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.
5.3 KiB
5.3 KiB
In [ ]:
import ferro_ta.config as config
import numpy as np
from ferro_ta.backtest import backtest
from ferro_ta.pipeline import Pipeline
from ferro_ta import BBANDS, EMA, RSI, SMA
# Synthetic data
np.random.seed(42)
n = 300
close = np.cumprod(1 + np.random.randn(n) * 0.01) * 100
volume = np.random.randint(1000, 10000, n).astype(float)
print(f"Generated {n} bars, final price: {close[-1]:.2f}")In [ ]:
result = backtest(close, strategy="rsi_30_70", timeperiod=14)
print("Strategy: RSI 30/70")
print(f"Final equity: {result.final_equity:.4f}")
print(f"Number of trades: {result.n_trades}")
print(f"Return: {(result.final_equity - 1.0) * 100:.2f}%")In [ ]:
result2 = backtest(close, strategy="sma_crossover", fast=10, slow=30)
print("Strategy: SMA Crossover (10/30)")
print(f"Final equity: {result2.final_equity:.4f}")
print(f"Number of trades: {result2.n_trades}")In [ ]:
# Set global defaults
config.set_default("timeperiod", 20) # global default for all indicators
config.set_default("RSI.timeperiod", 14) # RSI-specific override
print("Current defaults:", config.list_defaults())
print("RSI defaults:", config.get_defaults_for("RSI"))
print("SMA defaults:", config.get_defaults_for("SMA"))In [ ]:
# Context manager for temporary overrides
with config.Config(timeperiod=5):
temp_default = config.get_default("timeperiod")
print(f"Inside context: timeperiod={temp_default}")
print(f"After context: timeperiod={config.get_default('timeperiod')}") # back to 20
# Clean up
config.reset()In [ ]:
pipe = (
Pipeline()
.add("sma_10", SMA, timeperiod=10)
.add("sma_30", SMA, timeperiod=30)
.add("ema_10", EMA, timeperiod=10)
.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,
)
)
features = pipe.run(close)
print("Feature columns:", list(features.keys()))
# Build a simple feature matrix (last 5 complete rows)
valid_start = 30 # warmup
feature_matrix = np.column_stack([v[valid_start:] for v in features.values()])
print(f"Feature matrix shape: {feature_matrix.shape}")In [ ]:
# Signal: long when RSI < 40 AND close > SMA_30; flat otherwise
rsi_vals = features["rsi_14"]
sma30_vals = features["sma_30"]
signal = np.where((rsi_vals < 40) & (close > sma30_vals), 1.0, 0.0)
position = np.roll(signal, 1) # trade on next bar open
position[0] = 0.0
returns = np.diff(close) / close[:-1]
strategy_returns = returns * position[1:]
equity = np.cumprod(1 + strategy_returns)
print(f"Final equity: {equity[-1]:.4f}")
print(f"Number of signal bars: {int(signal.sum())}")