add donchian channels to ta menu (#682)

* add donchian channels to ta menu

* .

* .

* .

* .

gamestonk_terminal/common/technical_analysis/volatility_model.py

@@ -7,7 +7,7 @@ import pandas_ta as ta
 
 def bbands(
     s_interval: str, df_stock: pd.DataFrame, length: int, n_std: float, mamode: str
-):
+) -> pd.DataFrame:
     """Calculate Bollinger Bands
 
     Parameters
@@ -47,3 +47,37 @@ def bbands(
         ).dropna()
 
     return df_ta
+
+
+def donchian(
+    df_stock: pd.DataFrame,
+    upper_length: int,
+    lower_length: int,
+) -> pd.DataFrame:
+    """Calculate Donchian Channels
+
+    Parameters
+    ----------
+    s_interval : str
+        Interval of stock data
+    df_stock : pd.DataFrame
+        DataFrame of stock data
+    upper_length : int
+        Length of window to calculate upper channel
+    lower_length : int
+        Length of window to calculate lower channel
+
+
+    Returns
+    -------
+    df_ta: pd.DataFrame
+        Dataframe of upper and lower channels
+    """
+    df_ta = ta.donchian(
+        high=df_stock["High"],
+        low=df_stock["Low"],
+        upper_length=upper_length,
+        lower_length=lower_length,
+    ).dropna()
+
+    return df_ta

gamestonk_terminal/common/technical_analysis/volatility_view.py

@@ -83,3 +83,72 @@ def view_bbands(
         "bbands",
         df_ta,
     )
+
+
+def view_donchian(
+    ticker: str,
+    s_interval: str,
+    df_stock: pd.DataFrame,
+    upper_length: int,
+    lower_length: int,
+    export: str,
+):
+    """Show donchian channels
+
+    Parameters
+    ----------
+    ticker : str
+        Ticker
+    s_interval : str
+        Interval of stock data
+    df_stock : pd.DataFrame
+        Dataframe of stock prices
+    upper_length : int
+        Length of window to calculate upper channel
+    lower_length : int
+        Length of window to calculate lower channel
+    export : str
+        Format of export file
+    """
+    df_ta = volatility_model.donchian(df_stock, upper_length, lower_length)
+
+    fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
+    if s_interval == "1440min":
+        ax.plot(df_stock.index, df_stock["Adj Close"].values, color="k", lw=3)
+    else:
+        ax.plot(df_stock.index, df_stock["Close"].values, color="k", lw=3)
+    ax.plot(df_ta.index, df_ta.iloc[:, 0].values, "b", lw=1.5, label="upper")
+    ax.plot(df_ta.index, df_ta.iloc[:, 1].values, "b", lw=1.5, ls="--")
+    ax.plot(df_ta.index, df_ta.iloc[:, 2].values, "b", lw=1.5, label="lower")
+    ax.set_title(f"{ticker} donchian")
+    ax.set_xlim(df_stock.index[0], df_stock.index[-1])
+    ax.set_xlabel("Time")
+    ax.set_ylabel("Price ($)")
+
+    ax.legend([ticker, df_ta.columns[0], df_ta.columns[1], df_ta.columns[2]])
+    ax.fill_between(
+        df_ta.index,
+        df_ta.iloc[:, 0].values,
+        df_ta.iloc[:, 2].values,
+        alpha=0.1,
+        color="b",
+    )
+    ax.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"),
+        "donchian",
+        df_ta,
+    )

gamestonk_terminal/stocks/technical_analysis/ta_controller.py

@@ -59,6 +59,7 @@ class TechnicalAnalysisController:
         "adx",
         "aroon",
         "bbands",
+        "donchian",
         "ad",
         "obv",
         "fib",
@@ -124,6 +125,7 @@ Trend:
     aroon       aroon indicator
 Volatility:
     bbands      bollinger bands
+    donchian    donchian channels
 Volume:
     ad          accumulation/distribution line values
     obv         on balance volume
@@ -1110,6 +1112,64 @@ Custom:
         except Exception as e:
             print(e, "\n")
 
+    def call_donchian(self, other_args: List[str]):
+        """Process donchian command"""
+        parser = argparse.ArgumentParser(
+            add_help=False,
+            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+            prog="donchian",
+            description="""
+                Donchian Channels are three lines generated by moving average
+                calculations that comprise an indicator formed by upper and lower
+                bands around a midrange or median band. The upper band marks the
+                highest price of a security over N periods while the lower band
+                marks the lowest price of a security over N periods. The area
+                between the upper and lower bands represents the Donchian Channel.
+                """,
+        )
+        parser.add_argument(
+            "-u",
+            "--length_upper",
+            action="store",
+            dest="n_length_upper",
+            type=check_positive,
+            default=20,
+            help="length",
+        )
+        parser.add_argument(
+            "-l",
+            "--length_lower",
+            action="store",
+            dest="n_length_lower",
+            type=check_positive,
+            default=20,
+            help="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
+            volatility_view.view_donchian(
+                ticker=self.ticker,
+                s_interval=self.interval,
+                df_stock=self.stock,
+                upper_length=ns_parser.n_length_upper,
+                lower_length=ns_parser.n_length_lower,
+                export=ns_parser.export,
+            )
+
+        except Exception as e:
+            print(e, "\n")
+
     def call_ad(self, other_args: List[str]):
         """Process ad command"""
         parser = argparse.ArgumentParser(