Overview

The python scripts provided in this project facilitates trying different buy and sell strategies for stock and crypto currencies. There are also scripts that provide different types of security analysis. Both technical analysis and fundamental analysis are considered.

This software is intended for educational purposes only and nothing it produces should be taken as investment advice. Its accuracy is not guaranteed, the software may have bugs, or the data that it receives from 3rd parties may be inaccurate. You are responsible for verifying the results that are produced.

Features

Some of these features are:

Back-Testing Investment Strategies
Machine Learning Predictors
Investment Strategy Comparators
Intrinsic Value Calculations
Alerts
Automated Order Placement

Single Stock Strategies

Some already added strategies are:

Buy and Hold strategy
Buy low Sell High
Trade on MACD Signal
Trade on RSI
Black soldiers and white crows

It's easy to add more, so this list will continue to grow.

Multi stock strategy

Monitor 5 stocks, when any are below RSI 40 buy, when it crosses 70 sell, buy the next of the five to cross 40, rinse and repeat

Strategy optimization

Each investment strategy has different variables (e.g. RSI has period, upper, lower) and these can be tuned via back testing for a given securities.

Once an optimized set of parameters is determined for one security, then those parameters can be back tested across many different securities.

Build table of RSI and MACD and use it to predict the next relative price movement of a stock

This is the correlation of RSI and MACD with how other investors act based on this data

Integrated ML/AI to make price model predictor and then use it to trade

Separate groups of data are used for training the model and another for testing/prediction

Donations

If you want to donate, then you can do that here:

0x9046e392d4F12ec5950F058960aF48B3929eCad6 (ETH/BNB/any EVM compatible chain address really)

Running Tools

NOTE: If this is your first run, then see the Getting Started section first, also there is a Tool List which explains each of the different tools in this repository.

Let's run the intrinsic value runner which does a discounted cash flow analysis of securities.

First you must add a file at ~/.eContriver/intrinsic_value.yaml with the contents:

adapter_class: AlphaVantage
base_symbol: USD
price_interval: monthly
fundamentals_interval: yearly
graph: false
symbol: AAPL

To run it:

(.venv) ➜  finance git:(main) ✗ ./src/intrinsic_value.py
Finance from eContriver (C) 2021 eContriver LLC
This program comes with ABSOLUTELY NO WARRANTY; for details see the GNU GPL v3.
This is free software, and you are welcome to redistribute it under certain conditions.
Last time: 2021-09-30  First time: 2017-09-30
Data table for intrinsic value calculation using TimeInterval.YEAR...
             Shares   Assets Liabilities   Equity Long Debt Short Debt    Eps  Revenue Cash Flow Net Income Dividends Low P/E High P/E      ROE
2017-09-30  5126.2m  $375.3b     $241.3b  $134.0b   $112.6b     $12.0b  $2.30  $229.2b    $64.2b     $48.4b    $12.8b   15.43    17.06   36.07%
2018-09-30  4755.0m  $365.7b     $258.6b  $107.1b   $112.0b     $12.0b  $2.97  $265.6b    $77.4b     $59.5b    $13.7b   17.51    18.68   55.56%
2019-09-30  4443.2m  $338.5b     $248.0b   $90.5b   $117.8b   $5980.0m  $2.98  $256.6b    $69.4b     $55.3b    $14.1b   16.81    18.64   61.06%
2020-09-30    17.0b  $323.9b     $258.5b   $65.3b   $125.9b   $4996.0m  $3.27  $271.6b    $80.7b     $57.4b    $14.1b   31.24    41.81   87.87%
2021-09-30    16.4b  $351.0b     $287.9b   $63.1b   $118.7b   $6000.0m  $5.62  $363.2b   $104.0b     $94.7b    $14.5b   25.07    27.91  150.07%
Data table of intrinsic value predictions using TimeInterval.YEAR...
           Shares   Assets Liabilities   Equity Long Debt Short Debt    Eps  Revenue Cash Flow Net Income Dividends Low P/E High P/E      ROE
2022-09-29  16.4b  $323.8b     $286.8b   $37.0b   $125.2b   $2513.8m  $5.51  $359.3b   $104.0b     $90.2b    $15.0b   31.11    38.25  156.13%
2023-09-28  16.4b  $314.8b     $296.1b   $18.7b   $127.8b    $628.4m  $6.20  $386.6b   $112.2b     $99.2b    $15.3b   34.40    42.72  182.07%
2024-09-26  16.4b  $305.8b     $305.4b  $356.7m   $130.4b      $0.00  $6.89  $413.9b   $120.5b    $108.2b    $15.7b   37.69    47.19  208.01%
2025-09-25  16.4b  $296.7b     $314.7b  $-18.0b   $133.0b      $0.00  $7.58  $441.2b   $128.8b    $117.2b    $16.1b   40.98    51.66  233.95%
2026-09-24  16.4b  $287.7b     $324.0b  $-36.3b   $135.6b      $0.00  $8.27  $468.5b   $137.0b    $126.3b    $16.5b   44.27    56.13  259.89%
book value: current=$63.1b (on 2021-09-30) | prediction=$-36.3b (for 2026-09-24)
dividend: payout=$14.5b (on 2021-09-30) | div/share(DPS)=$0.88 | DPS/EPS=15.67% | retained=84.33%
book: value(BVPS)=$3.84 (on 2021-09-30) | EPS/BVPS(yield)=146.33% | yield*retained(growth)=123.40%
dividends: 2022-09-29: $0.86  2023-09-28: $0.97  2024-09-26: $1.08  2025-09-25: $1.19  2026-09-24: $1.30  
P/Es: max=73.05 avg=46.03 med=36.19 min=32.49 | ROE=259.89%
future prices (P/Es * future EPS $8.27): max=$604.41 avg=$380.84 med=$299.38 min=$268.83 (62.23%) | ROE= (2026-09-24)
last IRR from $141.11 (2021-09-30): max=27.8338% avg=18.4508% med=13.8482% min=11.8506% on 2026-09-24
current IRR from $167.30 (2022-02-18): max=24.224% avg=15.1001% med=10.6243% min=8.6815% on 2026-09-24
fair prices (P/Es * current EPS $5.62): max=$410.56 avg=$258.69 med=$203.36 min=$182.61 (91.62%) (2022-02-18)
price/book=43.56 | price/earnings=29.77 | price/cash flow=26.42
debt/equity=197.65% | liabilities/assets=82.03% | debt/assets=35.53%
-- End report for AAPL
Report file: file:///home/nikc/projects/finance/output/intrinsic_value_runner/AAPL_yearly_AlphaVantage.log
>> Running took: 0:01:10.004127s

