Pratik Bhadane
71b6343e92
Refresh the benchmark and performance surface across the repo. This updates the benchmark wrappers and helper scripts, regenerates the checked-in benchmark and perf-contract artifacts, and folds in the related roadmap, compatibility, and example notebook changes that belong with this performance-focused pass. Harden the Python CI and local pre-push flow so the same checks pass reliably in both places. The workflow and pre-push script now use module-safe uv typecheck invocations, the Python test environment installs the optional MCP dependency needed by the MCP server tests, and one-off root benchmark outputs are ignored to keep the repo clean. Align local tooling with the current project configuration by updating the Ruff pre-commit hook, tightening the API typing and MCP server helpers, and refreshing the lockfile to pick up the audited PyJWT fix while preserving the rest of the staged source changes.
5.2 KiB
5.2 KiB
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")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:])In [ ]:
rsi = RSI(close, timeperiod=14)
print("RSI(14):", rsi[-5:])
print(f"RSI range: [{np.nanmin(rsi):.2f}, {np.nanmax(rsi):.2f}]")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:])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:])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)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:])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")In [ ]:
from ferro_ta.exceptions import check_timeperiod
from ferro_ta import FerroTAValueError
try:
check_timeperiod(0)
except FerroTAValueError as e:
print("Caught:", e)