Merge pull request #14 from Cuttlas90/ta

Ta

main.py

@@ -19,7 +19,7 @@
                     page_title="وسهم",
                     page_icon="./assets/favicon.ico",
                     initial_sidebar_state='expanded')
-st.session_state.ver = '0.1.8'
+st.session_state.ver = '0.1.9'
 
 STREAMLIT_STATIC_PATH = Path(st.__path__[0]) / "static/static"
 CSS_PATH = STREAMLIT_STATIC_PATH / "media/"

menu.py

@@ -9,5 +9,7 @@ def add_menu():
     st.sidebar.page_link("pages/portfolio.py", label="تحلیل پورتفو", icon="📊")
     st.sidebar.page_link("pages/monte_carlo.py", label="مونته کارلو", icon="🧮")
     st.sidebar.page_link("pages/leveraged_funds.py", label="صندوقهای اهرمی", icon="🧮")
+    st.sidebar.page_link("pages/technical.py", label="اطلاعات تکنیکال", icon="📈")
+    st.sidebar.page_link("pages/social_page.py", label="اطلاعات رفتاری", icon="🌐")
     # st.sidebar.page_link("pages/simple_chart.py", label="نمودار ساده ماهانه", icon="📋")
     st.sidebar.page_link("pages/changelog.py", label="تازه ها", icon="💬")

pages/changelog.py

@@ -14,6 +14,11 @@
 if "ver" in st.session_state:
     st.sidebar.header(f'Vasahm DashBoard `{st.session_state.ver}`')
 
+st.subheader('changelog: `version 0.1.9`', divider='rainbow')
+st.markdown('''
+            * افزودن صفحه اظلاعات تکنیکال
+            * افزودن صفحه اطلاعات رفتاری
+            * رفع باگهای کوچک''', unsafe_allow_html=False, help=None)
 st.subheader('changelog: `version 0.1.8`', divider='rainbow')
 st.markdown('''
             * افزودن صفحه بررسی و مقایسه صندوقهای اهرمی

pages/helper/gauge_chart.py

@@ -0,0 +1,70 @@
+import streamlit as st
+import pandas as pd
+import altair as alt
+from login import check_local_token
+import numpy as np
+
+from pages.helper.query import Queries
+from request import get_stock_monthly
+# from slider import create_range_slider
+from menu import add_menu
+from finta import TA
+from request import vasahm_query
+import plotly.graph_objs as go
+import streamlit.components.v1 as components
+
+
+
+def plot_gauge(current_value, min_value, max_value, chart_title, place):
+    plot_bgcolor = "#def"
+    quadrant_colors = [plot_bgcolor, "#2bad4e", "#85e043", "#eff229", "#f2a529", "#f25829"] 
+    quadrant_text = ["", "<b>Strong Buy</b>", "<b>Buy</b>", "<b>Neutral</b>", "<b>Sell</b>", "<b>Strong Sell</b>"]
+    n_quadrants = len(quadrant_colors) - 1
+
+    hand_length = np.sqrt(2) / 4
+    hand_angle = np.pi * (1 - (max(min_value, min(max_value, current_value)) - min_value) / (max_value - min_value))
+
+    fig = go.Figure(
+        data=[
+            go.Pie(
+                values=[0.5] + (np.ones(n_quadrants) / 2 / n_quadrants).tolist(),
+                rotation=90,
+                hole=0.5,
+                marker_colors=quadrant_colors,
+                text=quadrant_text,
+                textinfo="text",
+                hoverinfo="skip",
+            ),
+        ],
+        layout=go.Layout(
+            showlegend=False,
+            margin=dict(b=0,t=10,l=10,r=10),
+            width=450,
+            height=450,
+            paper_bgcolor=plot_bgcolor,
+            annotations=[
+                go.layout.Annotation(
+                    text=f"<b>{chart_title}</b>",
+                    x=0.5, xanchor="center", xref="paper",
+                    y=0.25, yanchor="bottom", yref="paper",
+                    showarrow=False,
+                )
+            ],
+            shapes=[
+                go.layout.Shape(
+                    type="circle",
+                    x0=0.48, x1=0.52,
+                    y0=0.48, y1=0.52,
+                    fillcolor="#333",
+                    line_color="#333",
+                ),
+                go.layout.Shape(
+                    type="line",
+                    x0=0.5, x1=0.5 + hand_length * np.cos(hand_angle),
+                    y0=0.5, y1=0.5 + hand_length * np.sin(hand_angle),
+                    line=dict(color="#333", width=4)
+                )
+            ]
+        )
+    )
+    place.plotly_chart(fig)

pages/helper/query.py

@@ -24,6 +24,21 @@ def get_stock_data(self):
             """
         return string
 
