add centre of gravity & zlma to ta menu (#663)

* add centre of gravity & zlma to ta menu

* quickfix

gamestonk_terminal/common/technical_analysis/momentum.py

@@ -4,6 +4,7 @@ __docformat__ = "numpy"
 import argparse
 from typing import List
 
+import numpy as np
 import matplotlib.pyplot as plt
 import pandas_ta as ta
 import pandas as pd
@@ -691,3 +692,110 @@ def fisher(
 
     except Exception as e:
         print(e, "\n")
+
+
+def cg(other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame):
+    """Centre of Gravity
+
+    Parameters
+    ----------
+    other_args:List[str]
+        Argparse arguments
+    s_ticker: str
+        Ticker
+    s_interval: str
+        Stock time interval
+    df_stock: pd.DataFrame
+        Dataframe of prices
+    """
+
+    parser = argparse.ArgumentParser(
+        add_help=False,
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+        prog="cg",
+        description="""
+            The Center of Gravity indicator, also known as the COD indicator in short,
+            is a useful feature that enables traders to look into the concurrent price
+            fluctuations and try to predict the upcoming ones. According to the main
+            function of the Center of Gravity indicator explained by John Ehlers, traders
+            who use it will be able to closely speculate the upcoming price change of the asset.
+        """,
+    )
+
+    parser.add_argument(
+        "-l",
+        "--length",
+        action="store",
+        dest="n_length",
+        type=check_positive,
+        default=14,
+        help="length",
+    )
+    parser.add_argument(
+        "-o",
+        "--offset",
+        action="store",
+        dest="n_offset",
+        type=check_positive,
+        default=0,
+        help="offset",
+    )
+
+    try:
+        ns_parser = parse_known_args_and_warn(parser, other_args)
+        if not ns_parser:
+            return
+
+        # Daily
+        if s_interval == "1440min":
+            df_ta = ta.cg(
+                close=df_stock["Adj Close"],
+                length=ns_parser.n_length,
+                offset=ns_parser.n_offset,
+            ).dropna()
+
+        # Intraday
+        else:
+            df_ta = ta.cg(
+                close=df_stock["Close"],
+                length=ns_parser.n_length,
+                offset=ns_parser.n_offset,
+            ).dropna()
+
+        fig, axes = plt.subplots(2, 1, figsize=plot_autoscale(), dpi=PLOT_DPI)
+        ax = axes[0]
+        ax.set_title(f"{s_ticker} Centre of Gravity")
+        if s_interval == "1440min":
+            ax.plot(df_stock.index, df_stock["Adj Close"].values, "k", lw=1)
+        else:
+            ax.plot(df_stock.index, df_stock["Close"].values, "k", lw=1)
+        ax.set_xlim(df_stock.index[0], df_stock.index[-1])
+        ax.set_ylabel("Share Price ($)")
+        ax.grid(b=True, which="major", color="#666666", linestyle="-")
+        ax.minorticks_on()
+        ax.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
+        ax2 = axes[1]
+        ax2.plot(df_ta.index, df_ta.values, "b", lw=2, label="CG")
+        # shift cg 1 bar forward for signal
+        signal = df_ta.values
+        signal = np.roll(signal, 1)
+        ax2.plot(df_ta.index, signal, "g", lw=1, label="Signal")
+
+        ax2.set_xlim(df_stock.index[0], df_stock.index[-1])
+        ax2.grid(b=True, which="major", color="#666666", linestyle="-")
+        ax2.minorticks_on()
+        ax2.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
+
+        if gtff.USE_ION:
+            plt.ion()
+
+        plt.gcf().autofmt_xdate()
+        fig.tight_layout(pad=1)
+
+        plt.legend()
+        plt.show()
+
+        print("")
+
+    except Exception as e:
+        print(e, "\n")

gamestonk_terminal/common/technical_analysis/overlap.py

@@ -314,3 +314,121 @@ def vwap(other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.Dat
 
