add Chaikin Osc (A/D Osc) to ta menu (#692)

* add yahoofinance undervalued & most shorted screeners

* .

* add chaikin osc to ta menu

* follow up

* .

gamestonk_terminal/common/technical_analysis/volume_model.py

@@ -39,6 +39,43 @@ def ad(df_stock: pd.DataFrame, use_open: bool) -> pd.DataFrame:
     return pd.DataFrame(df_ta)
 
 
+def adosc(df_stock: pd.DataFrame, use_open: bool, fast: int, slow: int) -> pd.DataFrame:
+    """Calculate AD oscillator technical indicator
+
+    Parameters
+    ----------
+    df_stock : pd.DataFrame
+        Dataframe of prices
+    use_open : bool
+        Whether to use open prices
+
+    Returns
+    -------
+    pd.DataFrame
+        Dataframe with technical indicator
+    """
+    if use_open:
+        df_ta = ta.adosc(
+            high=df_stock["High"],
+            low=df_stock["Low"],
+            close=df_stock["Close"],
+            volume=df_stock["Volume"],
+            open_=df_stock["Open"],
+            fast=fast,
+            slow=slow,
+        ).dropna()
+    else:
+        df_ta = ta.adosc(
+            high=df_stock["High"],
+            low=df_stock["Low"],
+            close=df_stock["Close"],
+            volume=df_stock["Volume"],
+            fast=fast,
+            slow=slow,
+        ).dropna()
+    return pd.DataFrame(df_ta)
+
+
 def obv(s_interval: str, df_stock: pd.DataFrame) -> pd.DataFrame:
     """On Balance Volume
 

gamestonk_terminal/common/technical_analysis/volume_view.py

@@ -3,11 +3,9 @@ __docformat__ = "numpy"
 
 import os
 from datetime import timedelta
-
 import matplotlib.pyplot as plt
 import pandas as pd
 from pandas.plotting import register_matplotlib_converters
-
 from gamestonk_terminal import feature_flags as gtff
 from gamestonk_terminal.common.technical_analysis import volume_model
 from gamestonk_terminal.config_plot import PLOT_DPI
@@ -105,6 +103,109 @@ def plot_ad(
     )
 
 
+def plot_adosc(
+    s_ticker: str,
+    s_interval: str,
+    df_stock: pd.DataFrame,
+    fast: int,
+    slow: int,
+    use_open: bool,
+    export: str,
+) -> pd.DataFrame:
+    """Display AD Osc Indicator
+
+    Parameters
+    ----------
+    s_ticker : str
+        Stock ticker
+    s_interval : str
+        Interval of stock data
+    df_stock : pd.DataFrame
+        Dataframe of prices
+    use_open : bool
+        Whether to use open prices in calculation
+    fast: int
+         Length of fast window
+    slow : int
+        Length of slow window
+    export : str
+        Format to export data
+    """
+    bar_colors = ["r" if x[1].Open < x[1].Close else "g" for x in df_stock.iterrows()]
+
+    if s_interval == "1440min":
+        bar_width = timedelta(days=1)
+    else:
+        bar_width = timedelta(minutes=int(s_interval.split("m")[0]))
+
+    divisor = 1000000
+    df_vol = df_stock["Volume"].dropna()
+    df_vol = df_vol.values / divisor
+    df_ta = volume_model.adosc(df_stock, use_open, fast, slow)
+    df_cal = df_ta.values
+    df_cal = df_cal / divisor
+
+    fig, axes = plt.subplots(
+        3,
+        1,
+        figsize=plot_autoscale(),
+        dpi=PLOT_DPI,
+    )
+    ax = axes[0]
+    ax.set_title(f"{s_ticker} AD Oscillator")
+    if s_interval == "1440min":
+        ax.plot(df_stock.index, df_stock["Adj Close"].values, "fuchsia", lw=1)
+    else:
+        ax.plot(df_stock.index, df_stock["Close"].values, "fuchsia", lw=1)
+    ax.set_xlim(df_stock.index[0], df_stock.index[-1])
+    ax.set_ylabel("Price")
+    ax.grid(b=True, which="major", color="#666666", linestyle="-")
+
+    ax1 = axes[1]
+    ax1.set_ylabel("Volume [M]")
+
+    if s_interval == "1440min":
+        ax1.bar(
+            df_stock.index,
+            df_vol,
+            color=bar_colors,
+            alpha=0.8,
+            width=0.3,
+        )
+    else:
+        ax1.bar(
+            df_stock.index,
+            df_vol,
+            color=bar_colors,
+            alpha=0.8,
+            width=bar_width,
+        )
+    ax1.set_xlim(df_stock.index[0], df_stock.index[-1])
+
+    ax2 = axes[2]
+    ax2.set_ylabel("AD Osc [M]")
+
+    ax2.plot(df_ta.index, df_cal, "b", lw=2, label="AD Osc")
+    ax2.set_xlim(df_stock.index[0], df_stock.index[-1])
+    ax2.grid(b=True, which="major", color="#666666", linestyle="-")
+
+    if gtff.USE_ION:
+        plt.ion()
+
+    plt.gcf().autofmt_xdate()
+    fig.tight_layout(pad=1)
+    plt.legend()
+    plt.show()
+    print("")
+
+    export_data(
+        export,
+        os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
+        "adosc",
+        df_ta,
+    )
+
+
 def plot_obv(s_ticker: str, s_interval: str, df_stock: pd.DataFrame, export: str):
     """Plot OBV tecnhical inticator
 