Tool List

Intrinsic Value Runner

Add ~/.eContriver/intrinsic_value.yaml:

adapter_class: AlphaVantage
base_symbol: USD
price_interval: monthly
fundamentals_interval: yearly
#fundamentals_interval: quarterly
asset_type_overrides:
  ETH: DIGITAL_CURRENCY
  LTC: DIGITAL_CURRENCY
  NET: EQUITY
  GEO: EQUITY
  BLK: EQUITY
  WDC: EQUITY
  NOBL: EQUITY
  AI: EQUITY
graph: false
symbol:

Run:

(.venv) ➜  finance git:(main) ✗ ./src/intrinsic_value.py

Alert Runner

Add ~/.eContriver/alert.yaml:

base_symbol: USD
adapter_class: AlphaVantage
#price_interval: hourly
price_interval: daily
asset_type_overrides:
  ETH: DIGITAL_CURRENCY
  LTC: DIGITAL_CURRENCY
  GEO: EQUITY
  BLK: EQUITY
close_boundaries:
  AAPL: [105.00, 120.00]
  AMZN: [2700.00, 2800.00]
  GOOG: [2000.00, 2200.00]
  DIS: [150.00, 190.00]
  PYPL: [260.00, 300.00]
  TSLA: [600.00, 700.00]
  ETSY: [150.00, 200.00]
  ENPH: [150.00, 170.00]
  RDFN: [60.00, 70.00]
  ABNB: [150.00, 170.00]
  PLTR: [22.00, 23.00]

Run:

(.venv) ➜  finance git:(main) ✗ ./src/alert.py

Multi-Symbol Runner

Add ~/.eContriver/multi_symbol.yaml:

adapter_class: AlphaVantage
base_symbol: USD
price_interval: daily
graph: false
start_time: 2019-07-23
#end_time: 2021-07-23
asset_type_overrides:
  ETH: DIGITAL_CURRENCY
  LTC: DIGITAL_CURRENCY
  GEO: EQUITY
  BLK: EQUITY
symbols:
  # Crypto dance...
#  - BTC
#  - ETH
  - XLK  # Technology
  - XLE  # Energy

Run:

(.venv) ➜  finance git:(main) ✗ ./src/multi_symbol.py

Symbol Runner

Add ~/.eContriver/symbol.yaml:

base_symbol: USD
adapter_class: AlphaVantage
#price_interval: hourly
price_interval: daily
asset_type_overrides:
  ETH: DIGITAL_CURRENCY
  LTC: DIGITAL_CURRENCY
  DASH: EQUITY
  GEO: EQUITY
  BLK: EQUITY
graph: false
#graph: true
report_types:
  - BUY_AND_HOLD
  - LAST_BOUNCE
  - BUY_DOWN_SELL_UP_TRAILING
  - BUY_UP_SELL_DOWN_TRAILING
  - SOLDIERS_AND_CROWS
  - BOUNDED_RSI
  - MACD_CROSSING
symbol: BTC

Run:

(.venv) ➜  finance git:(main) ✗ ./src/symbol.py

Trend Runner

Add ~/.eContriver/trend.yaml:

base_symbol: USD
adapter_class: AlphaVantage
price_interval: hourly
#price_interval: daily
asset_type_overrides:
  ETH: DIGITAL_CURRENCY
  LTC: DIGITAL_CURRENCY
  DASH: EQUITY
  GEO: EQUITY
  BLK: EQUITY
report_types:
  - INTEREST_OVER_TIME

  - RELATED_QUERY
  - RELATED_TOPIC
graph: true
terms: ['hood stock price']
symbol: HOOD

Run:

(.venv) ➜  finance git:(main) ✗ ./src/trend.py

Adapter List

WARNING: The code has changed a lot recently and not all the adapters have been updated yet!

The adapters that were working at one point are:

AlphaVantage
Binance
CoinbasePro
FinancialModelingPrep
IexCloud
Kraken
Quandl
Robinhood
TDAmeritrade
Yahoo

Only the brokerage/exchange endpoints support orders i.e.

