Курнат Андрей
107 lines
3.4 KiB
Python
107 lines
3.4 KiB
Python
from __future__ import annotations
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from statistics import fmean
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from crypto_spot_bot.models import Candle
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def add_indicators(candles: list[Candle]) -> list[Candle]:
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closes = [c.close for c in candles]
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highs = [c.high for c in candles]
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lows = [c.low for c in candles]
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volumes = [c.volume for c in candles]
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ema20 = _ema(closes, 20)
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ema50 = _ema(closes, 50)
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ema200 = _ema(closes, 200)
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rsi14 = _rsi(closes, 14)
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atr14 = _atr(highs, lows, closes, 14)
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volume_ma20 = _sma(volumes, 20)
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for index, candle in enumerate(candles):
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candle.ema_20 = ema20[index]
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candle.ema_50 = ema50[index]
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candle.ema_200 = ema200[index]
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candle.rsi_14 = rsi14[index]
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candle.atr_14 = atr14[index]
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candle.volume_ma_20 = volume_ma20[index]
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return candles
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def _ema(values: list[float], period: int) -> list[float | None]:
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if not values:
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return []
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result: list[float | None] = [None] * len(values)
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if len(values) < period:
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return result
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seed = fmean(values[:period])
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result[period - 1] = seed
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multiplier = 2 / (period + 1)
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previous = seed
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for index in range(period, len(values)):
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previous = (values[index] - previous) * multiplier + previous
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result[index] = previous
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return result
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def _sma(values: list[float], period: int) -> list[float | None]:
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result: list[float | None] = []
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for index in range(len(values)):
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if index + 1 < period:
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result.append(None)
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else:
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result.append(fmean(values[index + 1 - period : index + 1]))
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return result
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def _rsi(closes: list[float], period: int) -> list[float | None]:
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result: list[float | None] = [None] * len(closes)
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if len(closes) <= period:
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return result
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gains: list[float] = []
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losses: list[float] = []
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for index in range(1, period + 1):
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delta = closes[index] - closes[index - 1]
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gains.append(max(delta, 0.0))
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losses.append(abs(min(delta, 0.0)))
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avg_gain = fmean(gains)
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avg_loss = fmean(losses)
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result[period] = _rsi_value(avg_gain, avg_loss)
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for index in range(period + 1, len(closes)):
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delta = closes[index] - closes[index - 1]
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gain = max(delta, 0.0)
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loss = abs(min(delta, 0.0))
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avg_gain = ((avg_gain * (period - 1)) + gain) / period
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avg_loss = ((avg_loss * (period - 1)) + loss) / period
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result[index] = _rsi_value(avg_gain, avg_loss)
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return result
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def _rsi_value(avg_gain: float, avg_loss: float) -> float:
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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 - (100 / (1 + rs))
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def _atr(highs: list[float], lows: list[float], closes: list[float], period: int) -> list[float | None]:
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result: list[float | None] = [None] * len(closes)
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true_ranges: list[float] = []
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for index in range(len(closes)):
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if index == 0:
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true_ranges.append(highs[index] - lows[index])
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else:
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true_ranges.append(
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max(
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highs[index] - lows[index],
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abs(highs[index] - closes[index - 1]),
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abs(lows[index] - closes[index - 1]),
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)
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)
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if len(true_ranges) < period:
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return result
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atr = fmean(true_ranges[:period])
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result[period - 1] = atr
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for index in range(period, len(true_ranges)):
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atr = ((atr * (period - 1)) + true_ranges[index]) / period
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result[index] = atr
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return result
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