Fix stocks asthetic style (#1355)

* Fix Theo's comment * Fix tests failing * Fix tests * Make plot lines thinner * Make ticks thinner globally by default * Apply global style when any controller is initialized Co-authored-by: Theodore Aptekarev <aptekarev@gmail.com>
2026-10-27 12:40:35 +00:00 · 2022-02-14 23:11:05 +07:00
parent 6c9999a9c8
commit 53945ddbae
16 changed files with 197 additions and 96 deletions
--- a/gamestonk_terminal/parent_classes.py
+++ b/gamestonk_terminal/parent_classes.py
@@ -27,6 +27,7 @@ from gamestonk_terminal.helper_funcs import (
    parse_known_args_and_warn,
    valid_date_in_past,
 )
+from gamestonk_terminal.config_terminal import theme
 from gamestonk_terminal.rich_config import console
 from gamestonk_terminal.stocks import stocks_helper
 from gamestonk_terminal.cryptocurrency import cryptocurrency_helpers
@@ -94,6 +95,8 @@ class BaseController(metaclass=ABCMeta):
        )
        self.parser.add_argument("cmd", choices=self.controller_choices)

+        theme.applyMPLstyle()
+
    def check_path(self) -> None:
        path = self.PATH
        if path[0] != "/":
--- a/gamestonk_terminal/stocks/comparison_analysis/finbrain_view.py
+++ b/gamestonk_terminal/stocks/comparison_analysis/finbrain_view.py
@@ -64,8 +64,8 @@ def display_sentiment_compare(

        for idx, tick in enumerate(similar):
            offset = 2 * idx
-            ax.axhline(y=offset, color="grey", linestyle="--", lw=2)
-            ax.axhline(y=offset + 1, color="grey", linestyle="--", lw=1)
+            ax.axhline(y=offset, color="white", linestyle="--", lw=1)
+            ax.axhline(y=offset + 1, color="white", linestyle="--", lw=1)

            senValues = np.array(pd.to_numeric(df_sentiment[tick].values))
            senNone = np.array(0 * len(df_sentiment))
@@ -74,7 +74,6 @@ def display_sentiment_compare(
                pd.to_numeric(df_sentiment[tick].values) + offset,
                offset,
                where=(senValues < senNone),
-                alpha=0.60,
                color=theme.down_color,
                interpolate=True,
            )
@@ -84,15 +83,13 @@ def display_sentiment_compare(
                pd.to_numeric(df_sentiment[tick].values) + offset,
                offset,
                where=(senValues >= senNone),
-                alpha=0.60,
                color=theme.up_color,
                interpolate=True,
            )

        ax.set_ylabel("Sentiment")
-        ax.axhline(y=-1, color="grey", linestyle="--", lw=1)
-        ax.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
-        ax.minorticks_on()
+
+        ax.axhline(y=-1, color="white", linestyle="--", lw=1)
        ax.set_yticks(np.arange(len(similar)) * 2)
        ax.set_yticklabels(similar)
        ax.set_xlim(df_sentiment.index[0], df_sentiment.index[-1])
@@ -172,7 +169,9 @@ def display_sentiment_correlation(
            ax=ax,
        )
        similar_string = ",".join(similar)
-        ax.set_title(f"Correlation heatmap across {similar_string}")
+        ax.set_title(
+            f"Sentiment correlation heatmap across \n{similar_string}", fontsize=11
+        )

        if not external_axes:
            theme.visualize_output()
--- a/gamestonk_terminal/stocks/comparison_analysis/yahoo_finance_model.py
+++ b/gamestonk_terminal/stocks/comparison_analysis/yahoo_finance_model.py
@@ -3,7 +3,7 @@ __docformat__ = "numpy"

 import logging
 from datetime import datetime, timedelta
-from typing import List
+from typing import List, Optional

 import matplotlib.pyplot as plt
 import pandas as pd
@@ -11,10 +11,11 @@ import yfinance as yf
 from sklearn.manifold import TSNE
 from sklearn.preprocessing import normalize

-from gamestonk_terminal import feature_flags as gtff
+from gamestonk_terminal.config_terminal import theme
 from gamestonk_terminal.config_plot import PLOT_DPI
 from gamestonk_terminal.decorators import log_start_end
 from gamestonk_terminal.helper_funcs import plot_autoscale
+from gamestonk_terminal.rich_config import console

 logger = logging.getLogger(__name__)

