Initial commit

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml

.idea/ProjectRam.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 3.8 (tradeplatform)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (tradeplatform)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/ProjectRam.iml" filepath="$PROJECT_DIR$/.idea/ProjectRam.iml" />
+    </modules>
+  </component>
+</project>

CS_pattern_rankings.py

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+# Note - 1
+# Only some patterns have bull and bear versions.
+# However, to make the process unified and for codability purposes
+# all patterns are labeled with "_Bull" and "_Bear" tags.
+# Both versions of the single patterns are given same performance rank,
+# since they will always return only 1 version.
+
+# Note - 2
+# Following TA-Lib patterns are excluded from the analysis since the corresponding ranking not found:
+# CounterAttack, Longline, Shortline, Stalledpattern, Kickingbylength
+
+
+candle_rankings = {
+        "CDL3LINESTRIKE_Bull": 1,
+        "CDL3LINESTRIKE_Bear": 2,
+        "CDL3BLACKCROWS_Bull": 3,
+        "CDL3BLACKCROWS_Bear": 3,
+        "CDLEVENINGSTAR_Bull": 4,
+        "CDLEVENINGSTAR_Bear": 4,
+        "CDLTASUKIGAP_Bull": 5,
+        "CDLTASUKIGAP_Bear": 5,
+        "CDLINVERTEDHAMMER_Bull": 6,
+        "CDLINVERTEDHAMMER_Bear": 6,
+        "CDLMATCHINGLOW_Bull": 7,
+        "CDLMATCHINGLOW_Bear": 7,
+        "CDLABANDONEDBABY_Bull": 8,
+        "CDLABANDONEDBABY_Bear": 8,
+        "CDLBREAKAWAY_Bull": 10,
+        "CDLBREAKAWAY_Bear": 10,
+        "CDLMORNINGSTAR_Bull": 12,
+        "CDLMORNINGSTAR_Bear": 12,
+        "CDLPIERCING_Bull": 13,
+        "CDLPIERCING_Bear": 13,
+        "CDLSTICKSANDWICH_Bull": 14,
+        "CDLSTICKSANDWICH_Bear": 14,
+        "CDLTHRUSTING_Bull": 15,
+        "CDLTHRUSTING_Bear": 15,
+        "CDLINNECK_Bull": 17,
+        "CDLINNECK_Bear": 17,
+        "CDL3INSIDE_Bull": 20,
+        "CDL3INSIDE_Bear": 56,
+        "CDLHOMINGPIGEON_Bull": 21,
+        "CDLHOMINGPIGEON_Bear": 21,
+        "CDLDARKCLOUDCOVER_Bull": 22,
+        "CDLDARKCLOUDCOVER_Bear": 22,
+        "CDLIDENTICAL3CROWS_Bull": 24,
+        "CDLIDENTICAL3CROWS_Bear": 24,
+        "CDLMORNINGDOJISTAR_Bull": 25,
+        "CDLMORNINGDOJISTAR_Bear": 25,
+        "CDLXSIDEGAP3METHODS_Bull": 27,
+        "CDLXSIDEGAP3METHODS_Bear": 26,
+        "CDLTRISTAR_Bull": 28,
+        "CDLTRISTAR_Bear": 76,
+        "CDLGAPSIDESIDEWHITE_Bull": 46,
+        "CDLGAPSIDESIDEWHITE_Bear": 29,
