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logic-center

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camphong24032002 commited on Mar 21, 2024

Commit

6b95d78

1 Parent(s): 71baec5

Test

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Files changed (33) hide show

.gitignore +160 -0
Dockerfile +11 -2
data/data.csv +0 -0
data/data_ceo.csv +252 -0
logic/__init__.py +0 -0
logic/api.py +93 -0
logic/nadaraya.py +65 -0
logic/pattern_hammer.py +275 -0
logic/res_sup.py +156 -0
logic/utils.py +0 -0
main.py +24 -1
models/__init__.py +0 -0
models/ichimoku.py +5 -0
models/macd.py +5 -0
models/resup.py +7 -0
models/rsi.py +10 -0
models/summary.py +10 -0
notebook.ipynb +0 -0
routes/__init__.py +0 -0
routes/confidence.py +23 -0
routes/signal.py +202 -0
services/__init__.py +0 -0
services/config.py +2 -0
services/credentials/__init__.py +0 -0
services/credentials/credentials.json +1 -0
services/credentials/token.pickle +3 -0
services/google_calendar.py +172 -0
services/indicator.py +97 -0
services/notification.py +50 -0
services/preprocess.py +9 -0
services/resup.py +153 -0
services/strategy.py +167 -0
setup.py +3 -0

.gitignore ADDED Viewed

	@@ -0,0 +1,160 @@

+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+# C extensions
+*.so
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+# Translations
+*.mo
+*.pot
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+# Flask stuff:
+instance/
+.webassets-cache
+# Scrapy stuff:
+.scrapy
+# Sphinx documentation
+docs/_build/
+# PyBuilder
+.pybuilder/
+target/
+# Jupyter Notebook
+.ipynb_checkpoints
+# IPython
+profile_default/
+ipython_config.py
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+# SageMath parsed files
+*.sage.py
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+# Spyder project settings
+.spyderproject
+.spyproject
+# Rope project settings
+.ropeproject
+# mkdocs documentation
+/site
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+# Pyre type checker
+.pyre/
+# pytype static type analyzer
+.pytype/
+# Cython debug symbols
+cython_debug/
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

Dockerfile CHANGED Viewed

@@ -1,12 +1,21 @@
 FROM python:3.9
 WORKDIR /code
 COPY ./requirements.txt /code/requirements.txt
 RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
 COPY . .
-CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]

 FROM python:3.9
+RUN apt-get update && apt-get install -y \
+    libgl1-mesa-glx \
+    && rm -rf /var/lib/apt/lists/*
 WORKDIR /code
 COPY ./requirements.txt /code/requirements.txt
+RUN pip install --upgrade pip
 RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
 COPY . .
+RUN chown -R 1000:1000 /code
+USER 1000
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

data/data.csv ADDED Viewed

The diff for this file is too large to render. See raw diff

data/data_ceo.csv ADDED Viewed

	@@ -0,0 +1,252 @@

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+144,2023-08-28,CEO,15099470.0,25400.0,26300.0,26400.0,26200.0,,
+145,2023-08-29,CEO,12754759.0,25600.0,26400.0,27000.0,25600.0,,
+146,2023-08-30,CEO,15126322.0,25000.0,25600.0,25800.0,25300.0,,
+147,2023-08-31,CEO,13255367.0,25200.0,25300.0,26500.0,26100.0,,
+148,2023-09-05,CEO,16948963.0,23500.0,26000.0,27800.0,27200.0,23500.0,
+149,2023-09-06,CEO,9612018.0,26600.0,26900.0,27600.0,27500.0,,
+150,2023-09-07,CEO,10605767.0,27100.0,27500.0,28300.0,27300.0,,
+151,2023-09-08,CEO,11421124.0,26800.0,27400.0,27600.0,27100.0,,
+152,2023-09-11,CEO,12008927.0,26700.0,27500.0,28000.0,26700.0,,
+153,2023-09-12,CEO,8324275.0,26400.0,26700.0,27600.0,27600.0,,
+154,2023-09-13,CEO,14638639.0,27600.0,27900.0,29600.0,28400.0,,29600.0
+155,2023-09-14,CEO,10061518.0,27000.0,28400.0,28600.0,27000.0,,
+156,2023-09-15,CEO,6141561.0,26600.0,27000.0,27600.0,27100.0,,
+157,2023-09-18,CEO,6894578.0,26100.0,27000.0,27200.0,26400.0,,
+158,2023-09-19,CEO,14234247.0,24900.0,25100.0,26700.0,25100.0,,
+159,2023-09-20,CEO,10221397.0,24900.0,25100.0,26500.0,26500.0,,
+160,2023-09-21,CEO,6874657.0,25800.0,26700.0,27000.0,25900.0,,
+161,2023-09-22,CEO,12549443.0,23500.0,25800.0,25900.0,23900.0,,
+162,2023-09-25,CEO,12234822.0,21600.0,23800.0,24200.0,21600.0,,
+163,2023-09-26,CEO,11109817.0,20200.0,21700.0,22300.0,20700.0,,
+164,2023-09-27,CEO,8707649.0,20100.0,20600.0,21800.0,21800.0,,
+165,2023-09-28,CEO,5060235.0,20800.0,22000.0,22200.0,21000.0,,
+166,2023-09-29,CEO,6053644.0,21100.0,21200.0,21800.0,21300.0,,
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+170,2023-10-05,CEO,7369644.0,18200.0,19700.0,20100.0,18300.0,,
+171,2023-10-06,CEO,8738334.0,17500.0,18300.0,18900.0,18500.0,17500.0,
+172,2023-10-09,CEO,7764334.0,18500.0,18800.0,19900.0,19800.0,,
+173,2023-10-10,CEO,7171004.0,19600.0,20000.0,20600.0,19600.0,,
+174,2023-10-11,CEO,6619141.0,19000.0,19600.0,20000.0,20000.0,,
+175,2023-10-12,CEO,11334191.0,20100.0,20100.0,21200.0,20600.0,,
+176,2023-10-13,CEO,8621291.0,19800.0,20600.0,21000.0,20900.0,,
+177,2023-10-16,CEO,8724225.0,20500.0,20900.0,21500.0,20500.0,,21500.0
+178,2023-10-17,CEO,5554165.0,19000.0,20700.0,20900.0,19000.0,,
+179,2023-10-18,CEO,10867376.0,17500.0,19300.0,19500.0,18200.0,17500.0,
+180,2023-10-19,CEO,8983183.0,17500.0,18200.0,19000.0,18200.0,17500.0,
+181,2023-10-20,CEO,11377635.0,18000.0,18200.0,20000.0,20000.0,,
+182,2023-10-23,CEO,12738156.0,18000.0,18000.0,21200.0,20300.0,,
+183,2023-10-24,CEO,8905742.0,19800.0,19800.0,21000.0,21000.0,,
+184,2023-10-25,CEO,9573246.0,20100.0,21100.0,21400.0,20200.0,,21400.0
+185,2023-10-26,CEO,20852383.0,18200.0,20200.0,20200.0,18500.0,,
+186,2023-10-27,CEO,9752679.0,18200.0,18500.0,20300.0,20300.0,,
+187,2023-10-30,CEO,9770085.0,19400.0,20100.0,20400.0,19600.0,,
+188,2023-10-31,CEO,14236635.0,19500.0,19700.0,20600.0,19700.0,,
+189,2023-11-01,CEO,10760413.0,18800.0,19700.0,20200.0,20200.0,,
+190,2023-11-02,CEO,18154138.0,20100.0,20500.0,22200.0,22100.0,,
+191,2023-11-03,CEO,12490448.0,21500.0,22100.0,22500.0,21900.0,,
+192,2023-11-06,CEO,10068538.0,21400.0,22000.0,22500.0,22200.0,,
+193,2023-11-07,CEO,11215293.0,21600.0,22200.0,22500.0,21800.0,,
+194,2023-11-08,CEO,20798138.0,21600.0,21800.0,23900.0,23900.0,,
+195,2023-11-09,CEO,14727762.0,24000.0,24500.0,25500.0,24100.0,,25500.0
+196,2023-11-10,CEO,23598705.0,23300.0,24100.0,25000.0,23500.0,,
+197,2023-11-13,CEO,14125299.0,22600.0,23600.0,24100.0,23100.0,,
+198,2023-11-14,CEO,14293800.0,22500.0,23500.0,23800.0,22700.0,,
+199,2023-11-15,CEO,16995446.0,22500.0,22900.0,23900.0,22600.0,,
+200,2023-11-16,CEO,46940585.0,21400.0,22100.0,23700.0,23500.0,,
+201,2023-11-17,CEO,26139595.0,22600.0,23500.0,24100.0,22800.0,,
+202,2023-11-20,CEO,17824665.0,21800.0,23000.0,23200.0,22900.0,,
+203,2023-11-21,CEO,16432028.0,20700.0,20700.0,23500.0,23100.0,,
+204,2023-11-22,CEO,20220451.0,22600.0,22800.0,23700.0,23400.0,,
+205,2023-11-23,CEO,33006034.0,22000.0,23400.0,24300.0,22000.0,,24300.0
+206,2023-11-24,CEO,23606344.0,19800.0,21800.0,22500.0,22400.0,19800.0,
+207,2023-11-27,CEO,11521525.0,21600.0,22000.0,22600.0,21600.0,,
+208,2023-11-28,CEO,17235960.0,20500.0,20500.0,21800.0,21600.0,,
+209,2023-11-29,CEO,10159152.0,21600.0,21700.0,22100.0,21900.0,,
+210,2023-11-30,CEO,12968147.0,21600.0,21900.0,22500.0,21600.0,,
+211,2023-12-01,CEO,9652051.0,21300.0,21600.0,22000.0,21600.0,,
+212,2023-12-04,CEO,21340997.0,21300.0,21300.0,23300.0,22900.0,,
+213,2023-12-05,CEO,15539506.0,22700.0,22900.0,23300.0,22800.0,,
+214,2023-12-06,CEO,20791558.0,22700.0,22700.0,23800.0,23600.0,,
+215,2023-12-07,CEO,28360957.0,21300.0,21300.0,24400.0,23400.0,21300.0,
+216,2023-12-08,CEO,16076922.0,23100.0,24800.0,24800.0,23400.0,,24800.0
+217,2023-12-11,CEO,15399352.0,23100.0,23100.0,24000.0,23900.0,,
+218,2023-12-12,CEO,12338828.0,21600.0,21700.0,24300.0,23800.0,,
+219,2023-12-13,CEO,16126308.0,21600.0,23100.0,24200.0,23000.0,,
+220,2023-12-14,CEO,14368276.0,22500.0,23000.0,23500.0,22600.0,,
+221,2023-12-15,CEO,15058563.0,22000.0,22300.0,23100.0,22800.0,,
+222,2023-12-18,CEO,6782798.0,20800.0,22600.0,23200.0,22300.0,,
+223,2023-12-19,CEO,10176411.0,20100.0,21200.0,22600.0,22600.0,20100.0,
+224,2023-12-20,CEO,4613358.0,22200.0,22200.0,22900.0,22500.0,,
+225,2023-12-21,CEO,5572479.0,22200.0,22300.0,22700.0,22600.0,,
+226,2023-12-22,CEO,9448885.0,22500.0,22700.0,23100.0,22700.0,,
+227,2023-12-25,CEO,8829755.0,20500.0,21500.0,23300.0,23000.0,,23300.0
+228,2023-12-26,CEO,6381009.0,20700.0,20700.0,23300.0,23000.0,,23300.0
+229,2023-12-27,CEO,8090760.0,22800.0,23200.0,23200.0,22800.0,,
+230,2023-12-28,CEO,7419177.0,22600.0,23000.0,23000.0,22600.0,,
+231,2023-12-29,CEO,6518709.0,22600.0,22700.0,22900.0,22700.0,,
+232,2024-01-02,CEO,6949166.0,22500.0,22900.0,23100.0,22500.0,,
+233,2024-01-03,CEO,4681307.0,22400.0,22400.0,22800.0,22700.0,22400.0,
+234,2024-01-04,CEO,17510437.0,22700.0,22800.0,23400.0,22800.0,,
+235,2024-01-05,CEO,8607853.0,22800.0,22900.0,23200.0,22900.0,,
+236,2024-01-08,CEO,15180351.0,22900.0,23000.0,23600.0,23300.0,,23600.0
+237,2024-01-09,CEO,9843985.0,22900.0,23200.0,23400.0,22900.0,,
+238,2024-01-10,CEO,13977672.0,22300.0,22700.0,23100.0,22400.0,,
+239,2024-01-11,CEO,7479203.0,22200.0,22500.0,22800.0,22600.0,,
+240,2024-01-12,CEO,16179428.0,21800.0,22400.0,22700.0,22000.0,,
+241,2024-01-15,CEO,7223186.0,21500.0,21500.0,22300.0,21500.0,,
+242,2024-01-16,CEO,5843595.0,20600.0,20600.0,21800.0,21800.0,20600.0,
+243,2024-01-17,CEO,5242101.0,21800.0,22000.0,22200.0,21800.0,,
+244,2024-01-18,CEO,6272216.0,21800.0,21900.0,22300.0,22000.0,,
+245,2024-01-19,CEO,5681085.0,21700.0,22300.0,22300.0,21800.0,,
+246,2024-01-22,CEO,7013691.0,21500.0,21800.0,22200.0,22200.0,,
+247,2024-01-23,CEO,4322253.0,21800.0,22500.0,22500.0,21800.0,,22500.0
+248,2024-01-24,CEO,4037889.0,21700.0,22100.0,22100.0,21700.0,,
+249,2024-01-25,CEO,3817719.0,21600.0,21800.0,21900.0,21600.0,,
+250,2024-01-26,CEO,4219559.0,21600.0,21600.0,22000.0,21700.0,,

