pandas-ta Reference for Research Scripts

The sandbox environment uses pandas-ta as the standard indicator library. Always use it for technical indicator calculations; do not write manual rolling/ewm implementations.

import pandas_ta as ta

Calling Convention

pandas-ta functions accept a Series (or OHLCV columns) plus keyword parameters that match pandas-ta's documented argument names:

# Single-series indicator
rsi = ta.rsi(df['close'], length=14)          # returns Series

# OHLCV indicator
atr = ta.atr(df['high'], df['low'], df['close'], length=14)

# Multi-output indicator (returns DataFrame)
macd_df = ta.macd(df['close'], fast=12, slow=26, signal=9)
# columns: MACD_12_26_9, MACDh_12_26_9, MACDs_12_26_9

bbands_df = ta.bbands(df['close'], length=20, std=2.0)
# columns: BBL_20_2.0, BBM_20_2.0, BBU_20_2.0, BBB_20_2.0, BBP_20_2.0

Default Parameters

Key defaults to keep in mind:

Most period/length indicators: length=14 (use length= not timeperiod=)
bbands: length=20, std=2.0 (note: single std, not separate upper/lower)
macd: fast=12, slow=26, signal=9
stoch: k=14, d=3, smooth_k=3
psar: af0=0.02, af=0.02, max_af=0.2
vwap: anchor='D' (requires DatetimeIndex)
ichimoku: tenkan=9, kijun=26, senkou=52

Available Indicators

These match the indicators supported by the TradingView web client. Use the pandas-ta function name shown here (lowercase):

Overlap / Moving Averages — plotted on the price pane

Function	Description
`sma`	Simple Moving Average — plain arithmetic mean over `length` periods
`ema`	Exponential Moving Average — more weight on recent prices
`wma`	Weighted Moving Average — linearly increasing weights
`dema`	Double EMA — two layers of EMA to reduce lag
`tema`	Triple EMA — three layers of EMA, even less lag than DEMA
`trima`	Triangular MA — double-smoothed SMA, very smooth
`kama`	Kaufman Adaptive MA — adapts speed to market noise/trending conditions
`t3`	T3 Moving Average — Tillson's smooth, low-lag MA using six EMAs
`hma`	Hull MA — very low-lag MA using WMAs
`alma`	Arnaud Legoux MA — Gaussian-weighted MA with reduced lag and noise
`midpoint`	Midpoint of close over `length` periods: (highest + lowest) / 2
`midprice`	Midpoint of high/low over `length` periods
`supertrend`	Trend-following band (ATR-based) that flips above/below price
`ichimoku`	Ichimoku Cloud — multi-line Japanese trend/support/resistance system
`vwap`	Volume-Weighted Average Price — average price weighted by volume, resets on `anchor`
`vwma`	Volume-Weighted MA — like SMA but candles weighted by volume
`bbands`	Bollinger Bands — SMA ± N standard deviations; returns upper, mid, lower bands

Momentum — typically plotted in a separate pane

Function	Description
`rsi`	Relative Strength Index — 0–100 oscillator measuring speed of price changes
`macd`	MACD — difference of two EMAs plus signal line and histogram
`stoch`	Stochastic Oscillator — %K/%D, measures close vs recent high/low range
`stochrsi`	Stochastic RSI — applies stochastic formula to RSI values
`cci`	Commodity Channel Index — deviation of price from its statistical mean
`willr`	Williams %R — inverse stochastic, −100 to 0 oscillator
`mom`	Momentum — raw price change over `length` periods
`roc`	Rate of Change — percentage price change over `length` periods
`trix`	TRIX — 1-period % change of a triple-smoothed EMA
`cmo`	Chande Momentum Oscillator — ratio of up/down momentum, −100 to 100
`adx`	Average Directional Index — strength of trend (0–100, direction-agnostic)
`aroon`	Aroon — measures how recently the highest/lowest price occurred; returns Up, Down, Oscillator
`ao`	Awesome Oscillator — difference of 5- and 34-period simple MAs of midprice
`bop`	Balance of Power — measures buying vs selling pressure: (close−open)/(high−low)
`uo`	Ultimate Oscillator — weighted combo of three period (fast/medium/slow) buying pressure ratios
`apo`	Absolute Price Oscillator — difference between two EMAs (like MACD without signal line)
`mfi`	Money Flow Index — RSI-like oscillator using price × volume
`coppock`	Coppock Curve — long-term momentum oscillator based on rate-of-change
`dpo`	Detrended Price Oscillator — removes trend to show cycle oscillations
`fisher`	Fisher Transform — converts price into a Gaussian normal distribution
`rvgi`	Relative Vigor Index — compares close−open to high−low to measure trend vigor
`kst`	Know Sure Thing — momentum oscillator from four ROC periods, smoothed

Volatility — plotted on price pane or separate

Function	Description
`atr`	Average True Range — average of true range (greatest of H−L, H−prevC, L−prevC)
`kc`	Keltner Channels — EMA ± N × ATR bands around price
`donchian`	Donchian Channels — highest high / lowest low over `length` periods

Volume — plotted in separate pane

Function	Description
`obv`	On Balance Volume — cumulative volume, added on up days, subtracted on down days
`ad`	Accumulation/Distribution — running total of the money flow multiplier × volume
`adosc`	Chaikin Oscillator — EMA difference of the A/D line
`cmf`	Chaikin Money Flow — sum of (money flow volume) / sum of volume over `length`
`eom`	Ease of Movement — relates price change to volume; high = price moves easily
`efi`	Elder's Force Index — combines price change direction with volume magnitude
`kvo`	Klinger Volume Oscillator — EMA difference of volume force
`pvt`	Price Volume Trend — cumulative: volume × percentage price change

Statistics / Price Transforms

Function	Description
`stdev`	Standard Deviation of close over `length` periods
`linreg`	Linear Regression Curve — least-squares line endpoint value over `length` periods
`slope`	Linear Regression Slope — gradient of the regression line
`hl2`	Median Price — (high + low) / 2
`hlc3`	Typical Price — (high + low + close) / 3
`ohlc4`	Average Price — (open + high + low + close) / 4

Trend

Function	Description
`psar`	Parabolic SAR — trailing stop-and-reverse dots that follow price
`vortex`	Vortex Indicator — VI+ / VI− lines measuring upward vs downward trend movement
`chop`	Choppiness Index — 0–100, high = choppy/sideways, low = strong trend

Usage Examples

Single-output indicators

import pandas_ta as ta

df['rsi'] = ta.rsi(df['close'], length=14)
df['ema_20'] = ta.ema(df['close'], length=20)
df['sma_50'] = ta.sma(df['close'], length=50)
df['atr'] = ta.atr(df['high'], df['low'], df['close'], length=14)
df['obv'] = ta.obv(df['close'], df['volume'])
df['adx'] = ta.adx(df['high'], df['low'], df['close'], length=14)['ADX_14']

Multi-output indicators — extract columns by position

# MACD → MACD_12_26_9, MACDh_12_26_9, MACDs_12_26_9
macd_df = ta.macd(df['close'], fast=12, slow=26, signal=9)
df['macd']        = macd_df.iloc[:, 0]  # MACD line
df['macd_hist']   = macd_df.iloc[:, 1]  # Histogram
df['macd_signal'] = macd_df.iloc[:, 2]  # Signal line

# Bollinger Bands → BBL, BBM, BBU, BBB, BBP
bb_df = ta.bbands(df['close'], length=20, std=2.0)
df['bb_lower'] = bb_df.iloc[:, 0]  # BBL
df['bb_mid']   = bb_df.iloc[:, 1]  # BBM
df['bb_upper'] = bb_df.iloc[:, 2]  # BBU

# Stochastic → STOCHk, STOCHd
stoch_df = ta.stoch(df['high'], df['low'], df['close'], k=14, d=3, smooth_k=3)
df['stoch_k'] = stoch_df.iloc[:, 0]
df['stoch_d'] = stoch_df.iloc[:, 1]

# Keltner Channels → KCLe, KCBe, KCUe
kc_df = ta.kc(df['high'], df['low'], df['close'], length=20)
df['kc_lower'] = kc_df.iloc[:, 0]
df['kc_mid']   = kc_df.iloc[:, 1]
df['kc_upper'] = kc_df.iloc[:, 2]

# ADX → ADX_14, DMP_14, DMN_14
adx_df = ta.adx(df['high'], df['low'], df['close'], length=14)
df['adx']  = adx_df.iloc[:, 0]  # ADX strength
df['dmp']  = adx_df.iloc[:, 1]  # +DI
df['dmn']  = adx_df.iloc[:, 2]  # -DI

# Aroon → AROOND_14, AROONU_14, AROONOSC_14
aroon_df = ta.aroon(df['high'], df['low'], length=14)
df['aroon_down'] = aroon_df.iloc[:, 0]
df['aroon_up']   = aroon_df.iloc[:, 1]

# Donchian Channels → DCL, DCM, DCU
dc_df = ta.donchian(df['high'], df['low'], lower_length=20, upper_length=20)
df['dc_lower'] = dc_df.iloc[:, 0]
df['dc_mid']   = dc_df.iloc[:, 1]
df['dc_upper'] = dc_df.iloc[:, 2]

Charting with indicators

import pandas_ta as ta
from dexorder.api import get_api
import asyncio

api = get_api()

df = asyncio.run(api.data.historical_ohlc(
    ticker="BINANCE:BTC/USDT",
    period_seconds=3600,
    start_time="2024-01-01",
    end_time="2024-01-08",
    extra_columns=["volume"]
))

# Compute indicators
df['ema_20'] = ta.ema(df['close'], length=20)
df['rsi'] = ta.rsi(df['close'], length=14)
macd_df = ta.macd(df['close'])
df['macd'] = macd_df.iloc[:, 0]
df['macd_signal'] = macd_df.iloc[:, 2]

# Main price chart with EMA overlay
fig, ax = api.charting.plot_ohlc(df, title="BTC/USDT 1H", volume=True)
ax.plot(df.index, df['ema_20'], label="EMA 20", color="orange", linewidth=1.5)
ax.legend()

# RSI panel
rsi_ax = api.charting.add_indicator_panel(fig, df, columns=["rsi"], ylabel="RSI", ylim=(0, 100))
rsi_ax.axhline(70, color='red', linestyle='--', alpha=0.5)
rsi_ax.axhline(30, color='green', linestyle='--', alpha=0.5)

# MACD panel
api.charting.add_indicator_panel(fig, df, columns=["macd", "macd_signal"], ylabel="MACD")

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