+    def get_daily_social(self):
+        """return query to monthly sell value data"""
+        string = f"""select
+                        number,
+                        date
+                    from
+                        public.social_data
+                        INNER JOIN stocks ON public.social_data.stock_id = stocks.id
+                    where
+                        stocks.name = '{self.name}'
+                    LIMIT
+						30
+
+        """
+        return string
     def get_monthly_sell_value_data(self, dollar = False):
         """return query to monthly sell value data"""
         string = f"""select
@@ -767,6 +782,26 @@ def _dollar_query(self, text):
             INNER JOIN dollar ON ranked_dates.end_to_period::varchar = dollar.\"Jalali\"
         """
         return query_string
+
+    def price_query(self, dollar = False):
+        """get stock price"""
+        string = f"""select
+                        date.miladi AS date,
+                        \"openingPrice\" AS open,
+                        \"maxPrice\" AS high,
+                        \"minPrice\" AS low,
+                        \"lastPrice\" AS close,
+                        \"tradeVolume\" AS volume
+                    from
+                        public.stock_price
+                        INNER JOIN stocks ON public.stock_price.stock_id = stocks.id
+                        INNER JOIN date ON public.stock_price.\"tradeDate\" = date.jalali_3::TEXT
+                    where
+                        stocks.name = '{self.name}'
+        """
+        if dollar:
+            string = self._dollar_query(string)
+        return string
 
