Indicator Lab · VIDYA · Trend and price
Variable Index Dynamic Average
Adapts an EMA-like smoothing weight using the absolute Chande Momentum Oscillator.
BTCUSDC · 1h · latest 30 complete days · shared snapshot · no account required
How does the magnitude of Chande momentum control the average’s response?
Adapts an EMA-like smoothing weight using the absolute Chande Momentum Oscillator.
One dataset, the same parameters and the same candle for application and explanation.
BTCUSDC · confirmed hourly candles
Snapshot retrieved: 11 Oct 2026, 15:52 UTC · stored snapshot · background refresh scheduled · age when opened: 3,136 min
11 Sept 2026, 15:00 — 11 Oct 2026, 15:00 UTC · 720 evaluation candles · 3000 earlier candles for warm-up only. Your opened snapshot stays fixed. A visit can refresh a stale cache in the background for later visitors; fetching fresh is not a tick stream.
Parameters and precise application
Number of confirmed hourly candles. Warm-up history stays fixed; only this parameter changes.
Directional rule · long or cash
BUY when the actual close crosses the primary line from below; SELL on the reverse cross. Synthetic lines are not fill prices. Confirmed closes only; cash start, no shorts/pyramiding. Fills no earlier than the next open with fees and slippage. Thresholds are analysis conventions, not optimized recommendations.
Shared capital and cost model
Includes the buy fee; the remainder stays in cash.
One adverse markup/markdown. Do not add spread a second time.
1 = next open. No retroactive trade at the signal close.
Preparing the calculation module. The shared market snapshot is already available.
Calculation and interpretation of the same candle
More one-sided movements increase adjustment; balanced movements reduce it.
VIDYAₜ=α|CMOₜ|Cₜ+(1−α|CMOₜ|)VIDYAₜ₋₁, α=2/(n+1)
TA-Lib Wilder CMO / 100; zero state before the first CMO, following pandas-ta. Zero output remains unavailable.
The adaptive factor contains no future information and establishes neither timely regime recognition nor lower trading costs.
Checked sources and reproducibility
- pandas-ta 0.4.71b0 · pandas_ta/overlap/vidya.py
Verified original file from the versioned library; the implemented convention and deliberate differences accompany the formula. · checked 2026-10-11
- The Probability of Backtest Overfitting
Why selection across many historical trials can produce misleading results. This tool does not estimate PBO or claim out-of-sample profitability. · checked 2026-10-11
Data source, versions and hash
- Market data source
- https://data-api.binance.vision/api/v3/klines?symbol=BTCUSDC&interval=1h&startTime=1778338800000&endTime=1791730799999
- Specification
- indicator-library-1.0.0
- Execution model
- signal-simulation-1.0.0
- Data hash
- 42ac75e03e017bcac023d319dabb0928e0562fda0e51f4bfa5549ac50e3e8be4
- Disclosed candles including warm-up
- 3720
The sources support definitions and methodological limits. They do not establish profit for this specific BTCUSDC rule. This analysis is descriptive; parameter trials on the same history are not independent out-of-sample evidence. Parity with a deployed unCoded engine is not claimed.