recommended to open a long position", alert.freq_once_per_bar)

    if baseExitCond and (na(lastExitBar) or bar_index - lastExitBar > gapBars)
        strategy.close("Z Score", comment="Z Score")
        lastExitBar := bar_index
        alert("The Z-score strategy triggers a sell signal and it is recommended to close the position and exit", alert.freq_once_per_bar)

// Buy and sell icons
plotshape(baseLongCond and (na(lastEntryBar) or bar_index - lastEntryBar > gapBars),
     title="Buy ​​signal", location=location.belowbar, color=color.green, style=shape.labelup, text="buy")

plotshape(baseExitCond and (na(lastExitBar) or bar_index - lastExitBar > gapBars),
     title="Sell ​​signal", location=location.abovebar, color=color.red, style=shape.labeldown, text="sell")

// Profit and loss table
var table positionTable = table.new(position.bottom_right, 2, 2, border_width=1)
table.cell(positionTable, 0, 0, "Opening price", text_color=color.white, bgcolor=color.gray)
table.cell(positionTable, 1, 0, "Unrealized profit and loss (%)", text_color=color.white, bgcolor=color.gray)

isLong        = strategy.position_size > 0
entryPrice    = strategy.position_avg_price
unrealizedPnL = isLong ? (close - entryPrice) / entryPrice * 100 : na
pnlColor      = unrealizedPnL > 0 ? color.green : unrealizedPnL < 0 ? color.red : color.gray

if isLong
    table.cell(positionTable, 0, 1, str.tostring(entryPrice, "#.####"), text_color=color.gray, bgcolor=color.new(color.gray, 90))
    table.cell(positionTable, 1, 1, str.tostring(unrealizedPnL, "#.##") + " %", text_color=pnlColor, bgcolor=color.new(pnlColor, 90))
else
    table.cell(positionTable, 0, 1, "—", text_color=color.gray, bgcolor=color.new(color.gray, 90))
    table.cell(positionTable, 1, 1, "—", text_color=color.gray, bgcolor=color.new(color.gray, 90))
    // === Display MA moving average ===
showMA        = input.bool(true, title="Display MA moving average")
maShortPeriod = input.int(5, title="Short-term MA period")
maMidPeriod   = input.int(21, title="Medium-term MA period")
maLongPeriod  = input.int(60, title="Long-term MA period")

maShort = ta.sma(close, maShortPeriod)
maMid   = ta.sma(close, maMidPeriod)
maLong  = ta.sma(close, maLongPeriod)

plot(showMA ? maShort : na, title="MA Short-term", color=color.rgb(0, 9, 11, 1), linewidth=1)
plot(showMA ? maMid   : na, title="MA Mid-term", color=color.orange, linewidth=1)
plot(showMA ? maLong  : na, title="MA Long-term", color=color.blue, linewidth=1)

Strategy parameters

The original address: Z-Score Dynamic Moving Average Crossover Quantitative Trading Strategy