Candlestick Screener

Candlestick Screener has 3 functionalities :

1. Stock Search - Get candle screener chart with 100ma, 200ma, 21ma of your selected stock from list of ~1800 stocks on NSE

1.1. Expand Show Chart and this candle stick chart will open.

2. Nifty 500 Explorer - This data analysis tool can present you with Breaking out, Consolidating, Volume breakouts, stocks near moving averages in realtime.

3. Twitter Search - Search with keyword with date and number of tweets and you will get all related tweets (I use it to get latest tweets on my favourite stocks)

Setup

$ pip3 install -r requirements.txt

How to Run quickly?

1. Clone the Repo
2. install Requirements
3. Run below command in CMD or other console.

streamlit run streamapp_500.py

4. A browser window will pop will Candle Screener open on localhost.
5. Pull latest data from button on top and Run your favourite analysis from list
6. Or Select your favourite stock and get everything on it.

Technicalities

This section will show some of the functionality of each class; however, it is by no means exhaustive.

Getting data

# This function will fetch ticker data from yfinance (tickers present in csv file)
# Tickers such as - Tata Chemicals,TATACHEM.NS ; Tata Coffee,TATACOFFEE.NS

def snapshot():
    with open('datasets/long.csv') as f:
        for line in f:
            if "," not in line:
                continue
            symbol = line.split(",")[1]
            Name = line.split(",")[0]
            data = yf.download(symbol, period="9mo", threads = True) 
            data.to_csv('datasets/long/{}.csv'.format(Name))
    return {
        "code": "success"
    }

# valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
# Valid periods : 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max

# Its set to 9 months by default for calculating 200 MA correctly

Updating data

while True:
    
    snapshot()
    time.sleep(300)
    print(f'Ran {i} time')
    i = i+1

Building a Custom Watchlist

Build your portfolio by adding the stocks in /long/long.csv in format below

Tata Chemicals,TATACHEM.NS 
Tata Coffee,TATACOFFEE.NS

Analysis

Analysis technique : Moving Average

    def moving_avg(df):
    df['100ma'] = df['Adj Close'].rolling(window=100, min_periods=0).mean() #for 100 moving avg
    df['200ma'] = df['Adj Close'].rolling(window=200, min_periods=0).mean()
    last_close = df[-1:]['Adj Close'].values[0]
    hma = df[-1:]['100ma'].values[0]
    twoma = df[-1:]['200ma'].values[0]
    per = (1/100)*last_close

    if abs(last_close - hma) <= per:
        x = filename + " is around 100 MA"
        ma.append(x)

    if abs(last_close - twoma) <= per:
        y = filename + " is around 200 MA"
        tma.append(y)

Reading and Passing the dataframe.

    for filename in os.listdir('datasets/long'):
    df = pandas.read_csv('datasets/long/{}'.format(filename))
    vol_break(df)
    moving_avg(df)

Sending it to Streamlit

    hundredma = pd.DataFrame(ma, columns=['Hundred_MA'])
    twohundredma = pd.DataFrame(tma, columns=['two_Hundred_MA'])

    st.dataframe(hundredma)
    st.dataframe(twohundredma)

Visualizing data

Run the Streamlit server by hitting the commmand

streamlit run streamapp_500.py