始めましょう

"volume"に関するスクリプトを検索

NIFTY IT volumeKEY TAKEAWAYS -Uses NIFTY IT Index Stocks Volume . -NIFTY IT Volume Indicator is created by adding all 10 NIFTY IT Stocks Volume together. -NIFTY IT Volume will be an important indicator in NIFTY IT Index technical analysis because it is used to measure the relative significance of a market move. -The higher the volume during a NIFTY IT index price move, the more significant the move and the lower the volume during a NIFTY IT index price move, the less significant the move. -Moving Average is also added.
Pine Script® インジケーター
bullseyetrading_lkoの投稿
Average Volume at Time (AVAT)Calculation of average volume at current time for a number of previous sessions, known as Average Volume at Time (AVAT). Inputs: * period to use for accumulation. "D" is the default value, useful to view data for each session. * number of previous sessions to average TODO: more intelligent accumulation of number of bars in a session, since there may be sessions with different values TODO: interpolate volume according to current time, inside of the last bar
Pine Script® インジケーター
danilogalisteuの投稿
10
Slow Volume Oscillator Indicator - Just a Better Way To TradeThis script is nothing new. However, I feel that traders do not know the full potential it can provide if they just increase the lookback periods. Tradingview's default Vol Osc uses a fast setting of 5 and a slow setting of 10. It just paints pointless scribbling. Slow all your indicators down and wake up to a whole new world of Technical Analysis. To create consistency with variables I typically use Fib numbers for lookback periods. (3,5,8,13,21,34,55,89,144,233,377,610,987) As noted in chart. An increase in volume can be either bullish or bearish. It's up to you to decide which way price action will move. If you use with other indicators you can create more confirmations. More confirmations = peace of mind My favorite indicators are (Detrended Price Oscillator, Momentum, MACD, Chaikin Money Flow, Ultimate Oscillator, and this Volume Oscillator) Here is just one example of MACD strategy indicator that can be used with this Vol Oscillator
Pine Script® インジケーター
Craig_Claussenの投稿
4
Volume Based Sampling [BackQuant]Volume Based Sampling What this does This indicator converts the usual time-based stream of candles into an event-based stream of “synthetic” bars that are created only when enough trading activity has occurred . You choose the activity definition: Volume bars : create a new synthetic bar whenever the cumulative number of shares/contracts traded reaches a threshold. Dollar bars : create a new synthetic bar whenever the cumulative traded dollar value (price × volume) reaches a threshold. The script then keeps an internal ledger of these synthetic opens, highs, lows, closes, and volumes, and can display them as candles, plot a moving average calculated over the synthetic closes, mark each time a new sample is formed, and optionally overlay the native time-bars for comparison. Why event-based sampling matters Markets do not release information on a clock: activity clusters during news, opens/closes, and liquidity shocks. Event-based bars normalize for that heteroskedastic arrival of information: during active periods you get more bars (finer resolution); during quiet periods you get fewer bars (coarser resolution). Research shows this can reduce microstructure pathologies and produce series that are closer to i.i.d. and more suitable for statistical modeling and ML. In particular: Volume and dollar bars are a common event-time alternative to time bars in quantitative research and are discussed extensively in Advances in Financial Machine Learning (AFML). These bars aim to homogenize information flow by sampling on traded size or value rather than elapsed seconds. The Volume Clock perspective models market activity in “volume time,” showing that many intraday phenomena (volatility, liquidity shocks) are better explained when time is measured by traded volume instead of seconds. Related market microstructure work on flow toxicity and liquidity highlights that the risk dealers face is tied to information intensity of order flow, again arguing for activity-based clocks. How the indicator works (plain English) Choose your bucket type Volume : accumulate volume until it meets a threshold. Dollar Bars : accumulate close × volume until it meets a dollar threshold. Pick the threshold rule Dynamic threshold : by default, the script computes a rolling statistic (mean or median) of recent activity to set the next bucket size. This adapts bar size to changing conditions (e.g., busier sessions produce more frequent synthetic bars). Fixed threshold : optionally override with a constant target (e.g., exactly 100,000 contracts per synthetic bar, or $5,000,000 per dollar bar). Build the synthetic bar While a bucket fills, the script tracks: o_s: first price of the bucket (synthetic open) h_s: running maximum price (synthetic high) l_s: running minimum price (synthetic low) c_s: last price seen (synthetic close) v_s: cumulative native volume inside the bucket d_samples: number of native bars consumed to complete the bucket (a proxy for “how fast” the threshold filled) Emit a new sample Once the bucket meets/exceeds the threshold, a new synthetic bar is finalized and stored. If overflow occurs (e.g., a single native bar pushes you past the threshold by a lot), the code will emit multiple synthetic samples to account for the extra activity. Maintain a rolling history efficiently A ring buffer can overwrite the oldest samples when you hit your Max Stored Samples cap, keeping memory usage stable. Compute synthetic-space statistics The script computes an SMA over the last N synthetic closes and basic descriptors like average bars per synthetic sample, mean and standard deviation of synthetic returns, and more. These are all in event time , not clock time. Inputs and options you will actually use Data Settings Sampling Method : Volume or Dollar Bars. Rolling Lookback : window used to estimate the dynamic threshold from recent activity. Filter : Mean or Median for the dynamic threshold. Median is more robust to spikes. Use Fixed? / Fixed Threshold : override dynamic sizing with a constant target. Max Stored Samples : cap on synthetic history to keep performance snappy. Use Ring Buffer : turn on to recycle storage when at capacity. Indicator Settings SMA over last N samples : moving average in synthetic space . Because its index is sample count, not minutes, it adapts naturally: more updates in busy regimes, fewer in quiet regimes. Visuals Show Synthetic Bars : plot the synthetic OHLC candles. Candle Color Mode : Green/Red: directional close vs open Volume Intensity: opacity scales with synthetic size Neutral: single color Adaptive: graded by how large the bucket was relative to threshold Mark new samples : drop a small marker whenever a new synthetic bar prints. Comparison & Research Show Time Bars : overlay the native time-based candles to visually compare how the two sampling schemes differ. How to read it, step by step Turn on “Synthetic Bars” and optionally overlay “Time Bars.” You will see that during high-activity bursts, synthetic bars print much faster than time bars. Watch the synthetic SMA . Crosses in synthetic space can be more meaningful because each update represents a roughly comparable amount of traded information. Use the “Avg Bars per Sample” in the info table as a regime signal. Falling average bars per sample means activity is clustering, often coincident with higher realized volatility. Try Dollar Bars when price varies a lot but share count does not; they normalize by dollar risk taken in each sample. Volume Bars are ideal when share count is a better proxy for information flow in your instrument. Quant finance background and citations Event time vs. clock time : Easley, López de Prado, and O’Hara advocate measuring intraday phenomena on a volume clock to better align sampling with information arrival. This framing helps explain volatility bursts and liquidity droughts and motivates volume-based bars. Flow toxicity and dealer risk : The same authors show how adverse selection risk changes with the intensity and informativeness of order flow, further supporting activity-based clocks for modeling and risk management. AFML framework : In Advances in Financial Machine Learning , event-driven bars such as volume, dollar, and imbalance bars are presented as superior sampling units for many ML tasks, yielding more stationary features and fewer microstructure distortions than fixed time bars. ( Alpaca ) Practical use cases 1) Regime-aware moving averages The synthetic SMA in event time is not fooled by quiet periods: if nothing of consequence trades, it barely updates. This can make trend filters less sensitive to calendar drift and more sensitive to true participation. 2) Breakout logic on “equal-information” samples The script exposes simple alerts such as breakout above/below the synthetic SMA . Because each bar approximates a constant amount of activity, breakouts are conditioned on comparable informational mass, not arbitrary time buckets. 3) Volatility-adaptive backtests If you use synthetic bars as your base data stream, most signal rules become self-paced : entry and exit opportunities accelerate in fast markets and slow down in quiet regimes, which often improves the realism of slippage and fill modeling in research pipelines (pair this indicator with strategy code downstream). 4) Regime diagnostics Avg Bars per Sample trending down: activity is dense; expect larger realized ranges. Return StdDev (synthetic) rising: noise or trend acceleration in event time; re-tune risk. Interpreting the info panel Method : your sampling choice and current threshold. Total Samples : how many synthetic bars have been formed. Current Vol/Dollar : how much of the next bucket is already filled. Bars in Bucket : native bars consumed so far in the current bucket. Avg Bars/Sample : lower means higher trading intensity. Avg Return / Return StdDev : return stats computed over synthetic closes . Research directions you can build from here Imbalance and run bars Extend beyond pure volume or dollar thresholds to imbalance bars that trigger on directional order flow imbalance (e.g., buy volume minus sell volume), as discussed in the AFML ecosystem. These often further homogenize distributional properties used in ML. alpaca.markets Volume-time indicators Re-compute classical indicators (RSI, MACD, Bollinger) on the synthetic stream. The premise is that signals are updated by traded information , not seconds, which may stabilize indicator behavior in heteroskedastic regimes. Liquidity and toxicity overlays Combine synthetic bars with proxies of flow toxicity to anticipate spread widening or volatility clustering. For instance, tag synthetic bars that surpass multiples of the threshold and test whether subsequent realized volatility is elevated. Dollar-risk parity sampling for portfolios Use dollar bars to align samples across assets by notional risk, enabling cleaner cross-asset features and comparability in multi-asset models (e.g., correlation studies, regime clustering). AFML discusses the benefits of event-driven sampling for cross-sectional ML feature engineering. Microstructure feature set Compute duration in native bars per synthetic sample , range per sample , and volume multiple of threshold as inputs to state classifiers or regime HMMs . These features are inherently activity-aware and often predictive of short-horizon volatility and trend persistence per the event-time literature. ( Alpaca ) Tips for clean usage Start with dynamic thresholds using Median over a sensible lookback to avoid outlier distortion, then move to Fixed thresholds when you know your instrument’s typical activity scale. Compare time bars vs synthetic bars side by side to develop intuition for how your market “breathes” in activity time. Keep Max Stored Samples reasonable for performance; the ring buffer avoids memory creep while preserving a rolling window of research-grade data.
Pine Script® インジケーター
BackQuantの投稿