Indicators¶

FLOX has around 25 indicators, split across moving averages, oscillators, volatility, volume, and statistics. Each works in two modes: batch (pass an array, get an array back) and streaming (call .update() each tick, check .ready before reading .value). The same indicator set is exposed by every binding — Python, Node.js, Codon, and the C++ core all share one implementation.

This page covers what each one actually measures and when you'd want it.

Moving averages¶

SMA¶

Arithmetic mean over a sliding window. Every bar gets equal weight.

SMA(n) = (x₁ + x₂ + ... + xₙ) / n

Responds slowly to recent price movement — which makes it less useful for short-term signals but reasonable for long-term trend reference. If you need a baseline to compare against, this is the simplest one.

EMA¶

Weighted average where recent bars count more. Smoothing factor: α = 2/(period+1).

EMA = α × price + (1 − α) × EMA_prev

Responds faster than SMA. MACD, ATR smoothing, and most other indicators build on it. The default choice when you need a moving average and have no strong reason to pick something else.

RMA¶

Same structure as EMA but α = 1/period — slower. RSI and ATR use it internally (it's Wilder's smoothing).

You probably won't use RMA directly unless you're reimplementing RSI/ATR from scratch or need exact TradingView parity.

DEMA¶

DEMA = 2 × EMA − EMA(EMA)

Less lag than EMA. Warmup takes 2 × period bars. Worth trying when EMA crossover signals are consistently arriving a bar or two late.

TEMA¶

TEMA = 3 × EMA − 3 × EMA(EMA) + EMA(EMA(EMA))

More lag reduction than DEMA. Warmup is 3 × period. Very reactive — expect more false signals in choppy markets.

KAMA¶

Kaufman Adaptive Moving Average. Adjusts the smoothing factor based on an "efficiency ratio": how much price moved versus how much it oscillated. Goes fast in trending markets, slow in sideways ones.

Useful if you want one MA that self-adjusts across regimes rather than manually switching between a fast and a slow EMA.

Slope¶

Linear regression slope over a rolling window. Not exactly a moving average, but used in similar ways.

slope = Σ(x − x̄)(y − ȳ) / Σ(x − x̄)²

Positive = upward trend. Magnitude is the steepness. Good for momentum filtering.

Oscillators¶

RSI¶

Ratio of average gains to average losses over period bars, scaled to 0–100.

RSI = 100 − 100 / (1 + RS)
RS  = avg_gain / avg_loss   (Wilder's RMA)

The classic levels (70 = overbought, 30 = oversold) work well in ranging markets. In a strong trend, RSI can stay above 70 for a long time — which is a feature, not a bug, depending on your strategy. Period 14 is standard; shorter periods make it noisier.

MACD¶

Difference between a fast EMA and a slow EMA, with a signal line on top.

MACD line   = EMA(fast) − EMA(slow)    [default: 12, 26]
signal line = EMA(MACD line, 9)
histogram   = MACD line − signal line

Crossing zero signals a momentum shift. The histogram slope shows whether momentum is accelerating or fading. Watching the histogram flatten before the line crossover is a common entry filter.

Stochastic¶

Where is the close relative to the recent high-low range?

%K = 100 × (close − lowest_low) / (highest_high − lowest_low)
%D = SMA(%K, d_period)

Ranges 0–100. Common levels: 80 overbought, 20 oversold. The %D line smooths %K. Main signals are the %K/%D crossover and divergence between price and the indicator.

CCI¶

Distance of the typical price from its SMA, divided by mean absolute deviation.

TP  = (high + low + close) / 3
CCI = (TP − SMA(TP)) / (0.015 × mean_deviation)

Scaled so roughly 70% of values fall between −100 and +100. Values outside that band signal unusual strength or weakness. Often used as a momentum filter rather than a primary entry signal.

Bollinger Bands¶

SMA with bands at ±N standard deviations.

middle = SMA(price, period)
upper  = middle + multiplier × std(price, period)
lower  = middle − multiplier × std(price, period)

Bands widen in volatile markets and contract when price quiets down. The squeeze — when bands get unusually narrow — often comes before a directional move. Price touching a band is context, not a signal by itself.