logic/__init__.py ADDED Viewed

File without changes

logic/api.py ADDED Viewed

	@@ -0,0 +1,93 @@

+from fastapi import FastAPI
+from pydantic import BaseModel
+import uvicorn
+from logic.nadaraya import NadarayaWatson
+from logic.res_sup import ResSupCluster
+from data_core.utils import prepare_data
+from collections import Counter
+import os
+import traceback
+app = FastAPI()
+class Response(BaseModel):
+    signal: dict = {}
+    message: str = None
+    error: str = None
+@app.post("/nadaraya")
+def nada(symbol: str = "CEO"):
+    res = Response()
+    try:
+        data = prepare_data(symbol=symbol, date_past=365)
+        Nada = NadarayaWatson(data=data)
+        if data.iloc[-1]["open"] <= Nada.lower_bound:
+            signal = "buy"
+        elif data.iloc[-1]["open"] >= Nada.upper_bound:
+            signal = "sell"
+        else:
+            signal = "hold"
+        # update res
+        res.signal["Nadaraya"] = signal
+    except:
+        res.error = traceback.format_exc()
+    return res
+@app.post("/resup")
+def ressup(symbol: str = "CEO"):
+    res = Response()
+    try:
+        # Extract Data
+        print("symbol", symbol)
+        data = prepare_data(symbol=symbol, date_past=365)
+        # today price
+        today_price = data.iloc[-1]["open"]
+        # fit cluster
+        ResSup = ResSupCluster(data=data,
+                            n_clusters=3,
+                            is_delete_outlier=True)
+        print(data)
+        # buy and sell range
+        current_week_cluster = Counter(ResSup.data.iloc[-7 :]["Clusters"])
+        current_day_cluster = current_week_cluster.most_common()[0][0]
+        support = ResSup.support[current_day_cluster]
+        resistance = ResSup.resistance[current_day_cluster]
+        # find buy range
+        buy_range = (support, support + (resistance - support) * 0.236)
+        sell_range = (resistance - (resistance - support) * 0.236, resistance)
+        if buy_range[0] <= today_price <= buy_range[1]:
+            signal = "buy"
+        elif sell_range[0] <= today_price <= sell_range[1]:
+            signal = "sell"
+        else:
+            signal = "hold"
+        res.signal["resup"] = signal
+        res.message = f"price: {today_price}, resitance: {resistance}, support: {support}"
+    except:
+        res.error = traceback.format_exc()
+    return res
+# if __name__ == "__main__":
+#     uvicorn.run(app, host="127.0.0.1", port=8000, log_level="info")