Binance
CoinbasePro
Kraken
Robinhood
TDAmeritrade

Getting Started

Linux Development

git clone git@github.com:eContriver/finance.git
cd finance

setup environment (using poetry to have per project python version support)

sudo apt update
sudo apt install python3 python3-pip
pip install poetry

poetry shell

NOTE: Some dependencies have been found to not build with Python 3.10.+ and 3.9 has not been tested. Currently, Python 3.8 is known to be working with all dependencies.

install dependencies

sudo apt update

sudo apt -y install python-wxtools unzip wget libnss3 

# This will get the binaries instead of build from source which takes FOREVER
pip install -f https://extras.wxpython.org/wxPython4/extras/linux/gtk3/ubuntu-20.04/ wxPython

pip install -r requirements.txt

WHAT HAPPENS IF I HAVE MULTIPLE PYTHON VERSIONS INSTALLED?```

(.venv) dro@dro-ThinkPad-L15-Gen-2a:~/SyncDropbox/SyncDropbox/CodeSpace/PYTHON/finance$ poetry env use 3.8
Recreating virtualenv finance in /home/dro/SyncDropbox/SyncDropbox/CodeSpace/PYTHON/finance/.venv
Using virtualenv: /home/dro/SyncDropbox/SyncDropbox/CodeSpace/PYTHON/finance/.venv

(.venv) dro@dro-ThinkPad-L15-Gen-2a:~/SyncDropbox/SyncDropbox/CodeSpace/PYTHON/finance$ which python
/home/dro/SyncDropbox/SyncDropbox/CodeSpace/PYTHON/finance/.venv/bin/python

(.venv) dro@dro-ThinkPad-L15-Gen-2a:~/SyncDropbox/SyncDropbox/CodeSpace/PYTHON/finance$ python
Python 3.8.13 (default, Aug 22 2022, 23:28:40) 
[GCC 11.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> exit()

