# -*- coding: utf-8 -*- from numba import njit from numpy import greater, nan, zeros_like from pandas import DataFrame, DateOffset, Series, infer_freq from pandas_ta._typing import DictLike from pandas_ta.utils import ( nb_nonzero_range, v_datetime_ordered, v_series, v_str ) # Support for Pandas v1.4.x and v2.2.x td_mapping = { 'Y': 'years', 'YE': 'years', 'M': 'months', 'ME': 'months', 'D': 'days', } @njit(cache=True) def pivot_camarilla(high, low, close): tp = (high + low + close) / 3 hl_range = nb_nonzero_range(high, low) s1 = close - 11 / 120 * hl_range s2 = close - 11 / 60 * hl_range s3 = close - 0.275 * hl_range s4 = close - 0.55 * hl_range r1 = close + 11 / 120 * hl_range r2 = close + 11 / 60 * hl_range r3 = close + 0.275 * hl_range r4 = close + 0.55 * hl_range return tp, s1, s2, s3, s4, r1, r2, r3, r4 @njit(cache=True) def pivot_classic(high, low, close): tp = (high + low + close) / 3 hl_range = nb_nonzero_range(high, low) s1 = 2 * tp - high s2 = tp - hl_range s3 = tp - 2 * hl_range s4 = tp - 3 * hl_range r1 = 2 * tp - low r2 = tp + hl_range r3 = tp + 2 * hl_range r4 = tp + 3 * hl_range return tp, s1, s2, s3, s4, r1, r2, r3, r4 @njit(cache=True) def pivot_demark(open_, high, low, close): if (open_ == close).all(): tp = 0.25 * (high + low + 2 * close) elif greater(close, open_).all(): tp = 0.25 * (2 * high + low + close) else: tp = 0.25 * (high + 2 * low + close) s1 = 2 * tp - high r1 = 2 * tp - low return tp, s1, r1 @njit(cache=True) def pivot_fibonacci(high, low, close): tp = (high + low + close) / 3 hl_range = nb_nonzero_range(high, low) s1 = tp - 0.382 * hl_range s2 = tp - 0.618 * hl_range s3 = tp - hl_range r1 = tp + 0.382 * hl_range r2 = tp + 0.618 * hl_range r3 = tp + hl_range return tp, s1, s2, s3, r1, r2, r3 @njit(cache=True) def pivot_traditional(high, low, close): tp = (high + low + close) / 3 hl_range = nb_nonzero_range(high, low) s1 = 2 * tp - high s2 = tp - hl_range s3 = tp - 2 * hl_range s4 = tp - 2 * hl_range r1 = 2 * tp - low r2 = tp + hl_range r3 = tp + 2 * hl_range r4 = tp + 2 * hl_range return tp, s1, s2, s3, s4, r1, r2, r3, r4 @njit(cache=True) def pivot_woodie(open_, high, low): tp = (2 * open_ + high + low) / 4 hl_range = nb_nonzero_range(high, low) s1 = 2 * tp - high s2 = tp - hl_range s3 = low - 2 * (high - tp) s4 = s3 - hl_range r1 = 2 * tp - low r2 = tp + hl_range r3 = high + 2 * (tp - low) r4 = r3 + hl_range return tp, s1, s2, s3, s4, r1, r2, r3, r4 def pivots( open_: Series, high: Series, low: Series, close: Series, method: str = None, anchor: str = None, **kwargs: DictLike ) -> DataFrame: """Pivot Points Pivot Points attempt to identify support and resistance levels. There are many different methods of calculating Pivot Points. The most common (and default) method is: Traditional. Other methods include: Camarilla, Classic, Demark, Fibonacci, and Woodie. Sources: * [sierrachart](https://www.sierrachart.com/index.php?page=doc/PivotPoints.html) Parameters: open_ (Series): ```open``` Series high (Series): ```high``` Series low (Series): ```low``` Series close (Series): ```close``` Series method (str): Pivot methode. Default: ```'traditional'``` anchor (str): Anchor frequency. Default: ```'D'``` Other Parameters: fillna (value): ```pd.DataFrame.fillna(value)``` Returns: (DataFrame): 3, 7 or 9 columns Note: [Pandas Offset Aliases](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases) """ # Validate open_ = v_series(open_) high = v_series(high) low = v_series(low) close = v_series(close) if open_ is None or high is None or low is None or close is None: return None methods = [ "traditional", "fibonacci", "woodie", "classic", "demark", "camarilla" ] method = v_str(method, methods[0]) if close.index.size < 3: return # Emergency Break if not v_datetime_ordered(close): print("[!] Pivots requires an ordered DatetimeIndex.") return dt_index = close.index freq = infer_freq(dt_index) if anchor and isinstance(anchor, str) and len(anchor) >= 1: anchor = anchor.upper() else: anchor = "D" # Resample if freq does not match the anchor if freq is not anchor: df = DataFrame( data={ "open": open_.resample(anchor).first(), "high": high.resample(anchor).max(), "low": low.resample(anchor).min(), "close": close.resample(anchor).last() } ) df.dropna(inplace=True) else: df = DataFrame( data={"open": open_, "high": high, "low": low, "close": close}, index=dt_index ) np_open = df.open.to_numpy() np_high = df.high.to_numpy() np_low = df.low.to_numpy() np_close = df.close.to_numpy() # Create nan arrays for "demark" and "fibonacci" pivots _nan_array = zeros_like(np_close) _nan_array[:] = nan tp = s1 = s2 = s3 = s4 = r1 = r2 = r3 = r4 = _nan_array # Calculate if method == "camarilla": tp, s1, s2, s3, s4, r1, r2, r3, r4 = \ pivot_camarilla(np_high, np_low, np_close) elif method == "classic": tp, s1, s2, s3, s4, r1, r2, r3, r4 = \ pivot_classic(np_high, np_low, np_close) elif method == "demark": tp, s1, r1 = pivot_demark(np_open, np_high, np_low, np_close) elif method == "fibonacci": tp, s1, s2, s3, r1, r2, r3 = pivot_fibonacci(np_high, np_low, np_close) elif method == "woodie": tp, s1, s2, s3, s4, r1, r2, r3, r4 = \ pivot_woodie(np_open, np_high, np_low) else: # Traditional tp, s1, s2, s3, s4, r1, r2, r3, r4 = \ pivot_traditional(np_high, np_low, np_close) # Name and Category _props = f"PIVOTS_{method[:4].upper()}_{anchor}" df[f"{_props}_P"] = tp df[f"{_props}_S1"], df[f"{_props}_S2"] = s1, s2 df[f"{_props}_S3"], df[f"{_props}_S4"] = s3, s4 df[f"{_props}_R1"], df[f"{_props}_R2"] = r1, r2 df[f"{_props}_R3"], df[f"{_props}_R4"] = r3, r4 time_unit = td_mapping.get(anchor.upper(), None) if time_unit: time_delta = DateOffset(**{time_unit: 1}) df.index = df.index + time_delta else: print(f"[!] Unsupported time anchor {anchor}.") if freq is not anchor: df = df.reindex(dt_index, method="ffill") df = df.iloc[:,4:] if method in ["demark", "fibonacci"]: df.drop(columns=[x for x in df.columns if all(df[x].isna())], inplace=True) df.name = _props df.category = "overlap" return df