@@ -84,7 +85,11 @@ def get_1y_sp500() -> pd.DataFrame:

@log_start_end(log=logger)
 def get_sp500_comps_tsne(
-    ticker: str, lr: int = 200, no_plot: bool = False, num_tickers: int = 10
+    ticker: str,
+    lr: int = 200,
+    no_plot: bool = False,
+    num_tickers: int = 10,
+    external_axes: Optional[List[plt.Axes]] = None,
 ) -> List[str]:
    """
    Runs TSNE on SP500 tickers (along with ticker if not in SP500).
@@ -125,20 +130,61 @@ def get_sp500_comps_tsne(
    xs = tsne_features[:, 0]
    ys = tsne_features[:, 1]
    if not no_plot:
-        fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
-        ax.scatter(xs, ys, alpha=0.5)
-        for x, y, company in zip(xs, ys, rets.index):
-            if company != ticker:
-                ax.annotate(company, (x, y), fontsize=9, alpha=0.75)
-            else:
-                ax.scatter(x, y, s=50, c="r")
-                ax.annotate(company, (x, y), fontsize=9, alpha=1)
-        fig.tight_layout()
-        if gtff.USE_ION:
-            plt.ion()
-        plt.show()
-    data = pd.DataFrame({"X": xs, "Y": ys}, index=rets.index)
-    x0, y0 = data.loc[ticker]
-    data["dist"] = (data.X - x0) ** 2 + (data.Y - y0) ** 2
-    data = data.sort_values(by="dist")
-    return data.iloc[1 : num_tickers + 1].index.to_list()
+        # This plot has 1 axis
+        if not external_axes:
+            _, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
+        else:
+            if len(external_axes) != 1:
+                console.print("[red]Expected list of one axis item./n[/red]")
+                return []
+            (ax,) = external_axes
+
+        data = pd.DataFrame({"X": xs, "Y": ys}, index=rets.index)
+        x0, y0 = data.loc[ticker]
+        data["dist"] = (data.X - x0) ** 2 + (data.Y - y0) ** 2
+        data = data.sort_values(by="dist")
+        ticker_df = data[data.index == ticker]
+
+        top_n = data.iloc[1 : (num_tickers + 1)]
+        top_n_name = top_n.index.to_list()
+
+        top_100 = data[(num_tickers + 1) : 101]
+
+        ax.scatter(
+            top_n.X,
+            top_n.Y,
+            alpha=0.8,
+            c=theme.up_color,
+            label=f"Top {num_tickers} closest tickers",
+        )
+        ax.scatter(
+            top_100.X, top_100.Y, alpha=0.5, c="grey", label="Top 100 closest tickers"
+        )
+
+        for x, y, company in zip(top_n.X, top_n.Y, top_n.index):
+            ax.annotate(company, (x, y), fontsize=9, alpha=0.9)
+
+        for x, y, company in zip(top_100.X, top_100.Y, top_100.index):
+            ax.annotate(company, (x, y), fontsize=9, alpha=0.75)
+
+        ax.scatter(
+            ticker_df.X,
+            ticker_df.Y,
+            s=50,
+            c=theme.down_color,
+        )
+        ax.annotate(ticker, (ticker_df.X, ticker_df.Y), fontsize=9, alpha=1)
+        ax.legend()
+
+        ax.set_title(
+            f"Top 100 closest stocks on S&P500 to {ticker} using TSNE algorithm",
+            fontsize=11,
+        )
+        ax.set_xlabel("Dimension 1")
+        ax.set_ylabel("Dimension 2")
+        theme.style_primary_axis(ax)
+
+        if not external_axes:
+            theme.visualize_output()
+
+    return top_n_name
--- a/gamestonk_terminal/stocks/comparison_analysis/yahoo_finance_view.py
+++ b/gamestonk_terminal/stocks/comparison_analysis/yahoo_finance_view.py
@@ -89,12 +89,14 @@ def display_historical(
            return
        (ax,) = external_axes