start IDE
```shell
pushd ~/.pycharm/ && (nohup /opt/pycharm-community-2022.1.3/bin/pycharm.sh ~/projects/finance &); popd

For new projects

To build the new environment start with:

poetry init

Windows Development

Windows WSL2 Development (w/ X11 forwarding)

If on Windows 10 Home you might need to add your user to the docker-users group:

net localgroup docker-users "your-user-id" /ADD

Install an X11 display tool, some options are:

Xming X Server for Windows
MobaXterm

First setup your ssh keys:

docker volume create root_home
docker run --rm -it -v root_home:/root python:3.8.8 ssh-keygen
docker run --rm -it -v root_home:/root python:3.8.8 cat /root/.ssh/id_rsa.pub

Copy this output and add it to your GitHub SSH keys:

Top-right Profile Icon | Settings | SSH and GPG Keys | Add New SSH Key

Title: root@finance_<hostname> Key: <paste the content of the id_rsa.pub>

_NOTE: The contents of root_home can be viewed on Windows using WSL2 at \\wsl$\docker-desktop-data\version-pack-data\community\docker\volumes\root_home\_data and on Linux at /var/lib/docker/volumes/root_home. This area can be used for multiple projects so that you don't have to have a separate ssh key for each project._

Cloning the repo

Create a volume mount on the host machine (works for BASH, CSH, and PowerShell):

docker volume create --name finance --opt type=none --opt device=$PWD --opt o=bind

Or create a volume mount on NFS using a CIFS path:

docker volume create --name finance --opt type=cifs --opt device=//192.168.2.235/private/projects/finance --driver local --opt o=username=user,password=pw

On Windows either of these solutions may perform slowly, using a named volume uses a different file system and may be faster:

docker volume create finance

If you make a mistake, then you can always clean these up with:

docker volume rm finance

Now clone the repo into the finance volume:

docker run --rm -it -v root_home:/root -v finance:/git alpine/git clone git@github.com:eContriver/finance.git .

NOTE: The contents of finance can be viewed on Windows using WSL2 at \\wsl$\docker-desktop-data\version-pack-data\community\docker\volumes\finance\_data and on Linux at /var/lib/docker/volumes/finance

Running from Docker

cd <clone location>
docker compose run dev
./src/intrinsic_value.py

Managing Secrets

NOTE: The above command only work if you have enable Docker Compose v2, if you have not then just add the - between docker and compose.

You only need to provide the secrets that you intend to use. These secrets are currently handled with environment variables.

In a ~/.bash_profile, ~/.zshrc, etc.

export DISPLAY='192.168.1.1:0.0'
export ALPHA_VANTAGE_API_KEY='?'
export ROBINHOOD_USERNAME='<email>'
export ROBINHOOD_PASSWORD='?'
# Nickname: ?
export BINANCE_API_KEY='?'
export BINANCE_SECRET='?'
# Nickname: ?
export COINBASE_API_KEY='?'
export COINBASE_PASSPHRASE='?'
export COINBASE_SECRET='?'
# export KRAKEN_API_KEY='?'
# Description: ?
# API Key: ?
# Private: ?
# Docs: https://apisb.etrade.com/docs/api/account/api-account-v1.html
export ETRADE_OAUTH_CONSUMER_KEY='?'
export ETRADE_CONSUMER_SECRET='?'
# Docs: https://financialmodelingprep.com/developer/docs/
export FMP_API_KEY='?'
# Docs: https://www.quandl.com/tools/api
export QUANDL_API_KEY='?'
# Docs: https://iexcloud.io/docs/api/
export IEX_CLOUD_API_KEY='?'

Adding python dependencies

# Install the thing
pip install requests
# Preserve the dependencies
pip freeze | tee requirements.txt

Install all dependencies

pip install -r requirements.txt

Upgrade individual

pip3 install --upgrade tensorflow

Upgrading all

pip3 list --outdated --format=freeze | grep -v '^\-e' | cut -d = -f 1 | xargs -n1 pip3 install -U

Upgrade to a specific version (can use ==)

pip install typing-extensions~=3.7.4

Building new images

To install a new OS dependency, or an image with new pip dependencies it is good to first test the build before modifying or messing with the latest. Only after it is working should it over ride the latest. To attempt to add a new version - look-up the existing version adn then bump the version:

export version=0.0.2
docker-compose run dev

This will attempt to use the version 0.0.2 image and because it doesn't exist it will have to be created.

If you tried to do this without having the version set, then it would just use the latest image and it wouldn't even try to build.

Testing

Sequential

The built-in unittest runner can be invoked here

PYTHONPATH=~/projects/finance/src python -m unittest discover ./src/test

NOTE: That when using PyCharm the root source path is automatically added to PYTHONPATH.

Individual tests can be run using:

PYTHONPATH=~/projects/finance/src python -m unittest discover ./src/test -k test_sma_up.TestSmaUpStrategy.test_sma_up_is_accurate

The built-in unittest runner can also be invoked here with custom configuration:

pushd ./src/ && ./test.py; popd

Parallel

To test in parallel use:

pip install unittest-parallel
cd /app
unittest-parallel -t ./src/ -s ./src/test/ -v

In PyCharm this can be setup using a configuration with runs script:

Script path

    /usr/local/bin/unittest-parallel

Parameters

-t ./src/ -s ./src/test/ -v

For PyCharm run test button next to each test to work it should be using unittests as the testing framework. PyCharm will try to auto-detect the frame work and in the past it has failed and selected Twisted Trial. To fix this change it back to Unittests here:

File | Settings | Tools | Python Integrated Tools

The error message was:

OSError: [Errno 95] Operation not supported: '12501' -> '/app/src/test/runners/_trial_temp.lock

The Twisted Trial runner: _jb_trialtest_runner.py The Unittests runner: _jb_unittest_runner.py

Coverage

Working flow is to run this as you work and make sure you are maintaining coverage.

unittest-parallel -t ./src/ -s ./src/test/ --coverage --coverage-rcfile .coveragerc --coverage-html ./coverage

Profiling

cProfile from argument

