Indicators

Vectorized technical indicators operating on numpy arrays. All functions release the GIL for parallel computation.

Moving Averages

ema(input, period) -> ndarray

Exponential Moving Average.

result = flox.ema(closes, period=20)

sma(input, period) -> ndarray

Simple Moving Average.

result = flox.sma(closes, period=20)

rma(input, period) -> ndarray

Wilder's Moving Average (alpha = 1/period). Used internally by RSI and ATR.

result = flox.rma(closes, period=14)

dema(input, period) -> ndarray

Double EMA: 2*EMA - EMA(EMA). Reduces lag versus standard EMA.

result = flox.dema(closes, period=20)

tema(input, period) -> ndarray

Triple EMA: 3*EMA - 3*EMA(EMA) + EMA(EMA(EMA)). Further lag reduction.

result = flox.tema(closes, period=20)

kama(input, period=10) -> ndarray

Kaufman Adaptive Moving Average. Adjusts smoothing based on market efficiency.

result = flox.kama(closes, period=10)

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Oscillators

rsi(input, period) -> ndarray

Relative Strength Index (0–100).

result = flox.rsi(closes, period=14)

macd(input, fast=12, slow=26, signal=9) -> dict

Moving Average Convergence Divergence. Returns a dict with three arrays.

result = flox.macd(closes, fast=12, slow=26, signal=9)
macd_line = result['line']
signal_line = result['signal']
histogram = result['histogram']

Key	Description
line	MACD line (fast EMA - slow EMA)
signal	Signal line (EMA of MACD line)
histogram	MACD - Signal

stochastic(high, low, close, k_period=14, d_period=3) -> dict

Stochastic oscillator (%K and %D).

result = flox.stochastic(highs, lows, closes, k_period=14, d_period=3)
k = result['k']
d = result['d']

cci(high, low, close, period=20) -> ndarray

Commodity Channel Index.

result = flox.cci(highs, lows, closes, period=20)

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Trend

adx(high, low, close, period=14) -> dict

Average Directional Index with directional indicators.

result = flox.adx(highs, lows, closes, period=14)
adx_values = result['adx']
plus_di = result['plus_di']
minus_di = result['minus_di']

Key	Description
adx	ADX values
plus_di	+DI (positive directional indicator)
minus_di	-DI (negative directional indicator)

chop(high, low, close, period=14) -> ndarray

Choppiness Index (0–100). High values indicate ranging markets.

result = flox.chop(highs, lows, closes, period=14)

slope(input, length=1) -> ndarray

Linear slope over a lookback window.

result = flox.slope(closes, length=5)

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Volatility

atr(high, low, close, period) -> ndarray

Average True Range.

result = flox.atr(highs, lows, closes, period=14)

bollinger(input, period=20, stddev=2.0) -> dict

Bollinger Bands.

result = flox.bollinger(closes, period=20, stddev=2.0)
upper = result['upper']
middle = result['middle']
lower = result['lower']

Key	Description
upper	Upper band (middle + stddev * std)
middle	Middle band (SMA)
lower	Lower band (middle - stddev * std)

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Volume

vwap(close, volume, window=96) -> ndarray

Rolling Volume-Weighted Average Price.

result = flox.vwap(closes, volumes, window=96)

obv(close, volume) -> ndarray

On-Balance Volume.

result = flox.obv(closes, volumes)

cvd(open, high, low, close, volume) -> ndarray

Cumulative Volume Delta. Estimates buying/selling pressure from OHLCV data.

result = flox.cvd(opens, highs, lows, closes, volumes)

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Strategy Helpers

bar_returns(signal_long, signal_short, log_returns) -> ndarray

Compute per-bar returns given position signals and log returns.

returns = flox.bar_returns(signal_long, signal_short, log_returns)

Parameter	Type	Description
signal_long	int8[]	Long position flags (+1 or 0)
signal_short	int8[]	Short position flags (-1 or 0)
log_returns	float64[]	Log returns of the asset

trade_pnl(signal_long, signal_short, log_returns) -> ndarray

Extract per-trade PnL from position signals.

pnls = flox.trade_pnl(signal_long, signal_short, log_returns)

profit_factor(returns) -> float

Ratio of gross profit to gross loss. Values > 1.0 indicate profitability.

pf = flox.profit_factor(returns)

win_rate(trade_pnls) -> float

Fraction of trades with positive PnL.

wr = flox.win_rate(pnls)