5f7702469e
- Removed v10 TradingView indicator (moneyline_v10_momentum_dots.pinescript) - Removed v10 penalty system from signal-quality.ts (-30/-25 point penalties) - Removed backtest result files (sweep_*.csv) - Updated copilot-instructions.md to remove v10 references - Simplified direction-specific quality thresholds (LONG 90+, SHORT 80+) Rationale: - 1,944 parameter combinations tested in backtest - All top results IDENTICAL (568 trades, $498 P&L, 61.09% WR) - Momentum parameters had ZERO impact on trade selection - Profit factor 1.027 too low (barely profitable after fees) - Max drawdown -$1,270 vs +$498 profit = terrible risk-reward - v10 penalties were blocking good trades (bug: applied to wrong positions) Keeping v9 as production system - simpler, proven, effective.
222 lines
6.0 KiB
Python
222 lines
6.0 KiB
Python
"""
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Discrete Fourier Transforms - helper.py
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"""
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from numpy.core import integer, empty, arange, asarray, roll
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from numpy.core.overrides import array_function_dispatch, set_module
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# Created by Pearu Peterson, September 2002
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__all__ = ['fftshift', 'ifftshift', 'fftfreq', 'rfftfreq']
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integer_types = (int, integer)
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def _fftshift_dispatcher(x, axes=None):
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return (x,)
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@array_function_dispatch(_fftshift_dispatcher, module='numpy.fft')
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def fftshift(x, axes=None):
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"""
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Shift the zero-frequency component to the center of the spectrum.
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This function swaps half-spaces for all axes listed (defaults to all).
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Note that ``y[0]`` is the Nyquist component only if ``len(x)`` is even.
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Parameters
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----------
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x : array_like
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Input array.
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axes : int or shape tuple, optional
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Axes over which to shift. Default is None, which shifts all axes.
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Returns
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-------
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y : ndarray
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The shifted array.
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See Also
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--------
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ifftshift : The inverse of `fftshift`.
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Examples
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--------
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>>> freqs = np.fft.fftfreq(10, 0.1)
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>>> freqs
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array([ 0., 1., 2., ..., -3., -2., -1.])
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>>> np.fft.fftshift(freqs)
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array([-5., -4., -3., -2., -1., 0., 1., 2., 3., 4.])
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Shift the zero-frequency component only along the second axis:
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>>> freqs = np.fft.fftfreq(9, d=1./9).reshape(3, 3)
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>>> freqs
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array([[ 0., 1., 2.],
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[ 3., 4., -4.],
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[-3., -2., -1.]])
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>>> np.fft.fftshift(freqs, axes=(1,))
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array([[ 2., 0., 1.],
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[-4., 3., 4.],
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[-1., -3., -2.]])
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"""
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x = asarray(x)
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if axes is None:
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axes = tuple(range(x.ndim))
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shift = [dim // 2 for dim in x.shape]
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elif isinstance(axes, integer_types):
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shift = x.shape[axes] // 2
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else:
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shift = [x.shape[ax] // 2 for ax in axes]
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return roll(x, shift, axes)
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@array_function_dispatch(_fftshift_dispatcher, module='numpy.fft')
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def ifftshift(x, axes=None):
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"""
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The inverse of `fftshift`. Although identical for even-length `x`, the
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functions differ by one sample for odd-length `x`.
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Parameters
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----------
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x : array_like
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Input array.
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axes : int or shape tuple, optional
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Axes over which to calculate. Defaults to None, which shifts all axes.
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Returns
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-------
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y : ndarray
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The shifted array.
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See Also
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--------
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fftshift : Shift zero-frequency component to the center of the spectrum.
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Examples
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--------
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>>> freqs = np.fft.fftfreq(9, d=1./9).reshape(3, 3)
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>>> freqs
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array([[ 0., 1., 2.],
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[ 3., 4., -4.],
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[-3., -2., -1.]])