The Launcher class which is used to start most of the command-line tools has an option '-p' or '--profile' which allows you to profile the entire session. This will just generate a profile report at the end.

The Profile class can also be used to profile a small section of code, but it does require that you edit the code.

Profiler.get_instance().enable()
<code to profile>
Profiler.get_instance().disable_and_report('/tmp/profile.log', 'cumtime')
Profiler.get_instance().dump_stats('/tmp/profile.prof')

cProfile from command-line

pip install CProfileV
/usr/local/bin/python -m cProfile /app/src/main.py

pip install CProfileV
/usr/local/bin/python -m cProfile -o /app/profile /app/src/main.py
cprofilev -f /app/profile

cProfile from command-line with KCacheGrind (a visualization tool)

python -m cProfile -o multi_symbol.prof multi_symbol.py
pip install pyprof2calltree
apt-get install kcachegrind
pyprof2calltree -k -i multi_symbol.prof

Large Reruns

To rerun the YAMLs that exist use:

find /app/output/intrinsic_value_runner/ -name "*.yaml" | xargs -n 1 -P 1 /app/src/intrinsic_value.py -i

To compare a line from these files:

grep "current IRR" *.log | grep -v nan | grep yearly

Example output

STDOUT - Symbol Runner

Used to test strategies with different settings against a single stock

== Strategy Comparison - Final Results
                                     Buy and Hold:     264.12 % (records:1000)
                              SMA 200 crossing 50:     165.53 % (records:1000)
                             Buy 0.9 and Sell 1.2:     107.36 % (records:1000)
                             Buy 0.8 and Sell 1.4:      96.00 % (records:1000)
    Buy 0.8 -> 1.05 and Sell 1.4 -> 0.95 Trailing:     346.90 % (records:1000)
    Buy 0.9 -> 1.03 and Sell 1.2 -> 0.97 Trailing:     306.07 % (records:1000)

STDOUT - Matrix Runner

Used to test a set of strategies with settings across a bunch of different securities.

Strategies - ALL PASSED (260/260)
== Strategies Across Symbols CAGR
                : Buy and Hold
                :               Last bounce ratio 0.5 threshold 0.05
                :                             Buy down 0.6 Sell up 1.3 Trailing
                :                                           Buy up 1.6 Sell down 0.6 Trailing
                :                                                         3 white soldiers and black crows
           AAPL :        19.90 %        4.12 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 1999-11-01 00:00:00)
           MSFT :        33.36 %        0.00 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 1999-11-01 00:00:00)
           NVDA :        61.15 %       30.77 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 1999-11-01 00:00:00)
           INTC :         5.97 %        0.00 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 1999-11-01 00:00:00)
           AVGO :        39.71 %        0.00 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 2009-08-06 00:00:00)
            AMD :         4.59 %      -11.71 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 1999-11-01 00:00:00)
           XLNX :        21.73 %      -19.41 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 1999-11-01 00:00:00)
            TSM :        54.36 %      -13.49 %        0.00 %      -25.65 %        0.00 % (data 2021-06-18 00:00:00 to 1999-11-01 00:00:00)
           GOOG :       111.34 %        0.00 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 2014-03-27 00:00:00)
             FB :        40.42 %        0.00 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 2012-05-18 00:00:00)
             VZ :        -1.32 %        0.00 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 1999-11-01 00:00:00)
              T :        15.30 %        7.08 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 1999-11-01 00:00:00)
           NFLX :        -7.69 %        0.00 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 2002-05-23 00:00:00)
            DIS :        63.04 %        3.24 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 1999-11-01 00:00:00)
           AMZN :        12.60 %        0.00 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 1999-11-01 00:00:00)
           TSLA :        73.73 %      -25.93 %       24.00 %      -31.03 %        0.00 % (data 2021-06-18 00:00:00 to 2010-06-29 00:00:00)
             HD :        12.18 %        0.00 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 1999-11-01 00:00:00)
              V :        26.89 %        0.00 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 2008-03-19 00:00:00)
             MA :        16.35 %        0.00 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 2006-05-25 00:00:00)
            AXP :        92.69 %       55.75 %        0.00 %        1.35 %        0.00 % (data 2021-06-18 00:00:00 to 1999-11-01 00:00:00)
            JPM :        82.89 %       44.45 %        0.00 %      -13.15 %        0.00 % (data 2021-06-18 00:00:00 to 1999-11-01 00:00:00)
            BAC :       112.05 %       71.74 %        0.00 %        0.19 %        0.00 % (data 2021-06-18 00:00:00 to 1999-11-01 00:00:00)
              C :       103.42 %       53.26 %        0.00 %       -1.64 %        0.00 % (data 2021-06-18 00:00:00 to 1999-11-01 00:00:00)
            WFC :       153.22 %       -1.18 %        0.00 %       25.84 %        0.00 % (data 2021-06-18 00:00:00 to 1999-11-01 00:00:00)
          BRK-B :        49.63 %        0.00 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 1999-11-01 00:00:00)
            AIG :        97.34 %       72.27 %        0.00 %      -12.53 %        0.00 % (data 2021-06-18 00:00:00 to 1999-11-01 00:00:00)
           TTCF :       -17.83 %      -42.97 %      112.94 %      -48.31 %        0.00 % (data 2021-06-18 00:00:00 to 2018-09-12 00:00:00)
           BYND :       -30.45 %       -9.14 %        3.62 %      -49.10 %        0.00 % (data 2021-06-18 00:00:00 to 2019-05-02 00:00:00)
            TLT :       -11.71 %        0.00 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 2002-07-26 00:00:00)