logic/nadaraya.py ADDED Viewed

	@@ -0,0 +1,65 @@

+import numpy as np
+import matplotlib.pyplot as plt
+class NadarayaWatson():
+    def __init__() -> None: pass
+    @staticmethod
+    def gauss_kernel(u):
+        return np.exp(-0.5 * u**2) / np.sqrt(2 * np.pi)
+    @staticmethod
+    def nadaraya_watson_envelope_indicator(prices,
+                                           bandwidth: int = 10,
+                                           env_percent: int = 1):
+        n = len(prices)
+        envelopes = np.zeros((n, 3))  # Two columns for upper and lower envelopes
+        for i in range(n):
+            # Estimate conditional expectation using Nadaraya-Watson estimator
+            numerator = \
+                np.sum(NadarayaWatson.gauss_kernel(
+                    (i - np.arange(n)) / bandwidth) * prices)
+            denominator = \
+                np.sum(NadarayaWatson.gauss_kernel(
+                    (i - np.arange(n)) / bandwidth))
+            conditional_expectation = \
+                numerator / denominator if denominator != 0 else 0
+            # Calculate upper and lower envelopes
+            envelopes[i, 0] = \
+                conditional_expectation + np.std(prices) * env_percent
+            envelopes[i, 1] = \
+                conditional_expectation - np.std(prices) * env_percent
+            envelopes[i, 2] = \
+                conditional_expectation
+        return envelopes
+    # @staticmethod
+    # def get_viz():
+    #     # Plotting
+    #     plt.figure(figsize=(10, 6))
+    #     plt.plot(self.prices, label='Prices', color='blue')
+    #     plt.plot(self.envelopes[:, 0], label='Upper Envelope', color='red', linestyle='--')
+    #     plt.plot(self.envelopes[:, 1], label='Lower Envelope', color='green', linestyle='--')
+    #     plt.plot(self.envelopes[:, 2], label='Estimator', color='k', linestyle='--')
+    #     plt.title('Nadaraya-Watson Envelope Indicator')
+    #     plt.xlabel('Days')
+    #     plt.ylabel('Price')
+    #     plt.legend()
+    #     plt.grid(True)
+    #     plt.show()
+# Example Usage
+# if __name__ == "__main__":
+#     data = vns.stock_historical_data(symbol="ACB", start_date="2022-01-31",
+#                                 end_date='2024-01-31', resolution='1D',
+#                                 type='stock', beautify=True, decor=False,
+#                                 source='DNSE').iloc[-269:-68].reset_index()
+#     nada = NadarayaWatson(data)
+#     print(nada.envelopes[-1])
+# %%

logic/pattern_hammer.py ADDED Viewed

	@@ -0,0 +1,275 @@

+#%%
+import pandas as pd
+import numpy as np
+import vnstock as vns
+import matplotlib.pyplot as plt
+from datetime import datetime
+def get_candle_plot(df: pd.DataFrame, buy_sell_df: pd.DataFrame ) -> None:
+    # "up" dataframe will store the self.df
+    # when the closing stock price is greater
+    # than or equal to the opening stock prices
+    up = df[df.close >= df.open]
+    # "down" dataframe will store the df
+    # when the closing stock price is
+    # lesser than the opening stock prices
+    down = df[df.close < df.open]
+    # When the stock prices have decreased, then it
+    # will be represented by blue color candlestick
+    col1 = 'red'
+    # When the stock prices have increased, then it
+    # will be represented by green color candlestick
+    col2 = 'green'
+    # Setting width of candlestick elements
+    width = .3
+    width2 = .03
+    fig = plt.figure()
+    # Plotting up prices of the stock
+    plt.bar(up.time, up.close-up.open, width, bottom=up.open, color=col1)
+    plt.bar(up.time, up.high-up.close, width2, bottom=up.close, color=col1)
+    plt.bar(up.time, up.low-up.open, width2, bottom=up.open, color=col1)
+    # Plotting down prices of the stock
+    plt.bar(down.time, down.close-down.open, width, bottom=down.open, color=col2)
+    plt.bar(down.time, down.high-down.open, width2, bottom=down.open, color=col2)
+    plt.bar(down.time, down.low-down.close, width2, bottom=down.close, color=col2)
+    # x and y labeling
+    plt.xlabel("Index")
+    plt.ylabel("Prices (VND)")
+    # plot buy point
+    for i in buy_sell_df.index:
+        if buy_sell_df.signal.loc[i] == "buy":
+            color = "red"
+        else:
+            color = "blue"
+        plt.axvline(x = buy_sell_df.date.loc[i],
+                    color = color,
+                    linestyle='--'  )
+    # displaying candlestick chart of stock data
+    # of a week
+    plt.show()
+class CandleBlow():
+    def __init__(self, df: pd.DataFrame,
+                 is_viz: bool=True, use_close_price: bool=True,
+                 slope_thres = 55) -> None:
+        # init variables
+        self.df = df
+        self.is_buy = False
+        self.is_sell = False
+        # ensure slope thres is positbve
+        slope_thres = abs(slope_thres)
+        # detect hammer value
+        self.is_hammer = self.detect_hammer(tail_ratio=2, tol_pct=0.1/100)
+        self.is_reverse_hammer = self.detect_inverse_hammer(tail_ratio=2, tol_pct=0.1/100)
+        # change point
+        is_change_point = self.is_hammer or self.is_reverse_hammer
+        if is_change_point:
+            # get fit
+            self.fit_function, self.fit_values = self.__get_fit(degree=1, use_close_price=use_close_price)
+            # find derivative
+            self.deriv_function, self.deriv_value = self.__get_derivative(self.fit_function)
+            # indentify buy point
+            if abs(self.deriv_value[-1]) > slope_thres and is_viz:
+                self.__get_viz(self.fit_values, self.deriv_value)
+            is_buy = self.deriv_value[-1] < -slope_thres
+            is_sell = self.deriv_value[-1] > slope_thres
+            self.is_buy = is_change_point and is_buy
+            self.is_sell = is_change_point and is_sell
+    def __get_viz(self, fit_values=None, deriv_value=None) -> None:
+        # "up" dataframe will store the self.df
+        # when the closing stock price is greater
+        # than or equal to the opening stock prices
+        up = self.df[self.df.close >= self.df.open]
+        # "down" dataframe will store the self.df
+        # when the closing stock price is
+        # lesser than the opening stock prices
+        down = self.df[self.df.close < self.df.open]
+        # When the stock prices have decreased, then it
+        # will be represented by blue color candlestick
+        col1 = 'red'
+        # When the stock prices have increased, then it
+        # will be represented by green color candlestick
+        col2 = 'green'
+        # Setting width of candlestick elements
+        width = .3
+        width2 = .03
+        fig, axs = plt.subplots(2,1, sharex=True)
+        # Plotting up prices of the stock
+        axs[0].bar(up.index, up.close-up.open, width, bottom=up.open, color=col1)
+        axs[0].bar(up.index, up.high-up.close, width2, bottom=up.close, color=col1)
+        axs[0].bar(up.index, up.low-up.open, width2, bottom=up.open, color=col1)
+        # Plotting down prices of the stock
+        axs[0].bar(down.index, down.close-down.open, width, bottom=down.open, color=col2)
+        axs[0].bar(down.index, down.high-down.open, width2, bottom=down.open, color=col2)
+        axs[0].bar(down.index, down.low-down.close, width2, bottom=down.close, color=col2)
+        # x and y labeling
+        axs[1].set_xlabel("Index")
+        axs[0].set_ylabel("Prices (VND)")
+        if len(fit_values) > 0:
+            axs[0].plot(self.df.index, fit_values, label = "Fit Line")
+        if len(deriv_value) > 0:
+            axs[1].plot(self.df.index, deriv_value, label = "Derivative Line")
+        axs[0].grid()
+        axs[1].grid()
+        # displaying candlestick chart of stock data
+        # of a week
+        fig.show()
+    def __get_fit(self, degree: int = 5,
+                  use_close_price: bool = True) -> np.ndarray:
+        """
+        Get poly fit coef and value estimation for stock data:
+        Inputs:
+            self.df: pd.DataFrame, stock price from vnstock
+            degree: int, how tight is the fit
+            use_close: bool, use close or open price
+        Outputs:
+            est_value: fit estimate value
+            var: np.poly1d object function of the polyfit
+        """
+        if use_close_price:
+            price = self.df.close
+        else:
+            price = self.df.open
+        data_len = self.df.shape[0]
+        # Perform polynomial fitting
+        coefficients = np.polyfit(self.df.index, price, degree)
+        # funciton
+        fit_function = np.poly1d(coefficients)
+        index = np.arange(data_len)
+        est_value = fit_function(index)
+        # # get y_axis value
+        # est_value = self.__get_fit_value(coefficients ,self.df.shape[0])
+        return fit_function, est_value
+    def __get_derivative(self, fit_function: np.poly1d) -> np.poly1d:
+        """
+        Find derivative function of the fit function
+        Inputs:
+            fit_function: np.poly1d object of the fit function, produced by np.polyfit
+        outputs:
+            deriv_function: np.poly1d objects of the derivative function
+            deriv_value: np.ndarray of the output value from deriv function
+        """
+        data_len = self.df.shape[0]
+        deriv_function = fit_function.deriv()
+        deriv_value = deriv_function(np.arange(data_len))
+        return deriv_function, deriv_value
+    def detect_hammer(self,
+                      tol_pct = 0.1 / 100,
+                      tail_ratio = 2.5) -> bool:
+        today_price = self.df.iloc[-1]
+        close = today_price.close
+        open = today_price.open
+        high = today_price.high
+        low = today_price.low
+        tol_price = high - tol_pct * high
+        return ((close >= tol_price or open >= tol_price)
+                and high - low >= tail_ratio * abs(close - open))
+    def detect_inverse_hammer(self,
+                              tol_pct = 0.1 / 100,
+                               tail_ratio = 2.5) -> bool:
+        today_price = self.df.iloc[-1]
+        close = today_price.close
+        open = today_price.open
+        high = today_price.high
+        low = today_price.low
+        tol_price = low + tol_pct * high
+        return ((close <= tol_price or open <= tol_price)
+                and high - low >= tail_ratio * abs(close - open))
+#%%
+# Sample Use
+if __name__ == "__main__":
+    df = vns.stock_historical_data(symbol="ACB", start_date="2023-01-15",
+                                end_date='2024-01-15', resolution='1D',
+                                type='stock', beautify=True, decor=False)
+    buy_sell_df = pd.DataFrame({"date": [df.time.iloc[0]],
+                            "hammer": [True],
+                            "reverse_hammer": [True],
+                            "signal":["buy"]})
+    # use trend from 1.5 trading week
+    for i in range(7, df.shape[0]):
+        train_data = df.iloc[i-7 : i]
+        candle = CandleBlow(df=train_data,
+                            use_close_price=False,
+                            is_viz=False)
+        if candle.is_buy:
+            buy_sell_df.loc[buy_sell_df.shape[0]] = {"date": train_data.time.iloc[-1],
+                            "hammer": candle.is_hammer,
+                            "reverse_hammer": candle.is_reverse_hammer,
+                            "signal":"buy"}
+        if candle.is_sell:
+            buy_sell_df.loc[buy_sell_df.shape[0]] = {"date": train_data.time.iloc[-1],
+                            "hammer": candle.is_hammer,
+                            "reverse_hammer": candle.is_reverse_hammer,
+                            "signal":"sell"}
+    # plot result
+    buy_sell_df = buy_sell_df.iloc[1:]
+    get_candle_plot(df, buy_sell_df)

