TrigWave Suite [InvestorUnknown]

10 thg 11, 2024

The TrigWave Suite combines Sine-weighted, Cosine-weighted, and Hyperbolic Tangent moving averages (HTMA) with a Directional Movement System (DMS) and a Relative Strength System (RSS).

Hyperbolic Tangent Moving Average (HTMA)

• The HTMA smooths the price by applying a hyperbolic tangent transformation to the difference between the price and a simple moving average. It also adjusts this value by multiplying it by a standard deviation to create a more stable signal.
  

// Function to calculate Hyperbolic Tangenttanh(x) =>    e_x                 = math.exp(x)    e_neg_x             = math.exp(-x)    (e_x - e_neg_x) / (e_x + e_neg_x)// Function to calculate Hyperbolic Tangent Moving Averagehtma(src, len, mul) =>    tanh_src            = tanh((src - ta.sma(src, len)) * mul) * ta.stdev(src, len) + ta.sma(src, len)    htma                = ta.sma(tanh_src, len)

Sine-Weighted Moving Average (SWMA)

• The SWMA applies sine-based weights to historical prices. This gives more weight to the central data points, making it responsive yet less prone to noise.
  

// Function to calculate the Sine-Weighted Moving Averagef_Sine_Weighted_MA(series float src, simple int length) =>    var float[] sine_weights = array.new_float(0)    array.clear(sine_weights)  // Clear the array before recalculating weights    for i = 0 to length - 1        weight = math.sin((math.pi * (i + 1)) / length)        array.push(sine_weights, weight)    // Normalize the weights    sum_weights = array.sum(sine_weights)    for i = 0 to length - 1        norm_weight = array.get(sine_weights, i) / sum_weights        array.set(sine_weights, i, norm_weight)    // Calculate Sine-Weighted Moving Average    swma = 0.0    if bar_index >= length        for i = 0 to length - 1            swma := swma + array.get(sine_weights, i) * src    swma

Cosine-Weighted Moving Average (CWMA)

• The CWMA uses cosine-based weights for data points, which produces a more stable trend-following behavior, especially in low-volatility markets.
  

f_Cosine_Weighted_MA(series float src, simple int length) =>    var float[] cosine_weights = array.new_float(0)    array.clear(cosine_weights)  // Clear the array before recalculating weights    for i = 0 to length - 1        weight = math.cos((math.pi * (i + 1)) / length) + 1  // Shift by adding 1        array.push(cosine_weights, weight)    // Normalize the weights    sum_weights = array.sum(cosine_weights)    for i = 0 to length - 1        norm_weight = array.get(cosine_weights, i) / sum_weights        array.set(cosine_weights, i, norm_weight)    // Calculate Cosine-Weighted Moving Average    cwma = 0.0    if bar_index >= length        for i = 0 to length - 1            cwma := cwma + array.get(cosine_weights, i) * src    cwma

Directional Movement System (DMS)

• DMS is used to identify trend direction and strength based on directional movement. It uses ADX to gauge trend strength and combines +DI and -DI for directional bias.
  

// Function to calculate Directional Movement Systemf_DMS(simple int dmi_len, simple int adx_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)    trur = ta.rma(ta.tr, dmi_len)    plus = fixnan(100 * ta.rma(plusDM, dmi_len) / trur)    minus = fixnan(100 * ta.rma(minusDM, dmi_len) / trur)    sum = plus + minus    adx = 100 * ta.rma(math.abs(plus - minus) / (sum == 0 ? 1 : sum), adx_len)    dms_up = plus > minus and adx > minus    dms_down = plus < minus and adx > plus    dms_neutral = not (dms_up or dms_down)    signal = dms_up ? 1 : dms_down ? -1 : 0

Relative Strength System (RSS)

• RSS employs RSI and an adjustable moving average type (SMA, EMA, or HMA) to evaluate whether the market is in a bullish or bearish state.
  

// Function to calculate Relative Strength Systemf_RSS(rsi_src, rsi_len, ma_type, ma_len) =>    rsi = ta.rsi(rsi_src, rsi_len)    ma = switch ma_type        "SMA" => ta.sma(rsi, ma_len)        "EMA" => ta.ema(rsi, ma_len)        "HMA" => ta.hma(rsi, ma_len)    signal = (rsi > ma and rsi > 50) ? 1 : (rsi < ma and rsi < 50) ? -1 : 0

ATR Adjustments

• To minimize false signals, the HTMA, SWMA, and CWMA signals are adjusted with an Average True Range (ATR) filter:
  

// Calculate ATR adjusted components for HTMA, CWMA and SWMAfloat atr                           = ta.atr(atr_len)float htma_up                       = htma + (atr * atr_mult)float htma_dn                       = htma - (atr * atr_mult)float swma_up                       = swma + (atr * atr_mult)float swma_dn                       = swma - (atr * atr_mult)float cwma_up                       = cwma + (atr * atr_mult)float cwma_dn                       = cwma - (atr * atr_mult)

• This adjustment allows for better adaptation to varying market volatility, making the signal more reliable.
  

Signals and Trend Calculation

• The indicator generates a Trend Signal by aggregating the output from each component. Each component provides a directional signal that is combined to form a unified trend reading. The trend value is then converted into a long (1), short (-1), or neutral (0) state.
  

Backtesting Mode and Performance Metrics

• The Backtesting Mode includes a performance metrics table that compares the Buy and Hold strategy with the TrigWave Suite strategy. Key statistics like Sharpe Ratio, Sortino Ratio, and Omega Ratio are displayed to help users assess performance. Note that due to labels and plotchar use, automatic scaling may not function ideally in backtest mode.
  

Alerts and Visualization

• Trend Direction Alerts: Set up alerts for long and short signals
  
• Color Bars and Gradient Option: Bars are colored based on the trend direction, with an optional gradient for smoother visual feedback.
  

Important Notes

• Customization: Default settings are experimental and not intended for trading/investing purposes. Users are encouraged to adjust and calibrate the settings to optimize results according to their trading style.
  
• Backtest Results Disclaimer: Please note that backtest results are not indicative of future performance, and no strategy guarantees success.
  

16 thg 12, 2024

Phát hành các Ghi chú

Updated the code to pinescript v6, added backtesting library v2 with more backtesting functions and removed old backtesting functions from the code

16 thg 12, 2024

Phát hành các Ghi chú

Updated the code to pinescript v6, added backtesting library v2 with more backtesting functions and removed old backtesting functions from the code