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>>> np.fft.ifftshift(np.fft.fftshift(freqs))
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array([[ 0., 1., 2.],
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[ 3., 4., -4.],
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[-3., -2., -1.]])
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"""
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x = asarray(x)
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if axes is None:
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axes = tuple(range(x.ndim))
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shift = [-(dim // 2) for dim in x.shape]
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elif isinstance(axes, integer_types):
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shift = -(x.shape[axes] // 2)
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else:
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shift = [-(x.shape[ax] // 2) for ax in axes]
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return roll(x, shift, axes)
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@set_module('numpy.fft')
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def fftfreq(n, d=1.0):
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"""
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Return the Discrete Fourier Transform sample frequencies.
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The returned float array `f` contains the frequency bin centers in cycles
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per unit of the sample spacing (with zero at the start). For instance, if
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the sample spacing is in seconds, then the frequency unit is cycles/second.
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Given a window length `n` and a sample spacing `d`::
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f = [0, 1, ..., n/2-1, -n/2, ..., -1] / (d*n) if n is even
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f = [0, 1, ..., (n-1)/2, -(n-1)/2, ..., -1] / (d*n) if n is odd
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Parameters
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----------
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n : int
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Window length.
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d : scalar, optional
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Sample spacing (inverse of the sampling rate). Defaults to 1.
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Returns
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-------
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f : ndarray
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Array of length `n` containing the sample frequencies.
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Examples
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--------
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>>> signal = np.array([-2, 8, 6, 4, 1, 0, 3, 5], dtype=float)
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>>> fourier = np.fft.fft(signal)
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>>> n = signal.size
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>>> timestep = 0.1
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>>> freq = np.fft.fftfreq(n, d=timestep)
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>>> freq
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array([ 0. , 1.25, 2.5 , ..., -3.75, -2.5 , -1.25])
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"""
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if not isinstance(n, integer_types):
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raise ValueError("n should be an integer")
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val = 1.0 / (n * d)
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results = empty(n, int)
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N = (n-1)//2 + 1
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p1 = arange(0, N, dtype=int)
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results[:N] = p1
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p2 = arange(-(n//2), 0, dtype=int)
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results[N:] = p2
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return results * val
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@set_module('numpy.fft')
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def rfftfreq(n, d=1.0):
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"""
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Return the Discrete Fourier Transform sample frequencies
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(for usage with rfft, irfft).
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The returned float array `f` contains the frequency bin centers in cycles
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per unit of the sample spacing (with zero at the start). For instance, if
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the sample spacing is in seconds, then the frequency unit is cycles/second.
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Given a window length `n` and a sample spacing `d`::
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f = [0, 1, ..., n/2-1, n/2] / (d*n) if n is even
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f = [0, 1, ..., (n-1)/2-1, (n-1)/2] / (d*n) if n is odd
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Unlike `fftfreq` (but like `scipy.fftpack.rfftfreq`)
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the Nyquist frequency component is considered to be positive.
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Parameters
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----------
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n : int
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Window length.
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d : scalar, optional
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Sample spacing (inverse of the sampling rate). Defaults to 1.
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Returns
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-------
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f : ndarray
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Array of length ``n//2 + 1`` containing the sample frequencies.
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Examples
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--------
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>>> signal = np.array([-2, 8, 6, 4, 1, 0, 3, 5, -3, 4], dtype=float)
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>>> fourier = np.fft.rfft(signal)
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>>> n = signal.size
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>>> sample_rate = 100
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>>> freq = np.fft.fftfreq(n, d=1./sample_rate)
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>>> freq
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array([ 0., 10., 20., ..., -30., -20., -10.])
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>>> freq = np.fft.rfftfreq(n, d=1./sample_rate)
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>>> freq
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array([ 0., 10., 20., 30., 40., 50.])
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"""
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if not isinstance(n, integer_types):
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raise ValueError("n should be an integer")
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val = 1.0/(n*d)
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N = n//2 + 1
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results = arange(0, N, dtype=int)
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return results * val
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