logic/res_sup.py ADDED Viewed

	@@ -0,0 +1,156 @@

+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.cluster import AgglomerativeClustering
+import vnstock as vns
+from collections import Counter, defaultdict
+class ResSupCluster():
+    def __init__(self, data: pd.DataFrame, n_clusters: int = 2,
+                  is_visualize: bool = False, is_delete_cluster_outlier: bool = True):
+        self.n_clusters = n_clusters
+        self.data = data
+        # train cluster
+        self.model, self.data["Clusters"] = self.train_AgglomerativeClustering(self.data["open"], n_clusters)
+        # Delete outlier to better fit data
+        if is_delete_cluster_outlier:
+            self.__delete_cluster_outlier(error_period=5)
+        # get resistance and support
+        self.__get_res_sup()
+        if is_visualize:
+            self.get_viz()
+    def __get_res_sup(self):
+        # Calculate cluster centroids
+        self.level_dict = defaultdict(dict)
+        prices = self.data["open"]
+        clusters = self.data["Clusters"].copy()
+        current_cluster = clusters.iloc[0]
+        # find min max of continuos cluster
+        min_price = float("inf")
+        max_price = 0
+        start = 0
+        sum = 0
+        for i in range(len(clusters)):
+            if clusters.iloc[i] == current_cluster:
+                max_price = max(max_price, prices.iloc[i])
+                min_price = min(min_price, prices.iloc[i])
+                sum += prices.iloc[i]
+            else:
+                period = (self.data["time"].iloc[start], self.data["time"].iloc[i])
+                self.level_dict[period]["cluster"] = current_cluster
+                self.level_dict[period]["support"] = min_price
+                self.level_dict[period]["resistance"] = max_price
+                self.level_dict[period]["index"] = (start, i)
+                self.level_dict[period]["average"] = sum / (i - start - 1)
+                # reinit
+                start = i
+                sum = 0
+                current_cluster = clusters.iloc[i]
+                min_price = prices.iloc[i]
+                max_price = prices.iloc[i]
+        period = (self.data["time"].iloc[start], self.data["time"].iloc[i])
+        self.level_dict[period]["cluster"] = current_cluster
+        self.level_dict[period]["support"] = min_price
+        self.level_dict[period]["resistance"] = max_price
+        self.level_dict[period]["average"] = sum / (i - start - 1)
+        self.level_dict[period]["index"] = (start, i)
+    def __delete_cluster_outlier(self, error_period: int = 5):
+        """
+        Delete outlier clusters
+        """
+        left = 0
+        right = 0
+        counter = 0
+        error = 10
+        clusters = list(self.data["Clusters"].copy())
+        while right < len(self.data["Clusters"]):
+            if self.data["Clusters"][left] == self.data["Clusters"][right]:
+                counter += 1
+            else:
+                if counter < error:
+                    clusters[left:right] = [clusters[left - 1]] * counter
+                counter = 1
+                left = right
+            right += 1
+        self.data["Clusters"] = clusters
+        self.data = self.data.iloc[2:]
+    def get_viz(self):
+        level_dict = self.level_dict
+        for period in level_dict.keys():
+            period_dict = level_dict[period]
+            plt.hlines(period_dict["support"], period_dict["index"][0], period_dict["index"][1],
+                        colors="r")
+            plt.hlines(period_dict["resistance"], period_dict["index"][0], period_dict["index"][1],
+                    colors="b")
+            plt.hlines(period_dict["average"], period_dict["index"][0], period_dict["index"][1],
+                    colors="k")
+        # labeling
+        plt.hlines(period_dict["support"], 0, 0, colors = "b", label="Resistance")
+        plt.hlines(period_dict["resistance"], 0, 0, colors = "r", label="Support")
+        plt.hlines(period_dict["average"], 0, 0, colors = "b", label="Resistance")
+        # actual price
+        plt.scatter(self.data.index, self.data["open"],
+                    c=self.data["Clusters"], cmap='viridis', label='Clusters')
+        plt.xlabel("Index")
+        plt.ylabel("Prices")
+        plt.grid()
+        plt.legend()
+        plt.show()
+    def train_AgglomerativeClustering(
+        self, data: np.ndarray, n_clusters: int = 3
+    ) -> np.ndarray:
+        """
+        Fit Agglomerative Clustering
+            Inputs:
+                data: pd.DataFrame data
+                n_clusters: numbers of clusters, default = 4
+            Ouputs:
+                clusters: clusters data where val = clusters, index is same
+        """
+        prices = data.values.reshape(-1, 1)
+        # Fit Agglomerative Clustering
+        model = AgglomerativeClustering(n_clusters=n_clusters, linkage="ward")
+        clusters = model.fit_predict(prices)
+        return model, clusters
+# Example Ussage
+if __name__ == "__main__":
+    data = vns.stock_historical_data(symbol="CEO", start_date="2023-01-31",
+                                end_date='2024-01-31', resolution='1D',
+                                type='stock', beautify=True, decor=False,
+                                source='DNSE')
+    Cluster = ResSupCluster(data=data,
+                            is_visualize=True,
+                            is_delete_cluster_outlier=True,
+                            n_clusters=3)
+    # cluster levels in self.level_dict
+    print(Cluster.level_dict)

logic/utils.py ADDED Viewed

File without changes

main.py CHANGED Viewed

	@@ -1 +1,24 @@
1	- ~~print("Hello~~ ~~world")~~

+from fastapi import FastAPI
+from starlette.middleware.cors import CORSMiddleware
+from routes.signal import router as SignalRouter
+from routes.confidence import router as ConfidenceRouter
+app = FastAPI(title="Stock logic API", docs_url="/docs", version="0.1.0")
+app.include_router(SignalRouter, tags=["Signal"], prefix="/signal")
+app.include_router(ConfidenceRouter, tags=["Confidence"], prefix="/confidence")
+@app.get("/", tags=["Root"])
+async def read_root():
+    return {"message": "Stock Signal API v1"}
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["POST", "PUT", "DELETE", "OPTION", "GET"],
+    allow_headers=["*"],
+)

models/__init__.py ADDED Viewed

File without changes

models/ichimoku.py ADDED Viewed

	@@ -0,0 +1,5 @@

+from pydantic import BaseModel
+class IchimokuPayload(BaseModel):
+    symbol: str

models/macd.py ADDED Viewed

	@@ -0,0 +1,5 @@

+from pydantic import BaseModel
+class MACDSignalPayload(BaseModel):
+    symbol: str

models/resup.py ADDED Viewed

	@@ -0,0 +1,7 @@

+from pydantic import BaseModel
+class ResSupPayload(BaseModel):
+    data: str
+    feature_map: dict
+    n_clusters: int = 3

models/rsi.py ADDED Viewed

	@@ -0,0 +1,10 @@

+from pydantic import BaseModel
+class RSISignalPayload(BaseModel):
+    symbol: str
+    periods: int = 14
+    smooth_k: int = 3
+    smooth_d: int = 3
+    threshold_low: int = 20
+    threshold_high: int = 80

models/summary.py ADDED Viewed

	@@ -0,0 +1,10 @@

+from pydantic import BaseModel
+class SummarySignalPayload(BaseModel):
+    symbol: str
+    periods: int = 14
+    smooth_k: int = 3
+    smooth_d: int = 3
+    threshold_low: int = 20
+    threshold_high: int = 80

notebook.ipynb ADDED Viewed

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routes/__init__.py ADDED Viewed