           EXPI :        32.54 %      343.91 %        5.14 %       64.49 %        0.00 % (data 2021-06-18 00:00:00 to 2014-02-19 00:00:00)
             DE :        62.17 %       55.51 %        0.00 %      -19.14 %        0.00 % (data 2021-06-18 00:00:00 to 1999-11-01 00:00:00)
           UHAL :        82.21 %        0.00 %        0.00 %      -14.98 %        0.00 % (data 2021-06-18 00:00:00 to 1999-11-01 00:00:00)
             SQ :        41.61 %       27.77 %        0.00 %      -23.73 %        0.00 % (data 2021-06-18 00:00:00 to 2015-11-19 00:00:00)
           PYPL :        67.46 %      -15.78 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 2015-07-20 00:00:00)
           SNOW :         1.47 %       -2.07 %       -1.21 %      -53.64 %        0.00 % (data 2021-06-18 00:00:00 to 2020-09-16 00:00:00)
           NVTA :       -44.57 %      -34.82 %      -16.46 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 2015-02-12 00:00:00)
           FROG :       -55.08 %      -13.62 %      -25.92 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 2020-09-16 00:00:00)
           CCIV :       275.47 %      751.48 %       -0.50 %      305.38 %        0.00 % (data 2021-06-18 00:00:00 to 2020-09-18 00:00:00)
           ARKW :        36.23 %       -5.36 %        0.00 %      -34.75 %        0.00 % (data 2021-06-18 00:00:00 to 2014-09-30 00:00:00)
           ARKQ :        49.01 %       -7.51 %        0.00 %      -25.97 %        0.00 % (data 2021-06-18 00:00:00 to 2014-09-30 00:00:00)
           ARKF :        35.70 %       -1.75 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 2019-02-04 00:00:00)
           ARKK :        27.48 %       -7.28 %        0.00 %      -34.73 %        0.00 % (data 2021-06-18 00:00:00 to 2014-10-31 00:00:00)
           ARKG :        28.38 %       43.46 %        0.00 %      -33.53 %        0.00 % (data 2021-06-18 00:00:00 to 2014-10-31 00:00:00)
           PRNT :        70.70 %       -4.26 %        0.00 %      -18.07 %        0.00 % (data 2021-06-18 00:00:00 to 2016-07-19 00:00:00)
           IZRL :        23.34 %        0.00 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 2017-12-05 00:00:00)
           SOXX :        52.23 %        0.00 %        0.00 %        0.00 %        0.00 % (data 2021-06-18 00:00:00 to 2001-07-13 00:00:00)
           CMPS :        -3.34 %        1.17 %       -2.73 %      -51.97 %        0.00 % (data 2021-06-18 00:00:00 to 2020-09-18 00:00:00)
           MNMD :       547.74 %      704.23 %       81.92 %       87.22 %        0.00 % (data 2021-06-18 00:00:00 to 2020-03-03 00:00:00)
            BTC :       413.54 %       54.26 %      -13.30 %      149.30 %        0.00 % (data 2021-06-21 00:00:00 to 2018-09-26 00:00:00)
            ETH :      1282.90 %     1134.22 %      -21.85 %      722.36 %        0.00 % (data 2021-06-21 00:00:00 to 2018-09-26 00:00:00)
            LTC :       465.35 %      647.02 %      -50.76 %      324.63 %        0.00 % (data 2021-06-21 00:00:00 to 2018-09-26 00:00:00)
           DOGE :    103066.29 %     7427.59 %      127.17 %     3439.62 %        0.00 % (data 2021-06-21 00:00:00 to 2019-07-05 00:00:00)
        Average :      2072.96 %      217.63 %        4.27 %       89.01 %        0.00 %
           Wins :           39             9             2             2             0
>> Running took: 0:01:21.022097s

Graphical

...images to come soon...

Proposal for new Adapter design

To get the data we need to connect the ValueType url end-point query params (apikey, args, etc.) the key to the location of the ValueType a conversion algorithm

Proposal for new Visualizer design

Given the canned data in a time-series format graph as: bar, line Given the canned data of high, low, open, close graph as: candle-stick Add ability to

draw annotations on any
select if multiple are added to any one plot with a legend
select if multiple are added to a single window

Planned

Play with

6/10 best days for the market came right after the worst 2 weeks

Check for 2 weeks of down trend across the market or a sector
Model buying here with an exponential increase

Exponential increase

at 50% dip invest 99.999% of cash
at 1% dip invest 2% or something similar
these are percents of percents, so 2% + 4% the cash pool is getting smaller adn these percents are not the same as the percent when the strategy starts, try to see if we can compensate for that

Narrowing Yield Curves

generally indicative of an on coming recession or a slowing of growth
sharp decline in bonds
- the 5 year yields are going up and 30 year yields are going down June 18 2021
- the 5 year prices are going down and 30 year prices are going up June 18 2021
Powell thinks we have more disk of seeing disinflation
- commodities like copper have been going down for ~20 years and in the last 6-8 weeks we just broke out of that and trended up for the first in a long time, commodities are just now (June 19th 2021) going down again

Crypto leverage

it is expected that because of no regulation cryptos are highly leveraged... what does this mean?
- is crypt trade on margin?
- is more than a 9/10 lent out?

Identify highly shorted

After GME heavily shorted companies will probably do well
In addition to this, we need to be able to see how many shares were available
For the short position to be open someone has to buy the stock and hold it so that someone else can short it if the number of available shares is low, then it's easier to buy through the resistance levels and push the stock higher and higher, the compound that with the short squeeze and wow

Add page generation for:

Bloomberg Terminal: Short interest
Insider trading: http://openinsider.com/
Bond maturity dates and prices - yield to maturity vs risk : finra
Debt Exchange ?: ?
Leverage exposure/risk: ?
What are the whales buying: whalewisdom
Read SEC information filings: sec.gov and openinsider.com
- https://notebooks.gesis.org/binder/jupyter/user/janlukasschroed-python-tutorial-fadn373a/notebooks/SEC%2BAPI%2BDemo.ipynb
