Liquidity ThermometerThis is a universal indicator that assesses market liquidity based on five key market parameters: volume, volatility, candlestick range, body size, and price momentum.
The indicator does not use open interest data and is suitable for all markets, including spot, futures, and Forex.
This indicator normalizes each metric historically and creates a composite index between 0 and 1, where higher values correspond to a stable and calm market environment, and lower values indicate periods of increased risk and potential liquidity stress.
LT generates an integral liquidity index in the range based on five normalized components:
-nVol — normalized volume, reflecting trading density and activity.
-nATR — the volatility component (ATR), inverted, as high volatility is typically associated with declining liquidity.
-nRange — the normalized candlestick range, also inverted to assess the structural narrowness of the price movement.
-nBody — the normalized candlestick body size (|close − open|), inverted to assess the balance of supply and demand.
-nMove — the normalized value of the price impulse movement (|Δclose|), reflecting short-term price spikes.
Each metric is linearly normalized over a sliding window (200 bars) using the formula:
norm(x) = (x − min) / (max − min),
where at max = min, the value is fixed at 0.5 to ensure stability.
The ALT index is calculated as a weighted combination:
ALT = 0.35 nVol + 0.20 (1 − nATR) + 0.20 (1 − nRange) + 0.15 (1 − nBody) + 0.10 (1 − nMove)
The result is further smoothed using EMA(3) to reduce micronoise.
Red Zone (MLI < 0.25) — Risk, Thin Liquidity
When the indicator falls into the red zone, it means the market is extremely volatile:
Characteristics:
Low volume — small trades have a strong impact on the price.
High volatility — candlesticks rise or fall sharply.
Wide candlestick range — the market is "breathing heavily," easily breaking price extremes.
Impulsive movements — small market shocks lead to sharp spikes.
Thin liquidity — few orders in the order book, large orders "eat up" the market.
What this means for a trader:
🔥 High risk of spikes and false breakouts.
⚠ Possible series of liquidations on leverage.
❌ It is not recommended to enter long or short positions without a filter or protection.
✅ Can be used for short scalping strategies if you know the entry point, but very carefully.
Green Zone (MLI > 0.75) — High Liquidity, Safe Zone
When the indicator rises into the green zone, it means the market is stable and balanced:
Characteristics:
High volume — the market is deep, orders are executed without a strong impact on the price.
Low volatility — candlesticks are stable, no sharp spikes.
Narrow candlestick range — price moves calmly.
Weak impulse movements — no sharp surges.
Sufficient liquidity — the market can handle large orders.
What this means for a trader:
✅ Safe zone for opening positions.
🔄 Easier to set stop-loss and take-profit orders.
💡 You can trade both up and down, the risk of sharp movements is minimal.
⚡ Under these conditions, there is a lower risk of spikes and accidental liquidations.
It does not predict price movements or guarantee results. It is an analytical tool intended for additional research into market structure.
Volatility-analysis
Multi-Mode Seasonality Map [BackQuant]Multi-Mode Seasonality Map
A fast, visual way to expose repeatable calendar patterns in returns, volatility, volume, and range across multiple granularities (Day of Week, Day of Month, Hour of Day, Week of Month). Built for idea generation, regime context, and execution timing.
What is “seasonality” in markets?
Seasonality refers to statistically repeatable patterns tied to the calendar or clock, rather than to price levels. Examples include specific weekdays tending to be stronger, certain hours showing higher realized volatility, or month-end flow boosting volumes. This tool measures those effects directly on your charted symbol.
Why seasonality matters
It’s orthogonal alpha: timing edges independent of price structure that can complement trend, mean reversion, or flow-based setups.
It frames expectations: when a session typically runs hot or cold, you size and pace risk accordingly.
It improves execution: entering during historically favorable windows, avoiding historically noisy windows.
It clarifies context: separating normal “calendar noise” from true anomaly helps avoid overreacting to routine moves.
How traders use seasonality in practice
Timing entries/exits : If Tuesday morning is historically weak for this asset, a mean-reversion buyer may wait for that drift to complete before entering.
Sizing & stops : If 13:00–15:00 shows elevated volatility, widen stops or reduce size to maintain constant risk.
Session playbooks : Build repeatable routines around the hours/days that consistently drive PnL.
Portfolio rotation : Compare seasonal edges across assets to schedule focus and deploy attention where the calendar favors you.
Why Day-of-Week (DOW) can be especially helpful
Flows cluster by weekday (ETF creations/redemptions, options hedging cadence, futures roll patterns, macro data releases), so DOW often encodes a stable micro-structure signal.
