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import os
from urllib.request import urlopen
from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType
from config import AppConfig
import findspark
from pipeline_utils import rename_cols, snake_case
from trading_data_pipeline.load_daily_bars_etl import JDBC_PROPERTIES

"""

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import sqlalchemy as db
import pandas as pd


def create_upsert_method(meta: db.MetaData):
    """
    Create upsert method that satisfied the pandas's to_sql API. Adapted to 2.0
    """

    def method(table, conn, keys, data_iter):

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import json  
from streamlit_lightweight_charts import renderLightweightCharts  
import pandas_ta as ta  
  
  
class StockChart:  
  
    @staticmethod  
    def show_chart(df, symbol):  
        COLOR_BULL = 'rgba(38,166,154,0.9)'  # #26a69a  

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import logging
import pandas as pd
from dateutil.relativedelta import relativedelta
from dateutil.utils import today

from app.charts import render_candlestick_chart
import streamlit as st
import asyncio
import plotly.graph_objects as go

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from abc import abstractmethod
import backtrader as bt


class BaseStrategy(bt.Strategy):
    params = dict(
        verbose=True,
        longs_enabled=True,
        shorts_enabled=True
    )

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import backtrader as bt

from trading_backtesting.stop_trailer import StopTrailer


class GoldenCrossStrategy(bt.Strategy):
    SHORT, NONE, LONG = -1, 0, 1

    params = dict(
        fast_length=50,

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SELECT s.symbol, s.sector, first(close, date) AS first_close, last(close, date) AS last_close,
      MIN(date) AS first_date,
      MAX(date) AS last_date,
      100 * (last(close, date) - first(close, date)) / first(close, date) AS return_change
    FROM
      daily_bars db, stocks s
    WHERE
      db.date >= now() - interval '1 year' AND db.symbol = s.symbol
    GROUP BY
      s.symbol

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import os
from pathlib import Path
from sqlalchemy import create_engine
import pandas as pd

from trading_data_pipeline.config import AppConfig

config = AppConfig(os.environ)

connection_url = config.get_postgres_uri()

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version: "3"
x-airflow-common: &airflow-common
  # In order to add custom dependencies or upgrade provider packages you can use your extended image.
  # Comment the image line, place your Dockerfile in the directory where you placed the docker-compose.yaml
  # and uncomment the "build" line below, Then run `docker-compose build` to build the images.
  image: trading-airflow #${AIRFLOW_IMAGE_NAME:-apache/airflow}
  # build: .
  environment: &airflow-common-env
    AIRFLOW__CORE__EXECUTOR: LocalExecutor
    AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow