ai/gateway/knowledge/pandas-ta-reference.md

description

description
Full catalog of technical indicators available via pandas-ta, with parameters and usage for research scripts and custom indicators.

pandas-ta Reference for Research Scripts

This catalog applies to both research scripts and custom indicators. For usage in research scripts see usage-examples.md. For writing custom indicator scripts (with metadata for the TradingView plotter) see indicators/indicator-development.md.

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="BTC/USDT.BINANCE",
    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(range(len(df)), df['ema_20'], label="EMA 20", color="orange", linewidth=1.5)  # range(len(df)), not df.index
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")