- https://sec-api.io/docs
Filters like market cap 100m - 700m for huge growth
Time based trailing limit adjustment - didn't get my sell in the last 3 days, adjust to the new high/low (already just on 1 day anyways, right?)
From X number of trailing days buy 10 on 5% dip buy using 10% of cash, 10% dip 20% of cash, 20% dip 40%, 30% dip 80%
Sell on high short interest buy on low short interest
Buy on high (6 sigma) insider buying and sell on high insider selling: edgar adapter
- https://iexcloud.io/docs/api/#insider-transactions
Momentum, day5ChangePercent...year5ChangePercent
- https://iexcloud.io/docs/api/#key-stats
Checkout Quandl
For 10 years+ of balance sheets checkout: https://financialmodelingprep.com/
Show margin debt for all of the traders and companies or in a macro way
- https://www.advisorperspectives.com/dshort/updates/2021/05/19/margin-debt-and-the-market-up-another-3-in-april-continues-record-trend
- This doesn't account for the equity in cryptos - $1.6T: https://coinmarketcap.com/
Company debt is way up in May 2021, can we view this versus
Average and aggregate all Future P/E estimates from all stocks in say the S&P 500 and see if the average estimate if the future is trending up or down

Real Estate research

Down payments size/percentages
Interest rates
Eviction Percentages
- Mortgage forbearance
- People holding in forbearance, how much equity do they have?
What is the current equity rate?
What is the appreciation rate?
Inventory
Who is getting loans and who is refinancing?
- Credit scores
- Income
Who is looking to buy?
- What are their credit scores?
- Income
- Is there enough of them to absorb a flood of supply?

Tax Services

Pay Taxes on the Spot for every crypto transaction = $100/year ?BTC/year ?ETH/year $10/month
- Give us your wallet addresses and we'll calculate what you owe on taxes
- Partner with: https://giveth.io/project/support-protests-in-colombia-soscolombia o To auto write-off donations
- Suggest donations of things that would off-set taxes
Save current Crypto prices and record from that point forward
...
How does the price of a house in Bitcoin change over time?
How does the price of a house in Gold change over time?
How does the price of a house in Ethereum change over time?

Calendar

Unemployment runs out in September Evictions Tapering Etc.

Stimulus payments with

Consumer spending
Stock prices

Service Signups

Sign-up for IEX Cloud: https://iexcloud.io/s/becb5771

Social Media

Social Media to Follow
- https://www.reddit.com/user/Criand/
- DFV

Careers

https://www.rentec.com/Careers.action?computerProgrammer=true

Notes

Normal Orders

Order Types       - market: $50
market            - buy: $50 (market)   sell: $50 (market)
stop-loss         - buy: $55 -> market  sell: $45 -> market (a.k.a. stop order)
limit             - buy: $45 (or less)  sell: $55 (or more)
stop-limit        - buy(stop: $55 limit: $60): $55 -> $60 (or less)
                    sell(stop: $45 limit: $40): $45 -> $40 (or more)
trailing stop     - buy(trailing: $5): low + $5 -> market
                  - sell(trailing: $5): high - $5 -> market
one-cancels-other - limit + stop-limit

For buy imagine stock closes one day $45 and then opens at $65

stop-loss would execute at $65
stop-limit would create a limit order at $60, but would not execute

For sell imagine stock closes one day $55 and then opens at $35

stop-loss would execute at $35
stop-limit would create a limit order at $40, but would not execute

Had to sync Windows host to get docker and wsl time to be accurate, but that wasn't enough

pip install --editable git+https://github.com/sammchardy/python-binance#egg=python-binance python-binance

Infra

Install pycharm into /root persistent local volume

Start and connect to docker container with root home mounted:

docker run --rm -it -v root_home:/root python sh

Then install pycharm there:

curl https://download.jetbrains.com/python/pycharm-community-2022.1.3.tar.gz > /tmp/file_to_extract
tar -xzf /tmp/file_to_extract -C /root/
rm -rf /tmp/file_to_extract
mv /root/pycharm-community-*/ /root/pycharm-community/

To test PyCharm run with:

/root/pycharm-community/bin/pycharm.sh

If that fails with messages about libXtst, then do this:

sudo apt-get update
sudo apt-get install libxtst6

Then locate the Dockerfile installed pip packages by switching the interpreter to:

/usr/local/bin/python

Run without cython for debugging (if not hitting breakpoints otherwise):

PYDEVD_USE_CYTHON=NO /root/pycharm-community-2020.3.3/bin/pycharm.sh /app

Install a browser like Chrome with

cd /root
curl https://dl.google.com/linux/direct/google-chrome-stable_current_amd64.deb
apt update

apt install google-chrome-stable_current_amd64.deb
/usr/bin/google-chrome-stable --no-sandbox

# Didn't work to install to root dir
#dpkg-deb -x /root/google-chrome-stable_current_amd64.deb /root/google-chrome/

# To prepend --no-sandbox
cat ~/chrome.sh
#!/bin/bash
/usr/bin/google-chrome --no-sandbox $@

WSL start up

Using WSL 2 on Windows 10

Use the Ubuntu 20.04 LTS (solid black background):

sudo mount -t drvfs S: /mnt/s

Launching via docker from within the WSL 2 shell:

PyCharm IDE logs and fixes

Hyperlinks in console stop working

Regular hyperlinks like https://google.com only work if you have a browser setup in the container (many do not).

File hyperlinks only work in Python console. To run and debug from the Python console then edit the run configuration > [x] (check) Run with Python console

libnss3

Logs are located here

vi /root/.cache/JetBrains/PyCharmCE2020.3/log/idea.log

One error:

2021-03-01 12:33:17,322 [   6969]  ERROR - #com.intellij.ui.jcef.JBCefApp - /root/pycharm-community-2020.3.3/jbr/lib/libjcef.so: libnss3.so: cannot open shared object file: No such file or directory
java.lang.Throwable: /root/pycharm-community-2020.3.3/jbr/lib/libjcef.so: libnss3.so: cannot open shared object file: No such file or directory

Clicking link takes you to:

