Add WaveTrend Oscillator indicator

[AlexCatarino]

Description

Implements WaveTrendOscillator (extends BarIndicator) per the linked issue. Adds the indicator class, the WTO helper in QCAlgorithm.Indicators.cs, unit tests inheriting CommonIndicatorTests<IBaseDataBar>, and the reference CSV under Tests/TestData/.

The main line (WT1) is an EMA of a normalized channel index derived from HLC3 and its EMA; the signal line (WT2) is a short SMA of the main line:

hlc3 = (High + Low + Close) / 3
esa  = EMA(hlc3, channelPeriod)
d    = EMA(|esa - hlc3|, channelPeriod)
ci   = (hlc3 - esa) / (0.015 * d)
wt1  = EMA(ci, averagePeriod)   (main line, exposed via Current.Value and ChannelIndexAverage)
wt2  = SMA(wt1, signalPeriod)   (signal line, exposed via Signal)

Each sub-indicator (ChannelAverage, ChannelDeviation, ChannelIndexAverage, Signal) is a public read-only property so callers can plot or chain them. IsReady becomes true only once Signal has accumulated signalPeriod samples downstream of the chained EMAs (WarmUpPeriod = 2 * channelPeriod + averagePeriod + signalPeriod - 3).

Related Issue

Closes #6411

Motivation and Context

The WaveTrend Oscillator is a widely-followed momentum oscillator on TradingView for identifying overbought/oversold conditions and main/signal crossovers. Issue #6411 requested it as a first-class LEAN indicator so algorithms can use it without hand-rolling the EMA/SMA chain. Reference: https://www.tradingview.com/script/2KE8wTuF-Indicator-WaveTrend-Oscillator-WT/

Requires Documentation Change

Yes — QuantConnect/Documentation#2309

How Has This Been Tested?

• dotnet build QuantConnect.Lean.sln
• dotnet test Tests/QuantConnect.Tests.csproj --filter "FullyQualifiedName~WaveTrendOscillatorTests" — all 14 tests pass (main-line external-data comparison, signal-line external-data comparison, reset, warm-up, renko, volume-renko, time-moves-forward, constructor argument validation, sub-indicator readiness ordering, etc.).
• Reference values generated via TA-Lib (talib.EMA, talib.SMA) following the script posted by @LouisSzeto in Implement WaveTrend Oscillator Indicator #6411; main and signal lines match within 1e-4.

The reference CSV at Tests/TestData/spy_wto.csv was produced with the script below. Reviewers can paste it into a file and re-run to reproduce the expected values:

Test data generator

"""
Test data generator for WaveTrendOscillator (WTO).

Source: Tier 1 - TA-Lib (talib.EMA + talib.SMA), per the formula in
QuantConnect/Lean issue #6411 (https://github.com/QuantConnect/Lean/issues/6411).

The reference implementation matches the script posted by @LouisSzeto in the
issue thread; only the output format differs (we keep the OHLCV columns and
write a header so the LEAN test framework can parse the file).

Generated: 2026-05-04
Usage:     python generate_test_data.py
Output:    spy_wto.csv

Dependencies: ta-lib, numpy, pandas
"""

import pandas as pd
import talib


def main() -> None:
    url = (
        "https://github.com/QuantConnect/Lean/raw/refs/heads/master/"
        "Data/equity/usa/daily/spy.zip"
    )
    history = pd.read_csv(
        url,
        names=["Date", "Open", "High", "Low", "Close", "Volume"],
    )

    # Lean stores prices scaled by 10000 in the daily zip files.
    for col in ["Open", "High", "Low", "Close"]:
        history[col] = pd.to_numeric(history[col], errors="coerce") / 10000.0
    history["Volume"] = pd.to_numeric(history["Volume"], errors="coerce")

    history["Date"] = pd.to_datetime(history["Date"], format="%Y%m%d %H:%M").dt.strftime(
        "%Y-%m-%d"
    )

    channel_period = 10
    average_period = 21
    signal_period = 4

    typical_price = (history["High"] + history["Low"] + history["Close"]) / 3.0
    channel_average = talib.EMA(typical_price, timeperiod=channel_period)
    channel_deviation = talib.EMA(
        (channel_average - typical_price).abs(), timeperiod=channel_period
    )
    channel_index = (typical_price - channel_average) / (0.015 * channel_deviation)
    wt1 = talib.EMA(channel_index, timeperiod=average_period)
    wt2 = talib.SMA(wt1, timeperiod=signal_period)

    history["WT1"] = wt1
    history["WT2"] = wt2

    history.to_csv("spy_wto.csv", index=False, float_format="%.6f")


if __name__ == "__main__":
    main()

Types of changes

• New feature (non-breaking change which adds functionality)

Checklist:

• My code follows the code style of this project.
• I have read the CONTRIBUTING document.
• I have added tests to cover my changes.
• All new and existing tests passed.
• My branch follows the naming convention feature-<issue>-<description>