-    ax.plot(df_similar)
+    # breakpoint()
+    companies_names = df_similar.columns.to_list()
+    ax.plot(df_similar, label=companies_names)
    ax.set_title("Historical price of similar companies")
    ax.set_ylabel(f"{['','Normalized'][normalize]} Share Price {['($)',''][normalize]}")
    # ensures that the historical data starts from same datapoint
    ax.set_xlim([df_similar.index[0], df_similar.index[-1]])
-
+    ax.legend(loc="best")
    theme.style_primary_axis(ax)

    if not external_axes:
@@ -139,13 +141,15 @@ def display_volume(
        (ax,) = external_axes

    df_similar = df_similar.div(1_000_000)
+    companies_names = df_similar.columns.to_list()

-    ax.plot(df_similar)
+    ax.plot(df_similar, label=companies_names)
    ax.set_title("Historical volume of similar companies")
    ax.set_ylabel("Volume [M]")
    # ensures that the historical data starts from same datapoint
    ax.set_xlim([df_similar.index[0], df_similar.index[-1]])

+    ax.legend()
    theme.style_primary_axis(ax)

    if not external_axes:
--- a/gamestonk_terminal/stocks/dark_pool_shorts/finra_view.py
+++ b/gamestonk_terminal/stocks/dark_pool_shorts/finra_view.py
@@ -108,6 +108,7 @@ def plot_dark_pools(
    ax2.set_ylabel("Shares per Trade")
    ax2.xaxis.set_major_formatter(mdates.DateFormatter("%m/%d"))
    ax2.xaxis.set_major_locator(mdates.DayLocator(interval=4))
+    ax2.set_xlim(otc.index[0], otc.index[-1])
    ax2.set_xlabel("Weeks")

    theme.style_primary_axis(ax1)
@@ -191,6 +192,8 @@ def plot_dark_pools_ats(
    ax.set_ylabel("Total Weekly Shares [Million]")
    ax.set_title("Dark Pool (ATS) growing tickers")
    ax.set_xlabel("Weeks")
+    ats["weekStartDate"] = pd.to_datetime(ats["weekStartDate"])
+    ax.set_xlim(ats["weekStartDate"].iloc[0], ats["weekStartDate"].iloc[-1])
    theme.style_primary_axis(ax)

    if not external_axes:
--- a/gamestonk_terminal/stocks/dark_pool_shorts/nyse_view.py
+++ b/gamestonk_terminal/stocks/dark_pool_shorts/nyse_view.py
@@ -86,6 +86,11 @@ def display_short_by_exchange(
        # remove the scientific notion on the left hand side
        ax.ticklabel_format(style="plain", axis="y")

+        ax.set_xlim(
+            volume_by_exchange.Date.iloc[0],
+            volume_by_exchange.Date.iloc[-1],
+        )
+
        ax.set_title(f"Net Short Volume for {ticker}")
        theme.style_primary_axis(ax)

--- a/gamestonk_terminal/stocks/dark_pool_shorts/sec_view.py
+++ b/gamestonk_terminal/stocks/dark_pool_shorts/sec_view.py
@@ -71,9 +71,11 @@ def fails_to_deliver(
    ax1.bar(
        ftds_data["SETTLEMENT DATE"],
        ftds_data["QUANTITY (FAILS)"] / 1000,
+        label="Fail Quantity",
    )
    ax1.set_ylabel("Shares [K]")
    ax1.set_title(f"Fails-to-deliver Data for {ticker}")
+    ax1.legend(loc="lower right")

    if num > 0:
        stock_ftd = stock[stock.index > (datetime.now() - timedelta(days=num + 31))]
@@ -81,8 +83,11 @@ def fails_to_deliver(
        stock_ftd = stock[stock.index > start]
        stock_ftd = stock_ftd[stock_ftd.index < end]

-    ax2.plot(stock_ftd.index, stock_ftd["Adj Close"], color="tab:orange")
+    ax2.plot(
+        stock_ftd.index, stock_ftd["Adj Close"], color="orange", label="Share Price"
+    )
    ax2.set_ylabel("Share Price [$]")
+    ax2.legend(loc="upper right")

    theme.style_twin_axes(ax1, ax2)

--- a/gamestonk_terminal/stocks/dark_pool_shorts/stockgrid_view.py
+++ b/gamestonk_terminal/stocks/dark_pool_shorts/stockgrid_view.py
@@ -197,7 +197,6 @@ def short_interest_volume(
            df["total_volume"] / 1_000_000,
            width=timedelta(days=1),
            color=theme.up_color,
-            alpha=0.4,
            label="Total Volume",
        )
        ax.bar(
@@ -205,7 +204,6 @@ def short_interest_volume(
            df["short_volume"] / 1_000_000,
            width=timedelta(days=1),
            color=theme.down_color,
-            alpha=0.4,
            label="Short Volume",
        )