File without changes

routes/confidence.py ADDED Viewed

	@@ -0,0 +1,23 @@

+from models.resup import ResSupPayload
+from fastapi import status, APIRouter
+from services.resup import ResSupCluster
+from typing import List
+import pandas as pd
+import json
+router = APIRouter()
+@router.post(
+    "/resup",
+    name="Get lines of Resistance and Support",
+    status_code=status.HTTP_200_OK
+)
+async def get_resup_line(payload: ResSupPayload) -> List[float]:
+    data = pd.DataFrame(json.loads(payload.data))
+    cluster = ResSupCluster(
+        data=data,
+        feature_map=payload.feature_map,
+        n_clusters=payload.n_clusters
+    )
+    return cluster.extract_all_lines()

routes/signal.py ADDED Viewed

	@@ -0,0 +1,202 @@

+from services.preprocess import Preprocess
+from services.strategy import Strategy
+from services.notification import Notification
+from models.rsi import RSISignalPayload
+from models.ichimoku import IchimokuPayload
+from models.macd import MACDSignalPayload
+from models.summary import SummarySignalPayload
+from pymongo import MongoClient, ASCENDING, DESCENDING
+from fastapi import status, APIRouter
+from typing import Sequence
+import requests
+import pandas as pd
+import os
+import asyncio
+from dotenv import load_dotenv
+load_dotenv()
+router = APIRouter()
+DATA_URL = "https://intellistock-data-api.hf.space/data"
+@router.get(
+    "/daily_report",
+    name="Get daily signal report",
+    status_code=status.HTTP_200_OK
+)
+async def get_daily_report():
+    uri = os.environ.get("MONGODB_URI")
+    client = MongoClient(uri)
+    database = client.get_database("data")
+    rsi_collection = database.get_collection("rsi")
+    price_collection = database.get_collection("price")
+    macd_collection = database.get_collection("macd")
+    signal_collection = database.get_collection("signal")
+    rsi_records = list(rsi_collection.find(sort=[("_id", ASCENDING)]))
+    price_records = list(price_collection.find(sort=[("_id", ASCENDING)]))
+    macd_records = list(macd_collection.find(sort=[("_id", ASCENDING)]))
+    rsi_df = pd.DataFrame(rsi_records).drop(columns=["_id"])
+    macd_df = pd.DataFrame(macd_records).drop(columns=["_id"])
+    lst_symbols = rsi_df.columns[1:]
+    last_date = rsi_df["time"].iloc[-1]
+    newest_record = \
+        signal_collection.find_one(sort=[("_id", DESCENDING)])
+    if newest_record["time"] == last_date:
+        return {"message": "The data is up to date!"}
+    result = Strategy.get_daily_report(price_records,
+                                       rsi_df,
+                                       macd_df,
+                                       last_date,
+                                       lst_symbols)
+    today_df = result[result["time"] == last_date][["ticker",
+                                                    "rsi_signal",
+                                                    "macd_signal",
+                                                    "ichimoku_signal_1",
+                                                    "ichimoku_signal_3"]]
+    today_dict = today_df.to_dict(orient="list")
+    today_dict["time"] = last_date
+    signal_collection.insert_one(today_dict)
+    prefix_url = os.environ.get("PREFIX_URL")
+    asyncio.create_task(
+        Notification.daily_message(today_df, prefix_url, last_date))
+    return {"message": "Updated signal"}
+@router.get(
+    "/daily_nadaraya",
+    name="Get daily nadaraya signal",
+    status_code=status.HTTP_200_OK
+)
+async def get_daily_nadaraya_report() -> None:
+    uri = os.environ.get("MONGODB_URI")
+    client = MongoClient(uri)
+    database = client.get_database("data")
+    nada_collection = database.get_collection("nada")
+    rsi_collection = database.get_collection("rsi")
+    rsi_records = list(rsi_collection.find(sort=[("_id", ASCENDING)]))
+    price_collection = database.get_collection("price")
+    price_records = list(price_collection.find(sort=[("_id", ASCENDING)]))
+    rsi_df = pd.DataFrame(rsi_records).drop(columns=["_id"])
+    lst_symbols = rsi_df.columns[1:]
+    last_date = rsi_df["time"].iloc[-1]
+    newest_record = \
+        nada_collection.find_one(sort=[("_id", DESCENDING)])
+    if newest_record["time"] == last_date:
+        return {"message": "The data is up to date!"}
+    result = Strategy.get_daily_nadaraya(price_records,
+                                         lst_symbols,
+                                         last_date)
+    result_dict = result.to_dict(orient="list")
+    result_dict["time"] = last_date
+    nada_collection.insert_one(result_dict)
+    prefix_url = os.environ.get("PREFIX_URL")
+    asyncio.create_task(
+        Notification.notify_nadaraya(result, prefix_url, last_date))
+    return {"message": "Updated nadaraya"}
+@router.post(
+    "/rsi",
+    name="Get signal of RSI",
+    status_code=status.HTTP_200_OK
+)
+async def get_rsi_signal(payload: RSISignalPayload) -> Sequence[dict]:
+    rsi_dict = {"symbol": payload.symbol,
+                "periods": payload.periods,
+                "smooth_k": payload.smooth_k,
+                "smooth_d": payload.smooth_d}
+    try:
+        response = requests.post(DATA_URL+"/get_rsi", json=rsi_dict)
+        rsi_json = response.json()
+        rsi_df = Preprocess.lst_dict_to_df(rsi_json)
+        rsi_df = Strategy.rsi_strategy(
+            rsi_df, threshold=(
+                payload.threshold_low, payload.threshold_high
+            )
+        )
+        rsi_signal_lst_dict = \
+            rsi_df[["time", "rsi_signal"]].to_dict(orient="records")
+    except Exception as e:
+        return [{"message": f"Caught error {e}"}]
+    return rsi_signal_lst_dict
+@router.post(
+    "/ichimoku",
+    name="Get signal of Ichimoku",
+    status_code=status.HTTP_200_OK
+)
+async def get_ichimoku_data(payload: IchimokuPayload) -> Sequence[dict]:
+    ichimoku_dict = {
+        "symbol": payload.symbol
+    }
+    try:
+        response = requests.post(DATA_URL+"/get_ichimoku", json=ichimoku_dict)
+        ichimoku_json = response.json()
+        ichimoku_df = Preprocess.lst_dict_to_df(ichimoku_json)
+        ichimoku_df = Strategy.ichimoku_strategy(ichimoku_df)
+        ichimoku_signal_lst_dict = \
+            ichimoku_df[["time",
+                         "ichimoku_signal_1",
+                         "ichimoku_signal_3"]].to_dict(orient="records")
+    except Exception as e:
+        return [{"message": f"Caught error {e}"}]
+    return ichimoku_signal_lst_dict
+@router.post(
+    "/macd",
+    name="Get signal of MACD",
+    status_code=status.HTTP_200_OK
+)
+async def get_macd_data(payload: MACDSignalPayload) -> Sequence[dict]:
+    macd_dict = {
+        "symbol": payload.symbol
+    }
+    try:
+        response = requests.post(DATA_URL+"/get_macd", json=macd_dict)
+        macd_json = response.json()
+        macd_df = Preprocess.lst_dict_to_df(macd_json)
+        macd_df = Strategy.macd_strategy(macd_df)
+        macd_signal_lst_dict = \
+            macd_df[["time", "macd_signal"]].to_dict(orient="records")
+    except Exception as e:
+        return [{"message": f"Caught error {e}"}]
+    return macd_signal_lst_dict
+@router.post(
+    "/summary",
+    name="Get summary of signal",
+    status_code=status.HTTP_200_OK
+)
+async def get_summary_data(payload: SummarySignalPayload) -> Sequence[dict]:
+    summary_dict = {"symbol": payload.symbol,
+                    "periods": payload.periods,
+                    "smooth_k": payload.smooth_k,
+                    "smooth_d": payload.smooth_d}
+    try:
+        response = requests.post(DATA_URL+"/get_rsi",
+                                 json=summary_dict)
+        rsi_json = response.json()
+        rsi_df = Preprocess.lst_dict_to_df(rsi_json)
+        rsi_df = Strategy.rsi_strategy(rsi_df)
+        response = requests.post(DATA_URL+"/get_macd",
+                                 json=summary_dict)
+        macd_json = response.json()
+        macd_df = Preprocess.lst_dict_to_df(macd_json)
+        macd_df = Strategy.macd_strategy(macd_df)
+        df = pd.merge(rsi_df, macd_df, how="left", on="time")
+        response = requests.post(DATA_URL+"/get_ichimoku",
+                                 json=summary_dict)
+        ichimoku_json = response.json()
+        ichimoku_df = Preprocess.lst_dict_to_df(ichimoku_json)
+        ichimoku_df = Strategy.ichimoku_strategy(ichimoku_df)
+        df = pd.merge(df, ichimoku_df, how="right", on="time")
+        query_df = \
+            df.query("ichimoku_signal_1 != -1 | ichimoku_signal_3 != -1")
+        query_df = query_df.fillna(-1)
+        summary_lst_dict = query_df.to_dict(orient="records")
+    except Exception as e:
+        return [{"message": f"Caught error {e}"}]
+    return summary_lst_dict