+        "CDLEVENINGDOJISTAR_Bull": 30,
+        "CDLEVENINGDOJISTAR_Bear": 30,
+        "CDL3WHITESOLDIERS_Bull": 32,
+        "CDL3WHITESOLDIERS_Bear": 32,
+        "CDLONNECK_Bull": 33,
+        "CDLONNECK_Bear": 33,
+        "CDL3OUTSIDE_Bull": 34,
+        "CDL3OUTSIDE_Bear": 39,
+        "CDLRICKSHAWMAN_Bull": 35,
+        "CDLRICKSHAWMAN_Bear": 35,
+        "CDLSEPARATINGLINES_Bull": 36,
+        "CDLSEPARATINGLINES_Bear": 40,
+        "CDLLONGLEGGEDDOJI_Bull": 37,
+        "CDLLONGLEGGEDDOJI_Bear": 37,
+        "CDLHARAMI_Bull": 38,
+        "CDLHARAMI_Bear": 72,
+        "CDLLADDERBOTTOM_Bull": 41,
+        "CDLLADDERBOTTOM_Bear": 41,
+        "CDLCLOSINGMARUBOZU_Bull": 70,
+        "CDLCLOSINGMARUBOZU_Bear": 43,
+        "CDLTAKURI_Bull": 47,
+        "CDLTAKURI_Bear": 47,
+        "CDLDOJISTAR_Bull": 49,
+        "CDLDOJISTAR_Bear": 51,
+        "CDLHARAMICROSS_Bull": 50,
+        "CDLHARAMICROSS_Bear": 80,
+        "CDLADVANCEBLOCK_Bull": 54,
+        "CDLADVANCEBLOCK_Bear": 54,
+        "CDLSHOOTINGSTAR_Bull": 55,
+        "CDLSHOOTINGSTAR_Bear": 55,
+        "CDLMARUBOZU_Bull": 71,
+        "CDLMARUBOZU_Bear": 57,
+        "CDLUNIQUE3RIVER_Bull": 60,
+        "CDLUNIQUE3RIVER_Bear": 60,
+        "CDL2CROWS_Bull": 61,
+        "CDL2CROWS_Bear": 61,
+        "CDLBELTHOLD_Bull": 62,
+        "CDLBELTHOLD_Bear": 63,
+        "CDLHAMMER_Bull": 65,
+        "CDLHAMMER_Bear": 65,
+        "CDLHIGHWAVE_Bull": 67,
+        "CDLHIGHWAVE_Bear": 67,
+        "CDLSPINNINGTOP_Bull": 69,
+        "CDLSPINNINGTOP_Bear": 73,
+        "CDLUPSIDEGAP2CROWS_Bull": 74,
+        "CDLUPSIDEGAP2CROWS_Bear": 74,
+        "CDLGRAVESTONEDOJI_Bull": 77,
+        "CDLGRAVESTONEDOJI_Bear": 77,
+        "CDLHIKKAKEMOD_Bull": 82,
+        "CDLHIKKAKEMOD_Bear": 81,
+        "CDLHIKKAKE_Bull": 85,
+        "CDLHIKKAKE_Bear": 83,
+        "CDLENGULFING_Bull": 84,
+        "CDLENGULFING_Bear": 91,
+        "CDLMATHOLD_Bull": 86,
+        "CDLMATHOLD_Bear": 86,
+        "CDLHANGINGMAN_Bull": 87,
+        "CDLHANGINGMAN_Bear": 87,
+        "CDLRISEFALL3METHODS_Bull": 94,
+        "CDLRISEFALL3METHODS_Bear": 89,
+        "CDLKICKING_Bull": 96,
+        "CDLKICKING_Bear": 102,
+        "CDLDRAGONFLYDOJI_Bull": 98,
+        "CDLDRAGONFLYDOJI_Bear": 98,
+        "CDLCONCEALBABYSWALL_Bull": 101,
+        "CDLCONCEALBABYSWALL_Bear": 101,
+        "CDL3STARSINSOUTH_Bull": 103,
+        "CDL3STARSINSOUTH_Bear": 103,
+        "CDLDOJI_Bull": 104,
+        "CDLDOJI_Bear": 104
+    }