@@ -327,7 +325,6 @@ def net_short_position(
            df["dates"],
            df["dollar_net_volume"] / 1_000,
            color=theme.down_color,
-            alpha=0.4,
            label="Net Short Vol. (1k $)",
        )
        ax1.set_ylabel("Net Short Vol. (1k $)")
--- a/gamestonk_terminal/stocks/due_diligence/business_insider_view.py
+++ b/gamestonk_terminal/stocks/due_diligence/business_insider_view.py
@@ -85,10 +85,12 @@ def price_target_from_analysts(
            df_analyst_data = df_analyst_data[start:]  # type: ignore

        if interval == "1440min":
-            ax.plot(stock.index, stock["Adj Close"].values, lw=3)
+            ax.plot(stock.index, stock["Adj Close"].values)
+            legend_price_label = "Adjusted closing price"
        # Intraday
        else:
-            ax.plot(stock.index, stock["Close"].values, lw=3)
+            ax.plot(stock.index, stock["Close"].values)
+            legend_price_label = "Closing price"

        if start:
            ax.plot(df_analyst_data.groupby(by=["Date"]).mean()[start:])  # type: ignore
@@ -96,10 +98,14 @@ def price_target_from_analysts(
            ax.plot(df_analyst_data.groupby(by=["Date"]).mean())

        ax.scatter(
-            df_analyst_data.index, df_analyst_data["Price Target"], c="red", s=40
+            df_analyst_data.index,
+            df_analyst_data["Price Target"],
+            color=theme.down_color,
+            edgecolors=theme.up_color,
+            zorder=2,
        )

-        ax.legend(["Closing Price", "Average Price Target", "Price Target"])
+        ax.legend([legend_price_label, "Average Price Target", "Price Target"])

        ax.set_title(f"{ticker} (Time Series) and Price Target")
        ax.set_xlim(stock.index[0], stock.index[-1])
--- a/gamestonk_terminal/stocks/fundamental_analysis/yahoo_finance_view.py
+++ b/gamestonk_terminal/stocks/fundamental_analysis/yahoo_finance_view.py
@@ -191,11 +191,12 @@ def display_dividends(
            mfc=theme.down_color,
            mec=theme.down_color,
            alpha=1,
+            label="Dividends Payout",
        )
        ax.set_ylabel("Amount ($)")
        ax.set_title(f"Dividend History for {ticker}")
        ax.set_xlim(div_history.index[-1], div_history.index[0])
-
+        ax.legend()
        theme.style_primary_axis(ax)

        if not external_axes:
--- a/gamestonk_terminal/stocks/government/quiverquant_view.py
+++ b/gamestonk_terminal/stocks/government/quiverquant_view.py
@@ -11,6 +11,7 @@ from datetime import datetime, timedelta
 import numpy as np
 import pandas as pd
 from matplotlib import pyplot as plt
+import matplotlib

 from gamestonk_terminal.config_terminal import theme
 from gamestonk_terminal.config_plot import PLOT_DPI
@@ -203,9 +204,10 @@ def display_government_buys(
            return
        (ax,) = external_axes

+    colors = theme.get_colors()
    df_gov.groupby("Ticker")["upper"].sum().div(1000).sort_values(ascending=False).head(
        n=num
-    ).plot(kind="bar", rot=0, ax=ax)
+    ).plot(kind="bar", rot=0, ax=ax, color=colors)

    ax.set_ylabel("Amount [1k $]")
    ax.set_title(
@@ -321,15 +323,15 @@ def display_government_sells(
            return
        (ax,) = external_axes

+    colors = theme.get_colors()
    df_gov.groupby("Ticker")["upper"].sum().div(1000).sort_values().abs().head(
        n=num
-    ).plot(kind="bar", rot=0, ax=ax)
+    ).plot(kind="bar", rot=0, ax=ax, color=colors)
    ax.set_ylabel("Amount ($1k)")
    ax.set_title(
        f"{num} most sold stocks over last {past_transactions_months} months"
        f" (upper bound) for {gov_type}"
    )
-    # plt.gcf().axes[0].yaxis.get_major_formatter().set_scientific(False)

    theme.style_primary_axis(ax)

