PandasTechAnalysis

This is a Technical Analysis Python library for markets using Pandas/Numpy. It's fast and simple.

Tech Analysis:

SMA
EMA
MACD
ATR
Bollinger Bands
Bollinger %B
Bollinger Band Width
RSI
Stoch RSI
ADX
Commodity Channel Index (CCI)
Stochastic Oscillator (Stoch)
Awesome Oscillator (AO)
Accelerator Oscillator (AC)
Williams %R
On-Balance Volume (OBV)
Money Flow Index (MFI)
Chaikin Money Flow (CMF)
Force Index (FI)
Keltner Channels

Dependencies:

Pandas
Numpy

How to use:

Get your candles/values data and convert it to Pandas data.
Calculate tech analysis with PandasTechAnalysis.
Visualize with Plotly or Bokeh.

Example:

import numpy as np
import pandas as pd
import pandas_tech_analysis as pta

candles_df: pd.DataFrame  # Your retrieved Candles/Values data from a market.
# Say, your candles_df['open'] is candles open values.
# Say, your candles_df['high'] is candles high values.
# Say, your candles_df['low'] is candles low values.
# Say, your candles_df['close'] is candles close values.
# Say, your candles_df['volume'] is volume values.

# Candles Candles EMA
ema_7 = pta.calculate_ema(candles_df['close'], 7)
ema_14 = pta.calculate_ema(candles_df['close'], 14)
ema_28 = pta.calculate_ema(candles_df['close'], 28)

# RSI
rsi_14 = pta.calculate_rsi_standard(candles_df['close'], window=14)
rsi_28 = pta.calculate_rsi_standard(candles_df['close'], window=28)
rsi_42 = pta.calculate_rsi_standard(candles_df['close'], window=42)

# Calculate Volume SMA
volume_sma_5 = pta.calculate_sma(candles_df['volume'], 5)
volume_sma_10 = pta.calculate_sma(candles_df['volume'], 10)

# Stoch RSI. Note only stoc_k and stoch_d values are used mostly.
stoch_rsi, stoc_k, stoch_d = pta.calculate_stoch_rsi(candles_df['close'], rsi_14, window=14)

# MACD
macd, macd_signal, macd_hist = pta.calculate_macd(candles_df['close'])

# Calculate Bollinger Bands
bb_mid, bb_upper, bb_lower =  pta.calculate_bollinger_bands(candles_df['close'])

# Calculate ATR
atr = pta.calculate_atr(candles_df['high'], candles_df['low'], candles_df['close'])

# Calculate ADX
# map_to_one=True is mapped from 0 to 1. map_to_one=False is mapped from 0% to 100%.
adx, adx_plus_di, adx_minus_di = pta.calculate_adx(candles_df['high'], candles_df['low'], candles_df['close'], window=14, map_to_one=True)

mifth/PandasTechAnalysis

PandasTechAnalysis