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NASDAQ / ASX stocks bullish screener.

Stock shortlisting logic uses the following conditions depending on the method:

MRI (method=mri)

• Daily higher candle closes above bodies of the previous 5 daily candles and is green
• Bullish MRI indicator value on the daily timeframe
• Bullish MRI indicator on the weekly timeframe
• Moving averages (10, 20, 30 day) Consensio (Guppy MMA)
• Moving averages (10, 20, 30 day) rising
• Market is not below MA200 with MA10 declining
• 2 most recent weekly candles closing above weekly moving averages (10/20/30 week)
• Close for the last week is not exceeding 300% when compared to 4 weeks ago
• Volume is significant in the last day compared to the 20-day moving average (defined in the config)
• The stock has a significant range of movement over the past few weeks (defined in the config)
• Stochastic RSI is not overextended (>90%)

Alphanumetrix (method=anx)

• Price above MA200
• Cross of MA7 and MA30
• Close for the last week is not exceeding 300% when compared to 4 weeks ago
• Note: Bullish SAR on weekly scale have to be checked manually as I cannot figure out the correct Lucid SAR calculation

For ASX, the best time to run it is in the evening after market closure to prepare for the next day. For US, that have to be morning (running in Australia) as US stocks data would refresh after a night of trading (per AU time). The shortlist acts as a guide for entering breakout trades which have high probability of success. As a data source, eodhistoricaldata.com is used.

Please note that the shortlist should only be used to guide your own research and should not be interpreted as 'signals'. If you buy the stocks just using the output, you will definitely lose all your money.

• Run python scanner.py --h to view context help
• To update the stocks list, run python scanner.py --update. It is recommended to run this daily prior to scanning. You can use the -date parameter (the format is YYYY-MM-DD).
• To scan and shortlist, run python scanner.py --scan -method=anx.
  • Use method to specify the method used (mri or anx).
  • Use stock if you want to force check one stock only. E.g. -stock=NVDA.
• To simulate scanning as of a particular date, use the -date parameter (the format is YYYY-MM-DD). For example, python scanner.py --update -date=2021-01-05.
• Helper scripts (note: requires configuring Google credentials in order to work). Use -method parameter as this will influence which spreadsheet is used.
  • monitor.py to run daily to check whether the exit condition was hit for active entries.
  • paperfill.py to run daily to fill in the values for paper trade entries automatically.
  • simulator.py simulates outcomes per the spreadsheet.
    • Current experiment: exit price variation if using stop under the formation. Use the argument --exit_variation_a
  • simulator_legacy.py works with the older 21 R&D spreadsheet and also has an optional argument --market which would include market MA200/MA10 conditions when running simulation in the tp mode. You don't need to run this. This argument is there because the rule on using market conditions was already integrated in methodology and used for the stock selection with scanner.
• The monitor would notify:
  • when the close for a position is below MA10
  • when the market switches to the bearish mode (market below MA200 with MA10 decreasing) as a trigger to close all open positions

Example running a simulator: python simulator.py -mode=main -start=2023-12-10 -end=2024-04-01 -method=mri --show_monthly

See config.yaml for settings which include:

• Market to use
• Price range for stocks considered on scan
• Minimum volume threshold
• Overextended threshold
• Other conditions and rules

Your eodhistoricaldata API key must be placed in .envrc under variable API_KEY

• The scripts were tested on a machine in the Australia/Sydney timezone.
• Python 3.6+ is required.

1. Clone this repo: git clone https://github.com/illi4/asx-screener.git

2. Install Python 3 and virtualenv if you do not have it installed yet. The easiest way is to download and install from the official website. Instructions to install virtualenv are published on the python packaging website.

3. Change to the cloned folder, create a python3 venv and activate it.

   Linux / Mac:

   python3 -m venv venv
   . venv/bin/activate
   

4. Install the required libraries: pip install -r requirements.txt

5. Run referring to the usage instructions.

If you want to log your stocks and use monitor and paperfill in addition to the screener, please create a sheet similar to this one and configure API access, then save the credentials under .config\gspread\service_account.json. This is not required if you only want to use the scanner (scanner.py).

There is a warning thrown when using gsheets, which is a known issue and doesn't affect the outcomes.

Note: legacy sheet (R&D 2021) covering 2021 and before is available here.