@@ -608,6 +610,8 @@ def display_contracts(
        ax.set_ylabel("Amount ($1k)")
        ax.set_title(f"Sum of latest government contracts to {ticker}")

+        ax.xaxis.set_major_locator(matplotlib.ticker.MultipleLocator(4))
+
        theme.style_primary_axis(ax)

        if not external_axes:
@@ -775,7 +779,14 @@ def display_hist_contracts(
                return
            (ax,) = external_axes

-        ax.plot(np.arange(0, len(amounts)), amounts / 1000, "-*", lw=2, ms=15)
+        ax.plot(
+            np.arange(0, len(amounts)),
+            amounts / 1000,
+            marker=".",
+            markerfacecolor=theme.down_color,
+            lw=2,
+            ms=15,
+        )

        ax.set_xlim([-0.5, len(amounts) - 0.5])
        ax.set_xticks(np.arange(0, len(amounts)))
@@ -844,7 +855,8 @@ def display_top_lobbying(
                return
            (ax,) = external_axes

-        lobbying_by_ticker.head(num).plot(kind="bar", ax=ax)
+        colors = theme.get_colors()
+        lobbying_by_ticker.head(num).plot(kind="bar", ax=ax, color=colors)
        ax.set_xlabel("Ticker")
        ax.set_ylabel("Total Amount ($100k)")
        ax.set_title(f"Corporate Lobbying Spent since {df_lobbying['Date'].min()}")
--- a/gamestonk_terminal/stocks/insider/businessinsider_view.py
+++ b/gamestonk_terminal/stocks/insider/businessinsider_view.py
@@ -103,7 +103,7 @@ def insider_activity(
                * float(row["Shares Traded"].replace(",", "")),
                axis=1,
            )
-            ax.set_xlim(df_insider.index[0], stock.index[-1])
+            ax.set_xlim(right=stock.index[-1])
            min_price, max_price = ax.get_ylim()

            price_range = max_price - min_price
--- a/gamestonk_terminal/stocks/sector_industry_analysis/financedatabase_view.py
+++ b/gamestonk_terminal/stocks/sector_industry_analysis/financedatabase_view.py
@@ -296,8 +296,8 @@ def display_companies_per_sector_in_country(
    df["Number of companies"] = df["Number of companies"].astype(int)

    title = mktcap + " cap companies " if mktcap else "Companies "
-    title += f"in {country}"
-    title += " excl. exchanges" if exclude_exchanges else " incl. exchanges"
+    title += f"in {country}\n"
+    title += "excl. exchanges" if exclude_exchanges else " incl. exchanges"

    if raw:
        print_rich_table(df, headers=list(df.columns), show_index=True, title=title)
@@ -357,7 +357,7 @@ def display_companies_per_sector_in_country(
                colors=colors,
                wedgeprops={"linewidth": 0.5, "edgecolor": "white"},
                labeldistance=1.05,
-                startangle=90,
+                startangle=45,
            )
            ax.set_title(title, fontsize=14)

@@ -439,8 +439,8 @@ def display_companies_per_industry_in_country(
    df["Number of companies"] = df["Number of companies"].astype(int)

    title = mktcap + " cap companies " if mktcap else "Companies "
-    title += f"in {country}"
-    title += " excl. exchanges" if exclude_exchanges else " incl. exchanges"
+    title += f"in {country}\n"
+    title += "excl. exchanges" if exclude_exchanges else " incl. exchanges"

    if raw:
        print_rich_table(df, headers=list(df.columns), show_index=True, title=title)
@@ -507,7 +507,7 @@ def display_companies_per_industry_in_country(
                colors=colors,
                wedgeprops={"linewidth": 0.5, "edgecolor": "white"},
                labeldistance=1.05,
-                startangle=90,
+                startangle=45,
            )

            ax.set_title(title, fontsize=14)
@@ -587,8 +587,8 @@ def display_companies_per_industry_in_sector(
    df["Number of companies"] = df["Number of companies"].astype(int)

    title = mktcap + " cap companies " if mktcap else "Companies "
-    title += f"in {sector} sector"
-    title += " excl. exchanges" if exclude_exchanges else " incl. exchanges"
+    title += f"in {sector} sector\n"
+    title += "excl. exchanges" if exclude_exchanges else " incl. exchanges"

    if raw:
        print_rich_table(
@@ -660,7 +660,7 @@ def display_companies_per_industry_in_sector(
                colors=colors,
                wedgeprops={"linewidth": 0.5, "edgecolor": "white"},
                labeldistance=1.05,
-                startangle=90,
+                startangle=45,
            )
            ax.set_title(title, fontsize=14)

