ferro-ta/examples/quickstart.ipynb

ferro-ta Quick Start

This notebook demonstrates the core ferro-ta API.

Install:

pip install ferro-ta

In [ ]:

import numpy as np

from ferro_ta import BBANDS, EMA, MACD, RSI, SMA

# Synthetic OHLCV data
np.random.seed(42)
n = 200
close = np.cumprod(1 + np.random.randn(n) * 0.01) * 100
high = close * (1 + np.abs(np.random.randn(n)) * 0.005)
low = close * (1 - np.abs(np.random.randn(n)) * 0.005)
volume = np.random.randint(1_000, 10_000, n).astype(float)

print(f"Generated {n} bars")

Moving Averages

In [ ]:

sma_20 = SMA(close, timeperiod=20)
ema_20 = EMA(close, timeperiod=20)

print("SMA(20):", sma_20[-5:])
print("EMA(20):", ema_20[-5:])

RSI

In [ ]:

rsi = RSI(close, timeperiod=14)
print("RSI(14):", rsi[-5:])
print(f"RSI range: [{np.nanmin(rsi):.2f}, {np.nanmax(rsi):.2f}]")

MACD

In [ ]:

macd_line, signal, hist = MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
print("MACD line:  ", macd_line[-5:])
print("Signal:     ", signal[-5:])
print("Histogram:  ", hist[-5:])

Bollinger Bands

In [ ]:

upper, middle, lower = BBANDS(close, timeperiod=20, nbdevup=2.0, nbdevdn=2.0)
print("Upper band: ", upper[-5:])
print("Middle band:", middle[-5:])
print("Lower band: ", lower[-5:])

Batch API — multiple symbols at once

In [ ]:

from ferro_ta.batch import batch_rsi, batch_sma

# Simulate 5 symbols
data = np.random.default_rng(0).random((200, 5)) * 100 + 50
sma_result = batch_sma(data, timeperiod=20)
rsi_result = batch_rsi(data, timeperiod=14)

print("Batch SMA shape:", sma_result.shape)  # (200, 5)
print("Batch RSI shape:", rsi_result.shape)  # (200, 5)

Pipeline API — compose multiple indicators

In [ ]:

from ferro_ta.pipeline import Pipeline

pipe = (
    Pipeline()
    .add("sma_20", SMA, timeperiod=20)
    .add("ema_20", EMA, timeperiod=20)
    .add("rsi_14", RSI, timeperiod=14)
    .add(
        "bb",
        BBANDS,
        output_keys=["bb_upper", "bb_mid", "bb_lower"],
        timeperiod=20,
        nbdevup=2.0,
        nbdevdn=2.0,
    )
)

results = pipe.run(close)
print("Pipeline outputs:", list(results.keys()))
print("SMA last 3:", results["sma_20"][-3:])

Pandas Integration

In [ ]:

try:
    import pandas as pd

    s = pd.Series(close, name="close")
    sma_pd = SMA(s, timeperiod=20)
    print("Result type:", type(sma_pd))  # pandas.Series
    print("Index preserved:", list(sma_pd.index[:3]))
except ImportError:
    print("pandas not installed")

Error Handling

In [ ]:

from ferro_ta import FerroTAValueError
from ferro_ta.exceptions import check_timeperiod

try:
    check_timeperiod(0)
except FerroTAValueError as e:
    print("Caught:", e)