services/__init__.py ADDED Viewed

File without changes

services/config.py ADDED Viewed

	@@ -0,0 +1,2 @@


1	+ DATE_FORMAT = "%Y-%m-%d"
2	+ QUERY_CONDITION = "(ichimoku_signal_1 > -1 \| ichimoku_signal_3 > -1) \| (rsi_signal > -1)"

services/credentials/__init__.py ADDED Viewed

File without changes

services/credentials/credentials.json ADDED Viewed

	@@ -0,0 +1 @@

+ {"installed":{"client_id":"140817432133-dmqodgl7bsjfalptf8q5ri171ps5373v.apps.googleusercontent.com","project_id":"stockmining-412308","auth_uri":"https://accounts.google.com/o/oauth2/auth","token_uri":"https://oauth2.googleapis.com/token","auth_provider_x509_cert_url":"https://www.googleapis.com/oauth2/v1/certs","client_secret":"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","redirect_uris":["http://localhost"]}}

services/credentials/token.pickle ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:cacd0d63843ea18ee68d79c6a7ad1e2f79f6f3efafa2306f5e58b8d061d2fc43
+size 929

services/google_calendar.py ADDED Viewed

	@@ -0,0 +1,172 @@

+from gcsa.google_calendar import GoogleCalendar
+from gcsa.event import Event
+from datetime import datetime
+from pathlib import Path
+import pickle
+import os.path
+from google.auth.transport.requests import Request
+from google_auth_oauthlib.flow import InstalledAppFlow
+BASE_DIR = Path(__file__).parent
+class GoogleCalendarAPI:
+    def __init__(self) -> None:
+        self.scopes = ['https://www.googleapis.com/auth/calendar']
+        self.calendar = self.create_service()
+    def create_service(self):
+        """create service with credentials set up and token file,
+        Note:
+            If you use a new machine or browser, just delete the `token.pickle`
+                in folder `credentials`. And then run application again,
+                a new `sign in cookie pop up` would appear and loging in.
+                After logged, a new `token.pickle` would be created
+                automatically in folder credentials.
+        Returns:
+            GoogleCalendar: Calendar object
+        """
+        creds = None
+        if os.path.exists(Path(BASE_DIR, 'credentials', 'token.pickle')):
+            with open(
+                Path(BASE_DIR, 'credentials', 'token.pickle'), 'rb'
+            ) as token:
+                creds = pickle.load(token)
+        if not creds or not creds.valid:
+            if creds and creds.expired and creds.refresh_token:
+                creds.refresh(Request())
+            else:
+                flow = InstalledAppFlow.from_client_secrets_file(
+                    Path(BASE_DIR, 'credentials', 'credentials.json'),
+                    self.scopes
+                )
+                creds = flow.run_local_server(port=0)
+            with open(
+                Path(BASE_DIR, 'credentials', 'token.pickle'), 'wb'
+            ) as token:
+                pickle.dump(creds, token)
+        try:
+            calendar = GoogleCalendar('primary', credentials=creds)
+        except Exception as e:
+            print(e)
+            return None
+        return calendar
+    def setup_event(
+        self,
+        event_name: str,
+        event_start_time: str,
+        event_end_time: str,
+        attendees: list = [],
+        event_description: str = None
+    ):
+        """Set up event to calendar from given information from user
+        Args:
+            event_name (str): The title of event,
+                you could generate out by yourself if user not provided
+            event_start_time (str): The start time string of event that user
+                want to set calendar. Format is "%Y-%m-%d %H:%M"
+                Example:
+                    "2001-01-02 13:55": which means 13:55 at 1s Feburary 2001
+            event_end_time (str): The end time string of event that user
+                want to set calendar. Format is "%Y-%m-%d %H:%M"
+                Example:
+                    "2001-01-02 14:30": which means 14:30 at 1s Feburary 2001
+            attendees (list, optional): List of attendee's email.
+                Defaults to [].
+            event_description (str, optional): The desription of event.
+                Defaults to None.
+        Returns:
+            str: result status of this action
+        """
+        try:
+            event = Event(
+                event_name,
+                start=datetime.strptime(event_start_time, "%Y-%m-%d %H:%M"),
+                end=datetime.strptime(event_end_time, "%Y-%m-%d %H:%M"),
+                attendees=attendees,
+                description=event_description,
+                default_reminders=True
+            )
+            self.calendar.add_event(event)
+        except Exception as ex:
+            return f"There is a problem while setting calendar, {ex}"
+        return "Set calendar successfully !"
+    def get_events(
+        self,
+        year_range: list,
+        month_range: list,
+        day_range: list,
+        hour_range: list
+    ) -> list:
+        """To get out all the events in user's calendar with
+            given range of time
+        Args:
+            year_range (list): Range year that user need to require
+                Example:
+                    [2023, 2025]: Range from 2023 to 2025
+            month_range (list): Range month that user need to require
+                Example:
+                    [1, 5]: Range from January to May
+            day_range (list): Range day that user need to require
+                Example:
+                    [1, 31]: Range from 1st to 31st
+            day_range (list): Range hour that user need to require,
+                if user want to require in same date, hour must be [0, 23]
+                Example:
+                    [0, 23]: Range from 0am to 23pm
+        Returns:
+            list: list of events found in user calendar with given range time
+        """
+        start_date = datetime(
+            year=year_range[0],
+            month=month_range[0],
+            day=day_range[0],
+            hour=hour_range[0]
+        )
+        end_date = datetime(
+            year=year_range[1],
+            month=month_range[1],
+            day=day_range[1],
+            hour=hour_range[1]
+        )
+        return [event for event in self.calendar.get_events(
+            time_min=start_date, time_max=end_date
+        )]
+    def get_events_by_date(
+        self,
+        start_date: datetime,
+        end_date: datetime
+    ) -> list:
+        return [event for event in self.calendar.get_events(
+            time_min=start_date, time_max=end_date
+        )]
+if __name__ == "__main__":
+    calendar = GoogleCalendarAPI()
+#     # Sample set up event on calendar
+#     calendar.setup_event(
+#         event_name="This is sample buy signal",
+#         event_start_time="02/17/2024 14:33",
+#         event_end_time="02/17/2024 15:30",
+#         attendees=['ndthinh0201@gmail.com', 'camphong.work@gmail.com'],
+#         event_description="""
+#             W trend has found in 7 days past and today figure out
+#                 the rise significantly, better to buy.
+#         """
+#     )