     QUERY_MONTHLY_COMPARE = """WITH
                         ranked_dates AS (

pages/helper/ta_metrics.py

@@ -0,0 +1,156 @@
+
+import streamlit as st
+import pandas as pd
+import altair as alt
+from login import check_local_token
+import numpy as np
+
+from pages.helper.gauge_chart import plot_gauge
+from pages.helper.query import Queries
+from request import get_stock_monthly
+# from slider import create_range_slider
+from menu import add_menu
+from finta import TA
+from request import vasahm_query
+import plotly.graph_objs as go
+import streamlit.components.v1 as components
+
+
+def _update_guage(a, b, c, condition):
+    if condition in ["EMA","SMA","VAMA", "HMA"]:
+        if b >= c:
+            a = a +1
+        else:
+            a=a-1
+        return a
+    elif condition in ["RSI","STOCH"]:
+        if b >= 70.0:
+            a = a +1
+        elif b <= 30.0:
+            a=a-1
+        return a
+    elif condition in ["ADX"]:
+        if b >= 50.0:
+            a = a +1
+        elif b <= 50.0:
+            a=a-1
+        return a
+    elif condition == "CCI":
+        if b >= 100.0:
+            a = a -1
+        elif b <= -100.0:
+            a=a+1
+        return a
+    elif condition in ["AO","MOM", "MACD"]:
+
+        if b >= 0.0:
+            a = a +1
+        elif b <= 0.0:
+            a=a-1
+        return a
+    elif condition in ["STOCHRSI"]:
+        if b >= 0.5:
+            a = a +1
+        elif b <= 0.5:
+            a=a-1
+        return a
+    elif condition in ["WILLIAMS"]:
+        if b >= -20.0:
+            a = a +1
+        elif b <= -80.0:
+            a=a-1
+        return a
+
+def add_ta_metrics(stock_data_history, data,metric_label, col, place, num, ind):
+    if ind == "EMA":
+        smas = TA.EMA(stock_data_history, data[0])
+        sma_df = pd.DataFrame(smas)
+        place.metric(label=metric_label, value = int(sma_df.iloc[-1][col]), delta = int(sma_df.iloc[-1][col]-sma_df.iloc[-2][col]))
+        num=_update_guage(num, sma_df.iloc[-1][col], stock_data_history.iloc[-1]['close'],ind)
+        return num
+    elif ind == "SMA":
+        smas = TA.SMA(stock_data_history, data[0])
+        sma_df = pd.DataFrame(smas)
+        place.metric(label=metric_label, value = int(sma_df.iloc[-1][col]), delta = int(sma_df.iloc[-1][col]-sma_df.iloc[-2][col]))
+        num=_update_guage(num, sma_df.iloc[-1][col], stock_data_history.iloc[-1]['close'], ind)
+        return num
+    elif ind == "VAMA":
+        smas = TA.VAMA(stock_data_history, data[0])
+        sma_df = pd.DataFrame(smas)
+        place.metric(label=metric_label, value = int(sma_df.iloc[-1][col]), delta = int(sma_df.iloc[-1][col]-sma_df.iloc[-2][col]))
+        num=_update_guage(num, sma_df.iloc[-1][col], stock_data_history.iloc[-1]['close'], ind)
+        return num
+    elif ind == "HMA":
+        smas = TA.HMA(stock_data_history, data[0])
+        sma_df = pd.DataFrame(smas)
+        place.metric(label=metric_label, value = int(sma_df.iloc[-1][col]), delta = int(sma_df.iloc[-1][col]-sma_df.iloc[-2][col]))
+        num=_update_guage(num, sma_df.iloc[-1][col], stock_data_history.iloc[-1]['close'], ind)
+        return num
+    elif ind == "RSI":
+        smas = TA.RSI(stock_data_history, data[0])
+        sma_df = pd.DataFrame(smas)
+        place.metric(label=metric_label, value = int(sma_df.iloc[-1][col]), delta = int(sma_df.iloc[-1][col]-sma_df.iloc[-2][col]))
+        num=_update_guage(num, sma_df.iloc[-1][col], stock_data_history.iloc[-1]['close'], ind)
+        return num
+    elif ind == "STOCH":
+        smas = TA.STOCH(stock_data_history, data[0])
+        sma_df = pd.DataFrame(smas)
+        place.metric(label=metric_label, value = int(sma_df.iloc[-1][col]), delta = int(sma_df.iloc[-1][col]-sma_df.iloc[-2][col]))
+        num=_update_guage(num, sma_df.iloc[-1][col], stock_data_history.iloc[-1]['close'], ind)
+        return num
+    elif ind == "CCI":
+        smas = TA.CCI(stock_data_history, data[0])
+        sma_df = pd.DataFrame(smas)
+        place.metric(label=metric_label, value = int(sma_df.iloc[-1][col]), delta = int(sma_df.iloc[-1][col]-sma_df.iloc[-2][col]))
+        num=_update_guage(num, sma_df.iloc[-1][col], stock_data_history.iloc[-1]['close'], ind)
+        return num
+    elif ind == "ADX":
+        smas = TA.ADX(stock_data_history, data[0])
+        sma_df = pd.DataFrame(smas)
+        place.metric(label=metric_label, value = int(sma_df.iloc[-1][col]), delta = int(sma_df.iloc[-1][col]-sma_df.iloc[-2][col]))
+        num=_update_guage(num, sma_df.iloc[-1][col], stock_data_history.iloc[-1]['close'], ind)
+        return num
+    elif ind == "AO":
+        smas = TA.AO(stock_data_history, data[0])
+        sma_df = pd.DataFrame(smas)
+        place.metric(label=metric_label, value = int(sma_df.iloc[-1][col]), delta = int(sma_df.iloc[-1][col]-sma_df.iloc[-2][col]))
+        num=_update_guage(num, sma_df.iloc[-1][col], stock_data_history.iloc[-1]['close'], ind)
+        return num
+    elif ind == "MOM":
+        smas = TA.MOM(stock_data_history, data[0])
+        sma_df = pd.DataFrame(smas)
+        place.metric(label=metric_label, value = int(sma_df.iloc[-1][col]), delta = int(sma_df.iloc[-1][col]-sma_df.iloc[-2][col]))
+        num=_update_guage(num, sma_df.iloc[-1][col], stock_data_history.iloc[-1]['close'], ind)
+        return num
+    elif ind == "MACD":
+        smas = TA.MACD(stock_data_history, data[0], data[1])
+        sma_df = pd.DataFrame(smas)
+        place.metric(label=metric_label, value = int(sma_df.iloc[-1][col]), delta = int(sma_df.iloc[-1][col]-sma_df.iloc[-2][col]))
+        num=_update_guage(num, sma_df.iloc[-1][col], stock_data_history.iloc[-1]['close'], ind)
+        return num
+    elif ind == "STOCHRSI":
+        smas = TA.STOCHRSI(stock_data_history, data[0], data[1])
+        sma_df = pd.DataFrame(smas)
+        place.metric(label=metric_label, value = int(sma_df.iloc[-1][col]), delta = int(sma_df.iloc[-1][col]-sma_df.iloc[-2][col]))
+        num=_update_guage(num, sma_df.iloc[-1][col], stock_data_history.iloc[-1]['close'], ind)
+        return num
+    elif ind == "WILLIAMS":
+        smas = TA.WILLIAMS(stock_data_history, data[0])
+        sma_df = pd.DataFrame(smas)
+        place.metric(label=metric_label, value = int(sma_df.iloc[-1][col]), delta = int(sma_df.iloc[-1][col]-sma_df.iloc[-2][col]))
+        num=_update_guage(num, sma_df.iloc[-1][col], stock_data_history.iloc[-1]['close'], ind)
+        return num
+    elif ind == "UO":
+        smas = TA.UO(stock_data_history)
+        sma_df = pd.DataFrame(smas)
+        st.write(sma_df.iloc[-1][col])
+        place.metric(label=metric_label, value = int(sma_df.iloc[-1][col]), delta = int(sma_df.iloc[-1][col]-sma_df.iloc[-2][col]))
+        _update_guage(num, sma_df.iloc[-1][col], stock_data_history.iloc[-1]['close'], ind)
+        return num
+    elif ind == "EBBP":
+        smas = TA.EBBP(stock_data_history)
+        sma_df = pd.DataFrame(smas)
+        place.metric(label=metric_label, value = int(sma_df.iloc[-1][col]), delta = int(sma_df.iloc[-1][col]-sma_df.iloc[-2][col]))
+        _update_guage(num, sma_df.iloc[-1][col], stock_data_history.iloc[-1]['close'], ind)
+        return num
+