PatternRecognition.py

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+import talib
+import pandas as pd
+
+def GetPatternForData(stock_data_df):
+    candle_name_list = talib.get_function_groups()['Pattern Recognition']
+    tech_analysis_df = stock_data_df.iloc[-10:].copy()
+    op_df = tech_analysis_df.Open
+    hi_df = tech_analysis_df.High
+    lo_df = tech_analysis_df.Low
+    cl_df = tech_analysis_df.Close
+
+    for candle in candle_name_list:
+        tech_analysis_df[candle] = getattr(talib, candle)(op_df, hi_df, lo_df, cl_df)
+
+    result = pd.DataFrame(tech_analysis_df[['Date']+candle_name_list].sum(),columns=['Count'])
+    filtered_results = result[result.Count!=0]
+
+    if filtered_results.empty:
+        return None,tech_analysis_df
+    else:
+        return filtered_results[filtered_results.Count == filtered_results.Count.max()].index[0],tech_analysis_df
+
+    return None,tech_analysis_df

VixAnalysis.py

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+from hmmlearn import hmm
+import plotly.graph_objects as go
+import streamlit as st
+
+original_map = {'max': 2, 'mid': 1, 'low': 0}
+
+@st.cache
+def GetMood(indval):
+    if indval <= 1/3:
+        return "Greed"
+    elif 1/3 < indval < 2/3:
+        return "Holding Steady"
+    else:
+        return "Fear"
+
+@st.cache
+def ProcessVixData(vix_df):
+    X = vix_df[['Close']].copy()
+    model = hmm.GaussianHMM(n_components=3, covariance_type="full", n_iter=100)
+    model.fit(X)
+    res = X.copy()
+    res['Class'] = model.predict(X)
+    resmap = res.groupby('Class').agg({'Close': 'mean'}).sort_values(by='Close')
+    resmap = dict(zip(resmap.index, ['low', 'mid', 'max']))
+    res['Class_name'] = res.Class.apply(lambda x:resmap[x])
+    res['Class_proc'] = res.Class_name.apply(lambda x:original_map[x])
+    res['Norm_Class'] = (res['Class_proc']-res['Class_proc'].min())/(res['Class_proc'].max()-res['Class_proc'].min())
+    return res
+
+@st.cache
+def GetIndicatorChart(res):
+    value = res.Norm_Class[-30:].mean()
+    fig = go.Figure(go.Indicator(
+        domain={'x': [0, 1], 'y': [0, 1]},
+        value=value,
+        mode="gauge+number",
+        title={'text': f"VIX Analysis\nMood : {GetMood(value)}"},
+        gauge={'axis': {'range': [None, res.Norm_Class.max()]},
+               'steps': [
+                   {'range': [0, 1/3], 'color': "green"},
+                   {'range': [1/3, 2/3], 'color': "gray"},
+                   {'range': [2/3, 1], 'color': "red"}],
+               'threshold': {'line': {'color': "black", 'width': 4}, 'thickness': 0.75, 'value': value}}))
+    return fig
+
+
+