Desk behavior and liquidity provision differ by weekday, impacting realized range and slippage.
DOW is simple to operationalize: easy rules like “fade Monday afternoon chop” or “press Thursday trend extension” can be tested and enforced.
What this indicator does
Multi-mode heatmaps : Switch between Day of Week, Day of Month, Hour of Day, Week of Month .
Metric selection : Analyze Returns , Volatility ((high-low)/open), Volume (vs 20-bar average), or Range (vs 20-bar average).
Confidence intervals : Per cell, compute mean, standard deviation, and a z-based CI at your chosen confidence level.
Sample guards : Enforce a minimum sample size so thin data doesn’t mislead.
Readable map : Color palettes, value labels, sample size, and an optional legend for fast interpretation.
Scoreboard : Optional table highlights best/worst DOW and today’s seasonality with CI and a simple “edge” tag.
How it’s calculated (under the hood)
Per bar, compute the chosen metric (return, vol, volume %, or range %) over your lookback window.
Bucket that metric into the active calendar bin (e.g., Tuesday, the 15th, 10:00 hour, or Week-2 of month).
For each bin, accumulate sum , sum of squares , and count , then at render compute mean , std dev , and confidence interval .
Color scale normalizes to the observed min/max of eligible bins (those meeting the minimum sample size).
How to read the heatmap
Color : Greener/warmer typically implies higher mean value for the chosen metric; cooler implies lower.
Value label : The center number is the bin’s mean (e.g., average % return for Tuesdays).
Confidence bracket : Optional “ ” shows the CI for the mean, helping you gauge stability.
n = sample size : More samples = more reliability. Treat small-n bins with skepticism.
Suggested workflows
Pick the lens : Start with Analysis Type = Returns , Heatmap View = Day of Week , lookback ≈ 252 trading days . Note the best/worst weekdays and their CI width.
Sanity-check volatility : Switch to Volatility to see which bins carry the most realized range. Use that to plan stop width and trade pacing.
Check liquidity proxy : Flip to Volume , identify thin vs thick windows. Execute risk in thicker windows to reduce slippage.
Drill to intraday : Use Hour of Day to reveal opening bursts, lunchtime lulls, and closing ramps. Combine with your main strategy to schedule entries.
Calendar nuance : Inspect Week of Month and Day of Month for end-of-month, options-cycle, or data-release effects.
Codify rules : Translate stable edges into rules like “no fresh risk during bottom-quartile hours” or “scale entries during top-quartile hours.”
Parameter guidance
Analysis Period (Days) : 252 for a one-year view. Shorten (100–150) to emphasize the current regime; lengthen (500+) for long-memory effects.
Heatmap View : Start with DOW for robustness, then refine with Hour-of-Day for your execution window.
Confidence Level : 95% is standard; use 90% if you want wider coverage with fewer false “insufficient data” bins.
Min Sample Size : 10–20 helps filter noise. For Hour-of-Day on higher timeframes, consider lowering if your dataset is small.
Color Scheme : Choose a palette with good mid-tone contrast (e.g., Red-Green or Viridis) for quick thresholding.
Interpreting common patterns
Return-positive but low-vol bins : Favorable drift windows for passive adds or tight-stop trend continuation.
Return-flat but high-vol bins : Opportunity for mean reversion or breakout scalping, but manage risk accordingly.
High-volume bins : Better expected execution quality; schedule size here if slippage matters.
Wide CI : Edge is unstable or sample is thin; treat as exploratory until more data accumulates.
Best practices
Revalidate after regime shifts (new macro cycle, liquidity regime change, major exchange microstructure updates).
Use multiple lenses: DOW to find the day, then Hour-of-Day to refine the entry window.
Combine with your core setup signals; treat seasonality as a filter or weight, not a standalone trigger.
Test across assets/timeframes—edges are instrument-specific and may not transfer 1:1.
Limitations & notes
History-dependent: short histories or sparse intraday data reduce reliability.
Not causal: a hot Tuesday doesn’t guarantee future Tuesday strength; treat as probabilistic bias.
Aggregation bias: changing session hours or symbol migrations can distort older samples.
CI is z-approximate: good for fast triage, not a substitute for full hypothesis testing.
Quick setup
Use Returns + Day of Week + 252d to get a clean yearly map of weekday edge.
Flip to Hour of Day on intraday charts to schedule precise entries/exits.
Keep Show Values and Confidence Intervals on while you calibrate; hide later for a clean visual.
The Multi-Mode Seasonality Map helps you convert the calendar from an afterthought into a quantitative edge, surfacing when an asset tends to move, expand, or stay quiet—so you can plan, size, and execute with intent.
