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Ultimate Balance Oscillator Trading Strategy

Author: ChaoZhang, Date: 2024-01-12 14:08:33
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Overview

The Ultimate Balance Oscillator trading strategy is a quantitative trading strategy that cleverly combines signals from multiple technical indicators. By harnessing the power of indicators like Rate of Change (ROC), Relative Strength Index (RSI), Commodity Channel Index (CCI), Williams %R and Average Directional Index (ADX), it calculates a composite oscillator to determine market trend and generate trading signals.

The biggest advantage of this strategy lies in its ability to objectively and systematically assess the markets to identify optimal entry and exit points. It triggers a buy signal when the oscillator line crosses above the 0.75 overbought level and an exit signal when crossing below the 0.25 oversold level.

Strategy Logic

The core of the Ultimate Balance Oscillator trading strategy is the computation of a composite oscillator indicator. The steps to calculate this indicator are:

Compute the values of individual technical indicators: ROC, RSI, CCI, Williams %R, and ADX
Standardize these indicator values to the 0-1 range to enable comparison
Use a weighted average methodology to compute a composite oscillator value. Each indicator has an adjustable weighting, with default values of 2 for ROC, 0.5 for RSI, 2 for CCI, 0.5 for %R, and 0.5 for ADX. Multiply each standardized indicator by its weight, sum them up, and divide by total weight to get a 0-1 composite value.
Trigger trade signals when this composite oscillator crosses appropriately set overbought and oversold levels.

As evident, the strategy flexibly utilizes signals from multiple indicators and processes them systematically to determine market trend and make trading decisions. This avoids the market noise from any single indicator and helps maintain robust decisions across various situations.

Advantages

The Ultimate Balance Oscillator trading strategy has several key advantages:

Provides an objective, systematic market analysis methodology by utilizing multiple indicators to overcome limitations of single tools and generate actionable, quant-driven signals.
Optimizes entry and exit timing/precision through the precise values and standardization of the oscillator.
Highly customizable and adaptable to suit individual trading styles and market conditions through adjustable indicator weightings and parameters.
Real-time alert system to notify traders of fresh buy/exit signals and ensure awareness of latest market developments.
Rigorous backtesting and optimization pre-live trading to evaluate performance over historical data and fine-tune parameters for strategy improvement.

Risks

Despite its merits, some key risks in practical application include:

Parameter optimization risk from suboptimal indicator weightings and settings impairing live performance. Requires extensive backtesting to discover ideal parameters.
Oversold/overbought level risk from improper range setting relative to broader market conditions and sentiment.
Divergent indicators risk skewing composite oscillator values. Consider removing or lowering weights of errant indicators.
Quant model limitations where certain market conditions can degrade performance. Maintaining risk awareness as a practitioner is critical.

To mitigate risks, comprehensive backtesting, calibration to understand model limitations, tracking live performance, and flexibility in adjusting parameters or weights based on evolving conditions is strongly advised. Manual overrides also prove invaluable at times.

Enhancement Opportunities

Some ways to further optimize the strategy include:

Expanding the multi-factor model with more diverse technical indicators to improve predictive accuracy.
Applying machine learning techniques like neural networks to uncover latent signals and forecast indicator values.
Incorporating fundamental data like earnings reports and economic indicators to augment quant factors.
Introducing adaptive parameter tuning to dynamically modify weightings and settings based on changing market landscapes.
Building in stop loss mechanisms to actively control downside on individual trades.
Integrating position sizing models based on account size for quantified capital management.

Conclusion

The Ultimate Balance Oscillator trading strategy is an outstanding quant approach, synthesizing the essence of multiple technical indicators into a rigorous methodology for market assessment. With tremendous customizability to suit individual requirements, it provides retail systematic traders a blueprint to thrive. As with any quant strategy, relentless enhancement through backtesting, optimization and innovation to expand model robustness across market environments remains the key pursuit. Overall, the strategy offers invaluable guidance and learnings to quants looking to enhance their trading toolkit. And over time, with greater maturity of models and markets, should deliver exceptional performance.