main.py

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+import streamlit as st
+from nsetools import Nse
+from nsepy import get_history
+from datetime import date, timedelta
+from PatternRecognition import GetPatternForData
+from VixAnalysis import *
+
+import yfinance as yf
+
+today = date.today()
+nse = Nse()
+all_stock_codes = nse.get_stock_codes()
+all_stock_names = dict(zip(nse.get_stock_codes().values(),nse.get_stock_codes().keys()))
+del all_stock_names['NAME OF COMPANY']
+
+st.set_page_config(page_title='Jarvis', page_icon='N', layout='wide', initial_sidebar_state='auto')
+st.title('Jarvis')
+stock_tab, mf_tab = st.tabs(['Stocks', 'Mutual Funds'])
+
+with stock_tab:
+    stock_selector_col, tenure_selector_start_col, tenure_selector_end_col = st.columns(3)
+    with stock_selector_col:
+        selected_stock = st.selectbox('Please select a stock for analysis',
+                                        list(all_stock_names.keys()),help='Choose stock to analyze')
+        ticker_yf = all_stock_names[selected_stock]+".NS"
+
+    with tenure_selector_start_col:
+        start_date = st.date_input("Start Date", today-timedelta(40))
+
+    with tenure_selector_end_col:
+        end_date = st.date_input("End Date", today)
+
+    if (end_date<start_date):
+        st.error('Start date should be before End date')
+
+    time_delta = end_date-start_date
+    if time_delta.days < 35:
+        st.error('Please extend the range of dates. We need atleast 30 days of data for analysis.')
+
+    with st.spinner(text=f'Please Wait...Loading data for {selected_stock}'):
+        tk = yf.Ticker(ticker_yf)
+        stock_data_df = tk.history(start=start_date, end=end_date, interval="1d").reset_index()
+
+    chart_title_col, download_button_col = st.columns(2)
+    with chart_title_col:
+        st.subheader(f'OHLC Chart for {selected_stock}')
+
+    with download_button_col:
+        @st.cache
+        def convert_df(df):
+            # IMPORTANT: Cache the conversion to prevent computation on every rerun
+            return df.to_csv().encode('utf-8')
+
+        csv = convert_df(stock_data_df)
+        st.download_button(
+            label="Download data as CSV",
+            data=csv,
+            file_name=f'{selected_stock}.csv',
+            mime='text/csv',
+        )
+
+    fig_main_cs = go.Figure(data=[go.Candlestick(x=stock_data_df['Date'],
+                                         open=stock_data_df['Open'],
+                                         high=stock_data_df['High'],
+                                         low=stock_data_df['Low'],
+                                         close=stock_data_df['Close'])])
+    st.plotly_chart(fig_main_cs, use_container_width=True)
+
+    st.subheader('Candlestick Matched pattern:')
+    pattern_expander_col, cs_gauge_col = st.columns((1,3))
+
+    matched_pattern, tech_df = GetPatternForData(stock_data_df=stock_data_df)
+    if matched_pattern is not None:
+        with pattern_expander_col:
+            with st.expander(matched_pattern):
+                st.write("Will add description here")
+
+    else:
+        with pattern_expander_col:
+            with st.expander("No Pattern Matched"):
+                st.write("It seems there were no pattern formations for last 10 days.")
+
+with st.sidebar:
+    vix_container = st.container()
+    with vix_container:
+        st.subheader("VIX Analysis")
+        with st.spinner(text='Loading VIX Analysis...'):
+            vix_df = get_history(symbol="India VIX", start=today-timedelta(30), end=today, index=True)
+            vix_analysis_df = ProcessVixData(vix_df)
+        value = vix_analysis_df.Norm_Class[-30:].mean()
+        st.metric("Current Market Mood", GetMood(value), '{:.2f}'.format(value))
+        mood_dict = {'Greed': 'Markets are in bullish state. Good time to book profits.',
+                     'Holding Steady': 'Markets are indecisive. its good to hold onto existing portfolio.',
+                     'Fear': "Markets are in bearish state. Good time to pick good stocks for cheap prices."}
+
+        with st.expander(GetMood(value)):
+            st.write(mood_dict[GetMood(value)])
+
+

test.py

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+import streamlit as st
+from nsetools import Nse
+from datetime import date, timedelta
+import plotly.graph_objects as go
+import pandas as pd
+import yfinance as yf
+
+
+with st.spinner(text=f'Please Wait...Loading data for Reliance'):
+    tk = yf.Ticker('RELIANCE.NS')
+    stock_data_df = tk.history(start='2022-11-01', end='2022-12-01', interval="1d").reset_index()
+st.success('Done!')
+
+fig = go.Figure(data=[go.Candlestick(x=stock_data_df['Date'],
+                                     open=stock_data_df['Open'],
+                                     high=stock_data_df['High'],
+                                     low=stock_data_df['Low'],
+                                     close=stock_data_df['Close'])])
+st.plotly_chart(fig)