services/indicator.py ADDED Viewed

	@@ -0,0 +1,97 @@

+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+from .config import DATE_FORMAT
+class Indicator:
+    def __init__(self) -> None:
+        pass
+    @staticmethod
+    def get_all_rsi(rsi_df: pd.DataFrame,
+                    periods: int = 14,
+                    smooth_k: int = 3,
+                    smooth_d: int = 3) -> pd.DataFrame:
+        rsi_df["stoch_rsi"] = \
+            Indicator.stoch_rsi(rsi_df["rsi"], periods)
+        rsi_df["stoch_rsi_smooth_k"] = \
+            Indicator.stoch_rsi_smooth_k(rsi_df["stoch_rsi"], smooth_k)
+        rsi_df["stoch_rsi_smooth_d"] = Indicator.stoch_rsi_smooth_d(
+            rsi_df["stoch_rsi_smooth_k"], smooth_d
+        )
+        return rsi_df
+    @staticmethod
+    def stoch_rsi(rsi: pd.Series, periods: int = 14) -> pd.Series:
+        ma, mi = (
+            rsi.rolling(window=periods).max(),
+            rsi.rolling(window=periods).min(),
+        )
+        return (rsi - mi) * 100 / (ma - mi)
+    @staticmethod
+    def stoch_rsi_smooth_k(stoch_rsi: pd.Series, k: int = 3) -> pd.Series:
+        return stoch_rsi.rolling(window=k).mean()
+    @staticmethod
+    def stoch_rsi_smooth_d(stoch_rsi_k: pd.Series, d: int = 3) -> pd.Series:
+        return stoch_rsi_k.rolling(window=d).mean()
+    @staticmethod
+    def generate_dates(start_date: datetime.date,
+                       num_days: int,
+                       format=DATE_FORMAT):
+        lst_date = []
+        current_date = start_date
+        for i in range(num_days):
+            current_date += timedelta(days=1)
+            while current_date.weekday() >= 5:
+                current_date += timedelta(days=1)
+            lst_date.append(current_date.strftime(format))
+        return lst_date
+    @staticmethod
+    def get_ichimoku_cloud(
+        df: pd.DataFrame,
+        conversion_period=9,
+        base_period=26,
+        span_b_period=52,
+        displacement=26,
+    ) -> pd.DataFrame:
+        space_displacement = np.full(displacement, np.nan)
+        tenkan_sen = (
+            df["high"].rolling(window=conversion_period).max()
+            + df["low"].rolling(window=conversion_period).min()
+        ) / 2
+        kijun_sen = (
+            df["high"].rolling(window=base_period).max()
+            + df["low"].rolling(window=base_period).min()
+        ) / 2
+        senkou_span_a = (tenkan_sen + kijun_sen) / 2
+        senkou_span_b = (
+            df["high"].rolling(window=span_b_period).max()
+            + df["low"].rolling(window=span_b_period).min()
+        ) / 2
+        chikou_span = df["close"].shift(-displacement)
+        last_date = datetime.strptime(df["time"].iloc[-1], DATE_FORMAT)
+        lst_date = Indicator.generate_dates(last_date, displacement)
+        time = np.concatenate((df["time"], lst_date))
+        tenkan_sen = np.concatenate((tenkan_sen, space_displacement))
+        kijun_sen = np.concatenate((kijun_sen, space_displacement))
+        senkou_span_a = np.concatenate((space_displacement, senkou_span_a))
+        senkou_span_b = np.concatenate((space_displacement, senkou_span_b))
+        chikou_span = np.concatenate((chikou_span, space_displacement))
+        data_dict = {
+            "time": time,
+            "tenkan_sen": tenkan_sen,
+            "kijun_sen": kijun_sen,
+            "senkou_span_a": senkou_span_a,
+            "senkou_span_b": senkou_span_b,
+            "chikou_span": chikou_span,
+            "tenkan_kijun": tenkan_sen - kijun_sen,
+            "kumo_cloud": senkou_span_a - senkou_span_b
+        }
+        return pd.DataFrame(data_dict)

services/notification.py ADDED Viewed

	@@ -0,0 +1,50 @@

+import pandas as pd
+import requests
+class Notification:
+    def __init__(self) -> None:
+        pass
+    @staticmethod
+    def generate_today_description(df: pd.DataFrame,
+                                   today: str, name: str) -> str:
+        df = df.astype(str)
+        result = [f"{today} {name} Signal"]
+        result = result + ['| '.join([f'{s:<6}' for s in df.columns])]
+        for value in df.values:
+            value = [f'{s:<6}' for s in value]
+            result.append('| '.join(value))
+        return '\n'.join(result)
+    @staticmethod
+    async def daily_message(df: pd.DataFrame, prefix: str, today: str) -> None:
+        signal_mapping = {-1: "-", 0: "Buy", 1: "Sell"}
+        df["rsi_signal"] = df["rsi_signal"].map(signal_mapping)
+        df["macd_signal"] = df["macd_signal"].map(signal_mapping)
+        df["ichimoku_signal_1"] = \
+            df["ichimoku_signal_1"].map(signal_mapping)
+        df["ichimoku_signal_3"] = \
+            df["ichimoku_signal_3"].map(signal_mapping)
+        df.columns = ["ticker", "rsi", "macd", "ichi1", "ichi3"]
+        message = Notification.generate_today_description(df, today, "Daily")
+        Notification.send_msg(prefix, message)
+        print("Sent daily message")
+    @staticmethod
+    async def notify_nadaraya(df: pd.DataFrame, prefix: str, today: str) -> None:
+        signal_mapping = {-1: "-", 0: "Buy", 1: "Sell"}
+        # Today signal
+        df["open_signal"] = df["open_signal"].map(signal_mapping)
+        df["close_signal"] = df["close_signal"].map(signal_mapping)
+        df = df[["ticker", "open_signal", "close_signal"]]
+        df.columns = ["ticker", "open", "close"]
+        message = Notification.generate_today_description(df,
+                                                          today, "Nadaraya")
+        Notification.send_msg(prefix, message)
+        print("Sent nadaraya message")
+    @staticmethod
+    def send_msg(prefix: str, message: str):
+        url = prefix + message
+        requests.get(url)

services/preprocess.py ADDED Viewed

	@@ -0,0 +1,9 @@

+import pandas as pd
+class Preprocess:
+    def __init__(self) -> None: pass
+    @staticmethod
+    def lst_dict_to_df(lst_dict) -> pd.DataFrame:
+        return pd.DataFrame.from_records(lst_dict)

services/resup.py ADDED Viewed

	@@ -0,0 +1,153 @@

+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.cluster import AgglomerativeClustering
+# import vnstock as vns
+class ResSupCluster:
+    def __init__(
+        self,
+        data: pd.DataFrame,
+        feature_map: dict,
+        n_clusters: int = 2,
+        is_visualize: bool = False,
+        is_delete_outlier: bool = True,
+    ):
+        self.n_clusters = n_clusters
+        self.data = data
+        self.feature_map = feature_map
+        # Delete outlier to better fit data
+        if is_delete_outlier:
+            self.delete_outlier()
+        # train cluster
+        self.model, self.data["Clusters"] = self.train_AgglomerativeClustering(
+            self.data[feature_map["open"]], n_clusters
+        )
+        # get resistance and support
+        self.get_res_sup()
+        if is_visualize:
+            self.get_viz()
+    def get_res_sup(self):
+        # Calculate cluster centroids
+        self.support = []
+        self.resistance = []
+        self.average = []
+        prices = self.data[self.feature_map["open"]]
+        clusters = self.data["Clusters"]
+        for cluster_label in range(self.n_clusters):
+            # Get prices in same cluster
+            cluster_points = prices[clusters == cluster_label]
+            # Get support resistance and average
+            self.resistance.append(max(cluster_points))
+            self.support.append(min(cluster_points))
+            self.average.append(np.mean(cluster_points))
+    def delete_outlier(self):
+        """
+        Delete outlier that is 2 std away from mean
+        """
+        # to make sure we dont drop today data
+        data = self.data.iloc[:-1]
+        # extract mean and std
+        mean = np.mean(data[self.feature_map["open"]])
+        std = np.std(data[self.feature_map["open"]])
+        # drop outlier
+        data = data[
+            (mean - 2 * std < data[self.feature_map["open"]])
+            & (data[self.feature_map["open"]] < mean + 2 * std)
+        ]
+        # update self.data
+        today_data = self.data.iloc[-1].copy()
+        self.data = data
+        self.data[len(data.index)] = today_data
+    def get_viz(self):
+        # plt.plot(train_data.index, train_data["open"])
+        plt.scatter(
+            self.data.index,
+            self.data[self.feature_map["open"]],
+            c=self.data["Clusters"],
+            cmap="viridis",
+            label="Clusters",
+        )
+        plt.hlines(
+            self.support,
+            self.data.index[0],
+            self.data.index[-1],
+            colors="r",
+            label="Support",
+        )
+        plt.hlines(
+            self.resistance,
+            self.data.index[0],
+            self.data.index[-1],
+            colors="b",
+            label="Resistance",
+        )
+        plt.hlines(
+            self.average,
+            self.data.index[0],
+            self.data.index[-1],
+            colors="k",
+            label="Average",
+        )
+        plt.plot(
+            self.data.index,
+            self.data[self.feature_map["open"]],
+            label="Clusters",
+        )
+        plt.grid()
+        plt.legend()
+        plt.show()
+    def train_AgglomerativeClustering(
+        self, data: np.ndarray, n_clusters: int = 3
+    ) -> np.ndarray:
+        """
+        Fit Agglomerative Clustering
+            Inputs:
+                data: pd.DataFrame data
+                n_clusters: numbers of clusters, default = 4
+            Ouputs:
+                clusters: clusters data where val = clusters, index is same
+        """
+        prices = data.values.reshape(-1, 1)
+        # Fit Agglomerative Clustering
+        model = AgglomerativeClustering(n_clusters=n_clusters, linkage="ward")
+        clusters = model.fit_predict(prices)
+        return model, clusters
+    def extract_all_lines(self):
+        return {
+            'support': self.support,
+            'resistance': self.resistance,
+            'average': self.average
+        }
+# Example Ussage
+# if __name__ == "__main__":
+#     data = vns.stock_historical_data(
+#         symbol="CEO",
+#         start_date="2023-01-31",
+#         end_date="2024-01-31",
+#         resolution="1D",
+#         type="stock",
+#         beautify=True,
+#         decor=False,
+#         source="DNSE",
+#     )
+#     Cluster = ResSupCluster(
+#         data=data, is_visualize=True, is_delete_outlier=True, n_clusters=3
+#     )

services/strategy.py ADDED Viewed

	@@ -0,0 +1,167 @@

+import pandas as pd
+import numpy as np
+from .indicator import Indicator
+from .config import QUERY_CONDITION
+from logic.nadaraya import NadarayaWatson
+class Strategy:
+    """
+        Signal: -1 is null, 0 is buy and 1 is sell
+    """
+    def __init__(self) -> None:
+        pass
+    @staticmethod
+    def rsi_strategy(rsi_df, threshold=(20, 80)) -> pd.DataFrame:
+        low, high = threshold[0], threshold[1]
+        signal_values = []
+        loop_df = rsi_df[["stoch_rsi",
+                          "stoch_rsi_smooth_k",
+                          "stoch_rsi_smooth_d"]]
+        for stoch, smooth_k, smooth_d in loop_df.values:
+            if stoch <= low and \
+                smooth_k <= low and \
+                    smooth_d <= low:
+                signal_values.append(0)
+            elif stoch >= high and \
+                smooth_k >= high and \
+                    smooth_d >= high:
+                signal_values.append(1)
+            else:
+                signal_values.append(-1)
+        rsi_df["rsi_signal"] = signal_values
+        return rsi_df
+    @staticmethod
+    def ichimoku_strategy(df) -> pd.DataFrame:
+        signal_values_1, signal_values_3 = [-1], [-1]
+        for index in range(1, df.shape[0]):
+            prev = df.iloc[index - 1]
+            signal_1, signal_3 = -1, -1
+            if (not (np.isnan(df["tenkan_kijun"].iloc[index])) and
+                    df["tenkan_kijun"].iloc[index - 1: index + 1].prod() < 0):
+                if prev["tenkan_kijun"] < 0 and prev["tenkan_sen"] < min(
+                    prev["senkou_span_a"], prev["senkou_span_b"]
+                ):
+                    signal_1 = 0
+                elif prev["tenkan_kijun"] > 0 and prev["tenkan_sen"] > max(
+                    prev["senkou_span_a"], prev["senkou_span_b"]
+                ):
+                    signal_1 = 1
+            signal_values_1.append(signal_1)
+            if (not (np.isnan(df["kumo_cloud"].iloc[index])) and
+                    df["kumo_cloud"].iloc[index - 1: index + 1].prod() < 0):
+                if prev["kumo_cloud"] < 0:
+                    signal_3 = 0
+                elif prev["kumo_cloud"] > 0:
+                    signal_3 = 1
+            signal_values_3.append(signal_3)
+        df["ichimoku_signal_1"] = signal_values_1
+        df["ichimoku_signal_3"] = signal_values_3
+        return df
+    @staticmethod
+    def macd_strategy(macd_df) -> pd.DataFrame:
+        signal_values = []
+        for macd in macd_df["macd"]:
+            if macd < 0:
+                signal_values.append(0)
+            elif macd > 0:
+                signal_values.append(1)
+            else:
+                signal_values.append(-1)
+        macd_df["macd_signal"] = signal_values
+        return macd_df
+    @staticmethod
+    def get_daily_report(price_records,
+                         rsi_df,
+                         macd_df,
+                         last_date,
+                         lst_symbols):
+        try:
+            result = []
+            for index, symbol in enumerate(lst_symbols):
+                symbol_rsi_df = rsi_df[["time", symbol]]
+                symbol_macd_df = macd_df[["time", symbol]]
+                price_values = []
+                for price_record in price_records:
+                    tmp_record = price_record["value"][index]
+                    tmp_record["time"] = price_record["time"]
+                    price_values.append(tmp_record)
+                symbol_price_df = pd.DataFrame(price_values)
+                symbol_rsi_df = \
+                    symbol_rsi_df[["time", symbol]].rename(
+                        columns={symbol: "rsi"})
+                symbol_rsi_df = Indicator.get_all_rsi(symbol_rsi_df)
+                symbol_rsi_df = Strategy.rsi_strategy(symbol_rsi_df)
+                symbol_rsi_df = symbol_rsi_df.iloc[-1:]
+                symbol_macd_df = \
+                    symbol_macd_df[["time", symbol]].rename(
+                        columns={symbol: "macd"})
+                symbol_macd_df = Strategy.macd_strategy(symbol_macd_df)
+                symbol_macd_df = symbol_macd_df.iloc[-1:]
+                df = pd.merge(symbol_rsi_df,
+                              symbol_macd_df,
+                              how="inner",
+                              on="time")
+                symbol_ichi_df = Indicator.get_ichimoku_cloud(symbol_price_df)
+                symbol_ichi_df = Strategy.ichimoku_strategy(symbol_ichi_df)
+                symbol_ichi_df = symbol_ichi_df[["time",
+                                                 "ichimoku_signal_1",
+                                                 "ichimoku_signal_3"]]
+                symbol_ichi_df["ticker"] = symbol
+                df = pd.merge(df, symbol_ichi_df, how="right", on="time")
+                query_df = df[df["time"] >= last_date]
+                query_df = query_df.query(QUERY_CONDITION)
+                query_df = query_df.fillna(-1)
+                result.extend(query_df.to_dict(orient="records"))
+            result_df = pd.DataFrame(result)
+            return result_df
+        except Exception as e:
+            return {"message": f"Caught error {e}"}
+    @staticmethod
+    def nadaraya_strategy(symbol, price, envelope) -> dict:
+        open_signal, close_signal = -1, -1
+        if price["open"] >= envelope[0]:
+            open_signal = 1
+        elif price["open"] <= envelope[1]:
+            open_signal = 0
+        if price["close"] >= envelope[0]:
+            close_signal = 1
+        elif price["close"] <= envelope[1]:
+            close_signal = 0
+        return [{"ticker": symbol,
+                 "open_signal": open_signal,
+                 "close_signal": close_signal}]
+    @staticmethod
+    def get_daily_nadaraya(price_records,
+                           lst_symbols,
+                           last_date):
+        try:
+            result = []
+            for index, symbol in enumerate(lst_symbols):
+                price_values = []
+                for price_record in price_records:
+                    tmp_record = price_record["value"][index]
+                    tmp_record["time"] = price_record["time"]
+                    price_values.append(tmp_record)
+                symbol_price_df = pd.DataFrame(price_values).iloc[-201:]
+                nada_envelopes = \
+                    NadarayaWatson.nadaraya_watson_envelope_indicator(
+                        symbol_price_df["close"])
+                current_price = symbol_price_df.iloc[-1]
+                signal = Strategy.nadaraya_strategy(symbol,
+                                                    current_price,
+                                                    nada_envelopes[-1])
+                result.extend(signal)
+            result_df = pd.DataFrame(result)
+            result_df = result_df.query("open_signal > -1 | close_signal > -1")
+            return result_df
+        except Exception as e:
+            return {"message": f"Caught error {e}"}

setup.py ADDED Viewed

	@@ -0,0 +1,3 @@


1	+ from setuptools import setup, find_packages
2	+
3	+ setup(name="main-api", version='1.0', packages=find_packages())