/*backtest
start: 2023-01-05 00:00:00
end: 2024-01-11 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// © Julien_Eche

//@version=5
strategy("Ultimate Balance Oscillator Strategy", overlay=true)

// Indicator Weights
weightROC = input.float(2, "Rate of Change (ROC) Weight", group="Weightings")
weightRSI = input.float(0.5, "Relative Strength Index (RSI) Weight", group="Weightings")
weightCCI = input.float(2, "Commodity Channel Index (CCI) Weight", group="Weightings")
weightWilliamsR = input.float(0.5, "Williams %R Weight", group="Weightings")
weightADX = input.float(0.5, "Average Directional Index (ADX) Weight", group="Weightings")

// ROC Settings
rocLength = input.int(20, "Length", minval=1, group="ROC")

// RSI Settings
rsiLength = input.int(14, "Length", minval=1, group="RSI")

// CCI Settings
cciLength = input.int(20, "Length", minval=1, group="CCI")

// Williams %R Settings
williamsRLength = input.int(14, "Length", minval=1, group="Williams %R")

// ADX Settings
adxLength = input.int(14, "ADX Length", minval=1, group="ADX")
adxDiLength = input.int(14, "DI Length", minval=1, group="ADX")

// Source
source_options = input.string("hlc3", "Source", options=["open", "high", "low", "close", "hl2", "hlc3", "ohlc4"])

price_open = request.security(syminfo.tickerid, "D", open)
price_high = request.security(syminfo.tickerid, "D", high)
price_low = request.security(syminfo.tickerid, "D", low)
price_close = request.security(syminfo.tickerid, "D", close)
price_hl2 = request.security(syminfo.tickerid, "D", hl2)
price_hlc3 = request.security(syminfo.tickerid, "D", hlc3)
price_ohlc4 = request.security(syminfo.tickerid, "D", ohlc4)

get_source(source_option) =>
    price = price_close
    if source_option == "open"
        price := price_open
    else if source_option == "high"
        price := price_high
    else if source_option == "low"
        price := price_low
    else if source_option == "close"
        price := price_close
    else if source_option == "hl2"
        price := price_hl2
    else if source_option == "hlc3"
        price := price_hlc3
    else
        price := price_ohlc4
    price

src = get_source(source_options)

// Overbought/Oversold Levels
obLevel = input.float(0.75, "Overbought Level")
osLevel = input.float(0.25, "Oversold Level")

// Calculating the indicators
rocValue = ta.change(close, rocLength)
rsiValue = ta.rsi(close, rsiLength)
cciValue = (src - ta.sma(src, cciLength)) / (0.015 * ta.dev(src, cciLength))
williamsRValue = -100 * (ta.highest(high, williamsRLength) - close) / (ta.highest(high, williamsRLength) - ta.lowest(low, williamsRLength))

dirmov(len) =>
    up = ta.change(high)
    down = -ta.change(low)
    plusDM = na(up) ? na : (up > down and up > 0 ? up : 0)
    minusDM = na(down) ? na : (down > up and down > 0 ? down : 0)
    truerange = ta.rma(ta.tr, len)
    plus = fixnan(100 * ta.rma(plusDM, len) / truerange)
    minus = fixnan(100 * ta.rma(minusDM, len) / truerange)
    [plus, minus]

adx(dilen, adxlen) =>
    [plus, minus] = dirmov(dilen)
    sum = plus + minus
    adx = 100 * ta.rma(math.abs(plus - minus) / (sum == 0 ? 1 : sum), adxlen)

adxValue = adx(adxDiLength, adxLength)

// Normalizing the values
normalize(value, min, max) =>
    (value - min) / (max - min)

normalizedROC = normalize(rocValue, ta.lowest(rocValue, rocLength), ta.highest(rocValue, rocLength))
normalizedRSI = normalize(rsiValue, 0, 100)
normalizedCCI = normalize(cciValue, ta.lowest(cciValue, cciLength), ta.highest(cciValue, cciLength))
normalizedWilliamsR = normalize(williamsRValue, ta.lowest(williamsRValue, williamsRLength), ta.highest(williamsRValue, williamsRLength))
normalizedADX = normalize(adxValue, 0, 50)

// Calculating the combined oscillator line
oscillatorLine = (normalizedROC * weightROC + normalizedRSI * weightRSI + normalizedCCI * weightCCI + normalizedWilliamsR * weightWilliamsR + normalizedADX * weightADX) / (weightROC + weightRSI + weightCCI + weightWilliamsR + weightADX)

// Strategy conditions
enterLong = ta.crossover(oscillatorLine, obLevel)
exitLong = ta.crossunder(oscillatorLine, osLevel)

// Strategy orders
if (enterLong)
    strategy.entry("Buy", strategy.long)
if (exitLong)
    strategy.close("Buy")

// Alert conditions
if (enterLong)
    alert("Buy signal")
if (exitLong)
    alert("Exit signal")

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