pages/social_page.py

@@ -0,0 +1,59 @@
+"""Query Workbench."""
+
+import streamlit as st
+import pandas as pd
+import altair as alt
+
+from login import check_local_token
+from pages.helper.query import Queries
+from request import vasahm_query
+from menu import add_menu
+
+
+
+st.set_page_config(layout='wide',
+                   page_title="وسهم - میزکار، دسترسی آزاد اطلاعات",
+                    page_icon="./assets/favicon.ico",
+                    initial_sidebar_state='expanded')
+
+with open( "style.css", encoding="UTF-8") as css:
+    st.markdown( f'<style>{css.read()}</style>' , unsafe_allow_html= True)
+
+
+add_menu()
+
+df = pd.read_csv("data.csv").dropna()
+list_of_name = df['name'].to_list()
+if "stock" in st.query_params:
+    STOCK_INDEX = list_of_name.index(st.query_params.stock)
+else:
+    STOCK_INDEX = 0
+name = st.sidebar.selectbox("لیست سهام", options = list_of_name, index=STOCK_INDEX)
+selected_stock = df.iloc[df.loc[df['name'] == name].index[0]]
+
+if "ver" in st.session_state:
+    st.sidebar.header(f'Vasahm DashBoard `{st.session_state.ver}`')
+
+check_local_token()
+if "token" in st.session_state:
+
+    queries = Queries(name)
+
+    error, stock_data = vasahm_query(queries.get_daily_social())
+    if error:
+        st.error(stock_data, icon="🚨")
+    else:
+        if len(stock_data) > 0:
+            st.header("اقبال به سهم", divider='rainbow')
+            stock_data_history = pd.DataFrame(stock_data, columns=[
+            "number",
+            "date"])
+            # stock_data_history["date"] = stock_data_history[
+            #     "date"].astype(str)
+
+            chart = alt.Chart(stock_data_history).mark_bar().encode(
+                # alt.Color('row_title:N', title="سرفصلها"),
+                alt.Y('number:Q', title="تعداد کامنت"),
+                alt.X('date:N',title="تاریخ")
+            )
+            st.altair_chart(chart, use_container_width=True)