Trend¶

ADX¶

Measures trend strength, not direction. Comes with two directional indicators.

+DI  = Wilder(upward movement, period)
−DI  = Wilder(downward movement, period)
ADX  = Wilder(|+DI − −DI| / (+DI + −DI), period)

ADX above 25 typically means there's a trend worth following. Below 20 is choppy. +DI and −DI tell you direction; ADX tells you whether to care.

CHOP¶

How much price moved as a fraction of the maximum possible range over the period.

CHOP = 100 × log₁₀(Σ ATR / (highest_high − lowest_low)) / log₁₀(period)

High CHOP (near 100) means directionless. Low (near 0) means trending. More useful for switching between strategy modes than as a signal itself.

Volatility¶

ATR¶

Average range per bar, accounting for gaps.

true_range = max(high − low, |high − prev_close|, |low − prev_close|)
ATR        = RMA(true_range, period)

Not directional. Standard use: position sizing (stop = N × ATR from entry) and filtering signals by volatility regime.

Parkinson volatility¶

Uses high-low ranges instead of close-to-close returns.

σ = sqrt(mean(ln(H/L)²) / (4 × ln(2)))

More efficient than close-to-close when you have intraday OHLC data. Underestimates if the market gaps frequently, since gaps don't show in the H-L range.

Rogers-Satchell volatility¶

OHLC volatility estimator designed to handle drift (trending markets) without bias.

σ² = mean(ln(H/C) × ln(H/O) + ln(L/C) × ln(L/O))

Better than Parkinson for trending assets. Both can be annualized by multiplying by sqrt(periods_per_year).

Volume¶

OBV¶

Running total: add volume on up bars, subtract on down bars.

OBV = OBV_prev + (close > prev_close ? +volume : −volume)

The absolute value is meaningless — you're looking at the trend of OBV and divergences from price. Price makes a new high but OBV doesn't: the rally may not have conviction.

VWAP¶

Average price weighted by volume, over a rolling window.

VWAP = Σ(price × volume) / Σ(volume)

Price above VWAP = buyers have been in control over that window. Used as a fair-value reference and order execution benchmark. Institutions care about VWAP when filling large orders.

CVD¶

Running total of buying minus selling volume, inferred from OHLCV.

CVD = CVD_prev + buy_volume − sell_volume

Similar to OBV but directional. Divergence between CVD and price is one of the more reliable short-term signals when it appears.

Statistical¶

Rolling z-score¶

How many standard deviations is the current value from the rolling mean?

z = (x − mean(x, period)) / std(x, period)

Standard use is mean-reversion signals: z > 2 or < −2 marks statistically unusual levels. Returns NaN when std = 0.

Skewness¶

Fisher-Pearson skewness of a rolling window — measures distribution asymmetry.

skew = (n / ((n−1)(n−2))) × Σ((x − mean) / std)³

Positive = right tail (large gains skew the distribution). Negative = left tail. Common in volatility forecasting and regime detection. Requires period ≥ 3; NaN if std = 0.

Kurtosis¶

Tail heaviness relative to a normal distribution (Fisher excess kurtosis, so a normal distribution = 0).

High kurtosis means fat tails — more outliers than a normal distribution would predict. Used in risk models to understand tail exposure. Requires period ≥ 4; NaN if std = 0.

Shannon entropy¶

How random is the recent price distribution? Normalized to [0, 1] using histogram binning.

H = −Σ p(x) × ln(p(x)) / ln(bins)

1 = uniform distribution (maximum uncertainty). 0 = all values identical. Entropy tends to drop before trends develop and rise in choppy, uncertain markets — useful as a regime filter.

Correlation¶

Rolling Pearson correlation between two series.

r = Σ(x − x̄)(y − ȳ) / sqrt(Σ(x − x̄)² × Σ(y − ȳ)²)

Range [−1, 1]. Used for pairs construction, cross-asset filters, or detecting when a relationship is breaking down. Returns NaN when either series is constant within the window.

For the cross-symbol case (Correlation(BTC, ETH) from a live strategy or paired bar arrays), the engine does not auto-align timestamps — the caller must synchronise the streams first. See the cross-symbol how-to for the alignment recipe.