technical-indicators

OpenAlgo Technical Indicators

Perform technical analysis using TA-Lib with OpenAlgo market data. Build trading strategies based on indicators, generate signals, and backtest ideas.

Environment Setup

# Install TA-Lib (requires system library)
# macOS
brew install ta-lib
pip install TA-Lib

# Ubuntu/Debian
sudo apt-get install libta-lib-dev
pip install TA-Lib

# Windows
# Download from https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
pip install TA_Lib‑0.4.28‑cp311‑cp311‑win_amd64.whl

from openalgo import api
import talib
import pandas as pd
import numpy as np

client = api(
    api_key='your_api_key_here',
    host='http://127.0.0.1:5000'
)

Quick Start Scripts

Calculate Indicators

python scripts/indicators.py --symbol SBIN --exchange NSE --interval 5m --days 5

Generate Signals

python scripts/signals.py --symbol NIFTY --exchange NSE_INDEX --strategy rsi_oversold

Scan for Patterns

python scripts/scanner.py --symbols RELIANCE,TCS,INFY,SBIN --exchange NSE --pattern bullish

---

Fetching Data for Analysis

from openalgo import api
import pandas as pd

client = api(api_key='your_key', host='http://127.0.0.1:5000')

# Fetch historical data
df = client.history(
    symbol="SBIN",
    exchange="NSE",
    interval="5m",
    start_date="2025-01-01",
    end_date="2025-01-15"
)

# TA-Lib requires numpy arrays
open_prices = df['open'].values
high_prices = df['high'].values
low_prices = df['low'].values
close_prices = df['close'].values
volume = df['volume'].values

---

Overlap Studies (Trend Indicators)

Simple Moving Average (SMA)

import talib

# Calculate SMA
sma_20 = talib.SMA(close_prices, timeperiod=20)
sma_50 = talib.SMA(close_prices, timeperiod=50)
sma_200 = talib.SMA(close_prices, timeperiod=200)

# Add to DataFrame
df['SMA_20'] = sma_20
df['SMA_50'] = sma_50
df['SMA_200'] = sma_200

# Crossover signal
df['SMA_Cross'] = np.where(df['SMA_20'] > df['SMA_50'], 1, -1)