gamestonk_terminal/stocks/technical_analysis/ta_controller.py

@@ -63,6 +63,7 @@ class TechnicalAnalysisController:
         "bbands",
         "donchian",
         "ad",
+        "adosc",
         "obv",
         "fib",
     ]
@@ -131,7 +132,8 @@ Volatility:
     bbands      bollinger bands
     donchian    donchian channels
 Volume:
-    ad          accumulation/distribution line values
+    ad          accumulation/distribution line
+    adosc       chaikin oscillator
     obv         on balance volume
 Custom:
     fib         fibonacci retracement
@@ -1326,6 +1328,73 @@ Custom:
         except Exception as e:
             print(e, "\n")
 
+    def call_adosc(self, other_args: List[str]):
+        """Process adosc command"""
+        parser = argparse.ArgumentParser(
+            add_help=False,
+            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+            prog="adosc",
+            description="""
+                 Acummulation/Distribution Oscillator, also known as the Chaikin Oscillator
+                 is essentially a momentum indicator, but of the Accumulation-Distribution line
+                 rather than merely price. It looks at both the strength of price moves and the
+                 underlying buying and selling pressure during a given time period. The oscillator
+                 reading above zero indicates net buying pressure, while one below zero registers
+                 net selling pressure. Divergence between the indicator and pure price moves are
+                 the most common signals from the indicator, and often flag market turning points.
+            """,
+        )
+        parser.add_argument(
+            "--open",
+            action="store_true",
+            default=False,
+            dest="b_use_open",
+            help="uses open value of stock",
+        )
+        parser.add_argument(
+            "-f",
+            "--fast_length",
+            action="store",
+            dest="n_length_fast",
+            type=check_positive,
+            default=3,
+            help="fast length",
+        )
+        parser.add_argument(
+            "-s",
+            "--slow_length",
+            action="store",
+            dest="n_length_slow",
+            type=check_positive,
+            default=10,
+            help="slow length",
+        )
+        parser.add_argument(
+            "--export",
+            choices=["csv", "json", "xlsx"],
+            default="",
+            type=str,
+            dest="export",
+            help="Export dataframe data to csv,json,xlsx file",
+        )
+
+        try:
+            ns_parser = parse_known_args_and_warn(parser, other_args)
+            if not ns_parser:
+                return
+
+            volume_view.plot_adosc(
+                s_ticker=self.ticker,
+                s_interval=self.interval,
+                df_stock=self.stock,
+                use_open=ns_parser.b_use_open,
+                fast=ns_parser.n_length_fast,
+                slow=ns_parser.n_length_slow,
+                export=ns_parser.export,
+            )
+        except Exception as e:
+            print(e, "\n")
+
     def call_obv(self, other_args: List[str]):
         """Process obv command"""
         parser = argparse.ArgumentParser(