@@ -739,8 +739,8 @@ def display_companies_per_country_in_sector(
    df["Number of companies"] = df["Number of companies"].astype(int)

    title = mktcap + " cap companies " if mktcap else "Companies "
-    title += f"in {sector} sector"
-    title += " excl. exchanges" if exclude_exchanges else " incl. exchanges"
+    title += f"in {sector} sector\n"
+    title += "excl. exchanges" if exclude_exchanges else " incl. exchanges"

    if raw:
        print_rich_table(df, headers=list(df.columns), show_index=True, title=title)
@@ -805,7 +805,7 @@ def display_companies_per_country_in_sector(
                colors=colors,
                wedgeprops={"linewidth": 0.5, "edgecolor": "white"},
                labeldistance=1.05,
-                startangle=90,
+                startangle=45,
            )
            ax.set_title(title, fontsize=14)

@@ -884,8 +884,8 @@ def display_companies_per_country_in_industry(
    df["Number of companies"] = df["Number of companies"].astype(int)

    title = mktcap + " cap companies " if mktcap else "Companies "
-    title += f"per country in {industry} industry"
-    title += " excl. exchanges" if exclude_exchanges else " incl. exchanges"
+    title += f"per country in {industry} industry\n"
+    title += "excl. exchanges" if exclude_exchanges else " incl. exchanges"

    if raw:
        print_rich_table(df, headers=list(df.columns), show_index=True, title=title)
@@ -950,7 +950,7 @@ def display_companies_per_country_in_industry(
                colors=colors,
                wedgeprops={"linewidth": 0.5, "edgecolor": "white"},
                labeldistance=1.05,
-                startangle=90,
+                startangle=45,
            )
            ax.set_title(title, fontsize=14)

--- a/gamestonk_terminal/stocks/stocks_helper.py
+++ b/gamestonk_terminal/stocks/stocks_helper.py
@@ -21,8 +21,8 @@ from numpy.core.fromnumeric import transpose
 from plotly.subplots import make_subplots
 from scipy import stats

+from gamestonk_terminal.config_terminal import theme
 from gamestonk_terminal import config_terminal as cfg
-from gamestonk_terminal import feature_flags as gtff
 from gamestonk_terminal.helper_funcs import (
    parse_known_args_and_warn,
    plot_autoscale,
@@ -269,6 +269,7 @@ def display_candle(
    add_trend: bool = False,
    ma: Optional[Tuple[int, ...]] = None,
    asset_type: str = "Stock",
+    external_axes: Optional[List[plt.Axes]] = None,
 ):
    """Shows candle plot of loaded ticker. [Source: Yahoo Finance, IEX Cloud or Alpha Vantage]

@@ -288,6 +289,10 @@ def display_candle(
        Moving averages to add to the candle
    asset_type_: str
        String to include in title
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (2 axes are expected in the list), by default None
+    asset_type_: str
+        String to include in title
    """
    if add_trend:
        if (df_stock.index[1] - df_stock.index[0]).total_seconds() >= 86400:
@@ -299,45 +304,54 @@ def display_candle(
        if add_trend:
            if "OC_High_trend" in df_stock.columns:
                ap0.append(
-                    mpf.make_addplot(df_stock["OC_High_trend"], color="g"),
+                    mpf.make_addplot(df_stock["OC_High_trend"], color=theme.up_color),
                )

            if "OC_Low_trend" in df_stock.columns:
                ap0.append(
-                    mpf.make_addplot(df_stock["OC_Low_trend"], color="b"),
+                    mpf.make_addplot(df_stock["OC_Low_trend"], color=theme.down_color),
                )