https://intellij-support.jetbrains.com/hc/en-us/articles/360016421559

Reported missing years:

12:33 PM    JCEF browser component failed to start
        Missing native libraries:   libnss3.so,     libnssutil3.so,     libsmime3.so,   libnspr4.so,    libasound.so.2

Looking for missing libs:

ldd /root/pycharm-community-2020.3.3/jbr/lib/libjcef.so | grep "not found"

apt-get install -y unzip wget libnss3

That seems to have fixed it

No connection

Debugger magically broke...

2021-03-01 13:17:44,994 [ 109600]  ERROR - debugger.pydev.AbstractCommand - No connection (command:  113 )
java.lang.Throwable: No connection (command:  113 )
        at com.intellij.openapi.diagnostic.Logger.error(Logger.java:159)
        at com.jetbrains.python.debugger.pydev.AbstractCommand.execute(AbstractCommand.java:159)
        at com.jetbrains.python.debugger.pydev.RemoteDebugger.evaluate(RemoteDebugger.java:158)
        at com.jetbrains.python.debugger.pydev.MultiProcessDebugger.evaluate(MultiProcessDebugger.java:167)

This sounds to be related to __str__ conversions which has just been changed, but was able to find another workaround which is to go to:

File | Settings | Build, Execution, Deployment | Python Debugger

Enable: Gevent compatible

Concepts

DataAdapters - Interfaces to data bases/services/etc. A single dataAdapter
               can be used with multiple symbolAdapters but sometimes there are duplicates - the cache helps here.
SymbolAdapters - Owns dataAdapters for a symbol and has interval, current time, as well as
                 historic price data for that symbol
SymbolCollection - A collection of symbolAdapters referenced by symbol
                   and tracks the baseSymbol
Portfolio - Represents quantities of symbols which tracks it's value over time
            The only representation of 'today' is the getLastCompletedTime which is really now - 1
            as we have not yet calculated the current day - where we are

Architecture

Portfolio Topology

Data               Symbol         Symbol
Adapters           Adapters       Collection    Portfolio

                            +---> USD <---+---> USD
(Broker A) <--+--> GOOG <---+             +---> GOOG
              +--> BTC  <---+             +---> BTC
(Broker B) <-----> DOGE <---+             +---> DOGE

Adapter Topology

Data            Data            Symbol
Response        Adapter         Adapter
                  +-----------

ValueType                            QueryType
HIGH, LOW, OPEN, CLOSE    <----->    SERIES
RSI                       <----->    RSI
SMA                                  SMA

User can pick data provider

For one stock a user may want series from provider A and MACD from another provider Each of these requires a different data adapter, but we should store under the symbol adapter for one symbol

The client API to get value:

collection.get_value(symbol, instance, value_type)

When the user requests a value it should traverse like:

SymbolCollection()
collection[symbol] -> SymbolAdapter()  # value_type -> query_type
                      symbolAdapter[query_type] -> DataAdapter(symbol)  # type should represent query_type
                                                   data_adapter.get_value(instance, value_type)

Construction:

symbol_types = {'ETH': SymbolType.DIGITAL_CURRENCY}
base_symbol = 'USD'
series_data_adapter: SeriesAdapter = AlphaVantageAdapter('AMD', base_symbol)
series_data_adapter.series_interval = interval
series_data_adapter.asset_type = AssetType.EQUITY
symbol_adapter = SymbolAdapter(symbol)
symbol_adapter.data_adapters[QueryType.SERIES] = series_data_adapter
rsi_data_adapter: RsiAdapter = BinanceAdapter('AMD', base_symbol)
rsi_data_adapter.interval = interval
rsi_data_adapter.period = period
symbol_adapter.data_adapters[QueryType.RSI] = rsi_data_adapter
macd_data_adapter: MacdAdapter = BinanceAdapter()
macd_data_adapter.fast_period = fast
macd_data_adapter.slow_period = slow
macd_data_adapter.signal_period = signal
symbol_adapter.data_adapters[QueryType.MACD] = macd_data_adapter
collection = SymbolCollection()
collection.baseSymbol = 'USD'  # propagate to all data adapters on set?
collection.add_symbol_adapter(symbol_adapter)

References

List of lots of quant APIs:

https://github.com/wilsonfreitas/awesome-quant#data-sources

Pandas DataReader

AlphaVantage

https://github.com/RomelTorres/alpha_vantage

eContriver / finance

readme

Overview

Features

Single Stock Strategies

Multi stock strategy

Strategy optimization

Build table of RSI and MACD and use it to predict the next relative price movement of a stock

Integrated ML/AI to make price model predictor and then use it to trade

Donations

Running Tools

Tool List

Intrinsic Value Runner

Alert Runner

Multi-Symbol Runner

Symbol Runner

Trend Runner

Adapter List

Getting Started

Linux Development

For new projects

Windows Development

Windows WSL2 Development (w/ X11 forwarding)

Cloning the repo

Running from Docker

Managing Secrets

Adding python dependencies

Install all dependencies

Upgrade individual

Upgrading all

Upgrade to a specific version (can use ==)

Building new images

Testing

Sequential

Parallel

Coverage

Profiling

cProfile from argument

cProfile from command-line

cProfile from command-line with KCacheGrind (a visualization tool)

Large Reruns

Example output

STDOUT - Symbol Runner

STDOUT - Matrix Runner

Graphical

Proposal for new Adapter design

Proposal for new Visualizer design

Planned

Play with

Identify highly shorted

Add page generation for:

Real Estate research

Tax Services

Calendar

Service Signups

Social Media

Careers

Notes

Normal Orders

Infra

Install pycharm into /root persistent local volume

Run without cython for debugging (if not hitting breakpoints otherwise):

Install a browser like Chrome with

WSL start up

PyCharm IDE logs and fixes

Hyperlinks in console stop working

libnss3

No connection

Concepts

Architecture

Portfolio Topology

Adapter Topology

User can pick data provider

References