     except Exception as e:
         print(e, "\n")
+
+
+def zlma(other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame):
+    """Plots zero lag moving average (ZLMA)
+
+    Parameters
+    ----------
+    other_args: List[str]
+        Argparse arguments
+    s_ticker: str
+        Ticker
+    s_interval: str
+        Data interval
+    df_stock: pd.DataFrame
+        Dataframe of dates and prices
+    """
+
+    parser = argparse.ArgumentParser(
+        add_help=False,
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+        prog="zlma",
+        description="""
+            The zero lag exponential moving average (ZLEMA) indicator
+            was created by John Ehlers and Ric Way. The idea is do a
+            regular exponential moving average (EMA) calculation but
+            on a de-lagged data instead of doing it on the regular data.
+            Data is de-lagged by removing the data from "lag" days ago
+            thus removing (or attempting to) the cumulative effect of
+            the moving average.
+        """,
+    )
+
+    parser.add_argument(
+        "-l",
+        "--length",
+        action="store",
+        dest="n_length",
+        type=check_positive,
+        default=10,
+        help="length",
+    )
+    parser.add_argument(
+        "-o",
+        "--offset",
+        action="store",
+        dest="n_offset",
+        type=check_positive,
+        default=0,
+        help="offset",
+    )
+
+    try:
+        ns_parser = parse_known_args_and_warn(parser, other_args)
+        if not ns_parser:
+            return
+
+        # Daily
+        if s_interval == "1440min":
+            df_ta = ta.zlma(
+                df_stock["Adj Close"],
+                length=ns_parser.n_length,
+                offset=ns_parser.n_offset,
+            ).dropna()
+
+        # Intraday
+        else:
+            df_ta = ta.zlma(
+                df_stock["Close"],
+                length=ns_parser.n_length,
+                offset=ns_parser.n_offset,
+            ).dropna()
+
+        fig, _ = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
+        plt.title(f"{ns_parser.n_length} Day ZLMA on {s_ticker}")
+
+        if s_interval == "1440min":
+            plt.plot(
+                df_stock["Adj Close"].index,
+                df_stock["Adj Close"].values,
+                "fuchsia",
+                lw=1,
+            )
+            plt.xlim(
+                df_stock["Adj Close"].index[0],
+                df_stock["Adj Close"].index[-1],
+            )
+        else:
+            plt.plot(df_stock["Close"].index, df_stock["Close"].values, "k", lw=1)
+            plt.xlim(df_stock["Close"].index[0], df_stock["Close"].index[-1])
+
+        plt.xlabel("Time")
+        plt.ylabel(f"{s_ticker} Price($)")
+        plt.plot(df_ta.index, df_ta.values, c="tab:blue")
+        l_legend = list()
+        l_legend.append(s_ticker)
+        # Pandas series
+        if len(df_ta.shape) == 1:
+            l_legend.append(f"ZLMA {ns_parser.n_length}")
+        # Pandas dataframe
+        else:
+            l_legend.append(df_ta.columns.tolist())
+
+        plt.legend(l_legend)
+        plt.grid(b=True, which="major", color="#666666", linestyle="-")
+        plt.minorticks_on()
+        plt.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
+
+        if gtff.USE_ION:
+            plt.ion()
+
+        plt.gcf().autofmt_xdate()
+        fig.tight_layout(pad=1)
+
+        plt.show()
+        print("")
+
+    except Exception as e:
+        print(e, "\n")

gamestonk_terminal/stocks/technical_analysis/ta_controller.py

@@ -45,6 +45,7 @@ class TechnicalAnalysisController:
         "ema",
         "sma",
         "vwap",
+        "zlma",
         "cci",
         "macd",
         "rsi",
@@ -56,6 +57,7 @@ class TechnicalAnalysisController:
         "ad",
         "obv",
         "fib",
+        "cg",
     ]
 
     def __init__(
@@ -109,12 +111,14 @@ class TechnicalAnalysisController:
         print("   ema         exponential moving average")
         print("   sma         simple moving average")
         print("   vwap        volume weighted average price")
+        print("   zlma        zero lag moving average")
         print("momentum:")
         print("   cci         commodity channel index")
         print("   macd        moving average convergence/divergence")
         print("   rsi         relative strength index")
         print("   stoch       stochastic oscillator")
         print("   fisher      fisher transform")
+        print("   cg          centre of gravity")
         print("trend:")
         print("   adx         average directional movement index")
         print("   aroon       aroon indicator")
@@ -201,6 +205,10 @@ class TechnicalAnalysisController:
         """Process vwap command"""
         overlap.vwap(other_args, self.ticker, self.interval, self.stock)
 
+    def call_zlma(self, other_args: List[str]):
+        """Process zlma command"""
+        overlap.zlma(other_args, self.ticker, self.interval, self.stock)
+
     def call_cci(self, other_args: List[str]):
         """Process cci command"""
         momentum.cci(other_args, self.ticker, self.interval, self.stock)
@@ -221,6 +229,10 @@ class TechnicalAnalysisController:
         """Process fisher command"""
         momentum.fisher(other_args, self.ticker, self.interval, self.stock)
 
+    def call_cg(self, other_args: List[str]):
+        """Process cg command"""
+        momentum.cg(other_args, self.ticker, self.interval, self.stock)
+
     def call_adx(self, other_args: List[str]):
         """Process adx command"""
         trend_indicators.adx(other_args, self.ticker, self.interval, self.stock)