-        if gtff.USE_ION:
-            plt.ion()
+        candle_chart_kwargs = {
+            "type": "candle",
+            "style": theme.mpf_style,
+            "volume": True,
+            "addplot": ap0,
+            "xrotation": theme.xticks_rotation,
+            "scale_padding": {"left": 0.3, "right": 1, "top": 0.8, "bottom": 0.8},
+            "update_width_config": {
+                "candle_linewidth": 0.6,
+                "candle_width": 0.8,
+                "volume_linewidth": 0.8,
+                "volume_width": 0.8,
+            },
+            "warn_too_much_data": 10000,
+        }
+
        kwargs = {"mav": ma} if ma else {}

-        fig, _ = mpf.plot(
-            df_stock,
-            type="candle",
-            style=cfg.theme.mpf_style,
-            volume=True,
-            addplot=ap0,
-            xrotation=10,
-            figratio=(10, 7),
-            figscale=1.10,
-            scale_padding={"left": 0.3, "right": 1.1, "top": 0.8, "bottom": 0.8},
-            figsize=(plot_autoscale()),
-            update_width_config=dict(
-                candle_linewidth=0.6,
-                candle_width=0.8,
-                volume_linewidth=0.8,
-                volume_width=0.8,
-            ),
-            warn_too_much_data=10000,
-            returnfig=True,
-            **kwargs,
-        )
-        fig.suptitle(
-            f"{asset_type} {s_ticker}",
-            x=0.055,
-            y=0.965,
-            horizontalalignment="left",
-        )
+        if external_axes is None:
+            candle_chart_kwargs["returnfig"] = True
+            candle_chart_kwargs["figratio"] = (10, 7)
+            candle_chart_kwargs["figscale"] = 1.10
+            candle_chart_kwargs["figsize"] = plot_autoscale()
+            fig, _ = mpf.plot(df_stock, **candle_chart_kwargs, **kwargs)
+            fig.suptitle(
+                f"{asset_type} {s_ticker}",
+                x=0.055,
+                y=0.965,
+                horizontalalignment="left",
+            )
+
+            theme.visualize_output(force_tight_layout=False)
+        else:
+            if len(external_axes) != 1:
+                console.print("[red]Expected list of 1 axis items./n[/red]")
+                return
+            (ax1,) = external_axes
+            candle_chart_kwargs["ax"] = ax1
+            mpf.plot(df_stock, **candle_chart_kwargs)
+
    else:
        fig = make_subplots(
            rows=2,
--- a/styles/default/dark.mplstyle
+++ b/styles/default/dark.mplstyle
@@ -54,6 +54,7 @@ xtick.major.size: 2
 xtick.minor.size: 1
 xtick.labelsize: small
 xtick.alignment: center
+xtick.major.width: 0.2

 ytick.major.size: 2
 ytick.minor.size: 1
@@ -63,6 +64,7 @@ ytick.left: False
 ytick.labelleft: False
 ytick.right: True
 ytick.labelright: True
+ytick.major.width: 0.2

 grid.color: F5EFF3
 grid.linewidth: 0.4
--- a/tests/gamestonk_terminal/stocks/test_stocks_helper.py
+++ b/tests/gamestonk_terminal/stocks/test_stocks_helper.py
@@ -6,6 +6,7 @@ import pytest

 # IMPORTATION INTERNAL
 from gamestonk_terminal.stocks import stocks_helper
+from gamestonk_terminal import helper_funcs


@pytest.fixture(scope="module")
@@ -35,7 +36,7 @@ def test_quote():
 )
 def test_search(mocker, use_tab):
    mocker.patch.object(
-        target=stocks_helper.gtff, attribute="USE_TABULATE_DF", new=use_tab
+        target=helper_funcs.gtff, attribute="USE_TABULATE_DF", new=use_tab
    )
    stocks_helper.search(query="pharma", amount=5)

@@ -73,8 +74,11 @@ def test_load(interval, recorder, source):
    [True, False],
 )
 def test_display_candle(mocker, use_matplotlib):
-    mocker.patch.object(target=stocks_helper.gtff, attribute="USE_ION", new=False)
-    mocker.patch("matplotlib.pyplot.show")
+    # MOCK VISUALIZE_OUTPUT
+    mocker.patch(
+        target="gamestonk_terminal.helper_classes.TerminalStyle.visualize_output"
+    )
+
    mocker.patch("plotly.basedatatypes.BaseFigure.show")

    # LOAD DATA