AdrianoDev
140 lines
4.1 KiB
Python
140 lines
4.1 KiB
Python
from __future__ import annotations
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def sma(values: list[float], period: int) -> float | None:
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if len(values) < period:
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return None
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return sum(values[-period:]) / period
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def rsi(closes: list[float], period: int = 14) -> float | None:
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if len(closes) < period + 1:
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return None
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gains: list[float] = []
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losses: list[float] = []
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for i in range(1, len(closes)):
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delta = closes[i] - closes[i - 1]
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gains.append(max(delta, 0.0))
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losses.append(-min(delta, 0.0))
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avg_gain = sum(gains[:period]) / period
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avg_loss = sum(losses[:period]) / period
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for i in range(period, len(gains)):
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avg_gain = (avg_gain * (period - 1) + gains[i]) / period
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avg_loss = (avg_loss * (period - 1) + losses[i]) / period
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if avg_loss == 0:
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return 100.0
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rs = avg_gain / avg_loss
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return 100.0 - (100.0 / (1.0 + rs))
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def _ema_series(values: list[float], period: int) -> list[float]:
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if len(values) < period:
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return []
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k = 2.0 / (period + 1)
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seed = sum(values[:period]) / period
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out = [seed]
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for v in values[period:]:
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out.append(out[-1] + k * (v - out[-1]))
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return out
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def macd(
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closes: list[float],
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fast: int = 12,
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slow: int = 26,
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signal: int = 9,
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) -> dict[str, float | None]:
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nothing: dict[str, float | None] = {"macd": None, "signal": None, "hist": None}
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if len(closes) < slow + signal:
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return nothing
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ema_fast = _ema_series(closes, fast)
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ema_slow = _ema_series(closes, slow)
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offset = slow - fast
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aligned_fast = ema_fast[offset:]
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macd_line = [f - s for f, s in zip(aligned_fast, ema_slow, strict=False)]
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if len(macd_line) < signal:
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return nothing
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signal_line = _ema_series(macd_line, signal)
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if not signal_line:
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return nothing
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last_macd = macd_line[-1]
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last_sig = signal_line[-1]
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return {
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"macd": last_macd,
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"signal": last_sig,
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"hist": last_macd - last_sig,
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}
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def atr(
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highs: list[float],
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lows: list[float],
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closes: list[float],
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period: int = 14,
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) -> float | None:
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if len(closes) < period + 1:
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return None
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trs: list[float] = []
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for i in range(1, len(closes)):
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tr = max(
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highs[i] - lows[i],
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abs(highs[i] - closes[i - 1]),
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abs(lows[i] - closes[i - 1]),
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)
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trs.append(tr)
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if len(trs) < period:
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return None
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avg = sum(trs[:period]) / period
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for i in range(period, len(trs)):
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avg = (avg * (period - 1) + trs[i]) / period
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return avg
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def adx(
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highs: list[float],
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lows: list[float],
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closes: list[float],
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period: int = 14,
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) -> dict[str, float | None]:
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nothing: dict[str, float | None] = {"adx": None, "+di": None, "-di": None}
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if len(closes) < 2 * period + 1:
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return nothing
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trs: list[float] = []
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plus_dms: list[float] = []
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minus_dms: list[float] = []
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for i in range(1, len(closes)):
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tr = max(
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highs[i] - lows[i],
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abs(highs[i] - closes[i - 1]),
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abs(lows[i] - closes[i - 1]),
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)
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up = highs[i] - highs[i - 1]
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dn = lows[i - 1] - lows[i]
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plus_dm = up if (up > dn and up > 0) else 0.0
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minus_dm = dn if (dn > up and dn > 0) else 0.0
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trs.append(tr)
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plus_dms.append(plus_dm)
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minus_dms.append(minus_dm)
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atr_s = sum(trs[:period])
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pdm_s = sum(plus_dms[:period])
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mdm_s = sum(minus_dms[:period])
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dxs: list[float] = []
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pdi = mdi = 0.0
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for i in range(period, len(trs)):
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atr_s = atr_s - atr_s / period + trs[i]
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pdm_s = pdm_s - pdm_s / period + plus_dms[i]
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mdm_s = mdm_s - mdm_s / period + minus_dms[i]
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pdi = 100.0 * pdm_s / atr_s if atr_s else 0.0
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mdi = 100.0 * mdm_s / atr_s if atr_s else 0.0
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s = pdi + mdi
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dx = 100.0 * abs(pdi - mdi) / s if s else 0.0
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dxs.append(dx)
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if len(dxs) < period:
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return nothing
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adx_val = sum(dxs[:period]) / period
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for i in range(period, len(dxs)):
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adx_val = (adx_val * (period - 1) + dxs[i]) / period
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return {"adx": adx_val, "+di": pdi, "-di": mdi}
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