VOCABURARY

MINUTE BAR - Real time 1 minute streaming data from Alpaca. It only sends data that has changed. ACTIVE BAR - List of stocks that were changed. REALTIME TRADE DATA. Real time trade data. We have to subscribe/unsubscribe per individual stock. REALTIME 1 MIN BAR. One minute data stream from alpaca. Subscirbe once for all stocks. REALTIME 2 MIN BAR. Use the redis timeseries automatic aggregation. Use 1 Min. REALTIME 5 MIN BAR. Use the redis timeseries automatic aggregation. Use 1 Min. SUBSCRIBE - Hash table. Real-Time trade data subscription list. List of company symbols. UMSUBSCRIBE - Hash table. Real-Time trade data unsubscribe list. List of company symbols. STACK - Hash. It keeps a list of companies (symbols) that has price moment that meets the initial condition of the three bar play. SCORE - Hash. This keeps a list of companies that are being actively scored by the system. It has to be in the "Stack" before it can appear on the Score.

KEY FEATURES

MinuteBarStream.MinuteBarStream() - class This module handles the real-time 1 minute bar, and live real-time trade of selected stocks.
It also creates redis timeseries tables for stock data.

ThreeBarCandidates.StudyThreeBarsCandidates() - class

Data Model for Stock Prices and Technical Indicators (using RedisTimeSeries)

This repository demonstrate a sample code for using RedisTimeSeries to store, aggregate/query stock prices, technical indicators and time-series data sets used by investors. These sets of scripts create various timeseries for prices and indicators. It shows how to create aggregations on top of the raw time series, and demonstrate how easily bulk time series can be ingested and queried using various RedisTimeSeries commands.

The blog that discusses this code in detail and walks through the Redis datamodel and the various Redis TimeSeries commands can be found in the references section below.

Pre-requisite

There are multiple services that offer stock prices and technical indicator data. The code presented here uses data from https://iexcloud.io/ Get a trial account in iexcloud.

Clone this Repository

git clone https://github.com/redis-developer/redis-datasets
cd redis-datasets/redistimeseries/StockPrice

Install Python3

Ensure that python3 and pip3 is installed in your system.

Install Prerequisite software

Using pip3 to install redistimeseries, iexfinance & pandas software.

pip3 install -r requirements.txt

Once you have the Redis TimeSeries container up and running you can connect to the server (make sure you have the right IP address or hostname) using Python script:

Running the scripts

Before running these scripts, ensure that you modify host and port number(6379) for Redis as per your infrastructure setup.

 % python3 ThreeBarCandidates.py
 % python3 ThreeBarScore.py

TS.QUERYINDEX - working command

ts.queryindex INDICATOR=max TIMEFRAME=1MIN

Running REDIS-TIMESERIES in a DOCKER Container

sudo service redis stop
sudo docker run -p 6379:6379 -it --rm redislabs/redistimeseries
redis-cli

config get maxmemory config set maxmemory 4GB config get maxmemory

REDIS-TIMESERIES local version

$ redis-server --loadmodule /home/young/Desktop/code/RedisTimeSeries/bin/redistimeseries.so

Running GRAFANA in a DOCKER Container

sudo systemctl start grafana-server sudo systemctl status grafana-server http://localhost:3000 admin Admin$11

Generate test data

python3 redisTestDataGenerator. python3 redis3bar.py

Scanning the Keys

127.0.0.1:6379> scan 0
1) "15"
2)  1) "INTRADAYPRICES15MINSTDP:GS"
    2) "DAILYRSI:CAT"
    3) "DAILYRSI15MINMAX:GS"
    4) "DAILYRSI15MINMIN:GS"
    5) "INTRADAYPRICES15MINRNG:GS"
    6) "INTRADAYPRICES15MINMIN:GS"
    7) "DAILYRSI15MINLAST:GS"
    8) "INTRADAYPRICES:GS"
    9) "DAILYRSI:GS"
   10) "INTRADAYPRICES15MINMAX:GS"
   11) "DAILYRSI15MINFIRST:GS"
   12) "DAILYRSI15MINRNG:GS"
127.0.0.1:6379> type INTRADAYPRICES15MINSTDP:GS
TSDB-TYPE
127.0.0.1:6379

References

1. study score
2. candle stick pattern
3. price-action
4. multiframe analysis
5. fibonacci https://www.youtube.com/watch?v=xU9j_MkRYfg Calculate and plot fibonacci retracement levels for an upward trending using python
6. divergence https://raposa.trade/trade-rsi-divergence-python/ RSI Divegence in Python
7. breakout
8. trend - with
9. fresh trend
10. key levels
11. vwap
12. ema50
13. news
14. total
15. volume
16. volitility
17. standard deviation

alpaca returns

quote Quote({ 'ask_exchange': 'U', 'ask_price': 20.53, 'ask_size': 1, 'bid_exchange': 'T', 'bid_price': 20.52, 'bid_size': 1, 'conditions': ['R'], 'symbol': 'DNB', 'tape': 'A', 'timestamp': 1627487138544951592}) trade Trade({ 'conditions': [' ', 'F', 'I'], 'exchange': 'T', 'id': 62879500359534, 'price': 20.52, 'size': 10, 'symbol': 'DNB', 'tape': 'A', 'timestamp': 1627487138544984019}) trade Trade({ 'conditions': [' ', 'I'], 'exchange': 'T', 'id': 62879500359535, 'price': 20.52, 'size': 28, 'symbol': 'DNB', 'tape': 'A', 'timestamp': 1627487138545036660}) quote Quote({ 'ask_exchange': 'T', 'ask_price': 20.53, 'ask_size': 2, 'bid_exchange': 'P', 'bid_price': 20.52, 'bid_size': 1, 'conditions': ['R'], 'symbol': 'DNB', 'tape': 'A', 'timestamp': 1627487138545020147}) quote Quote({ 'ask_exchange': 'P', 'ask_price': 19.32, 'ask_size': 1197, 'bid_exchange': 'U', 'bid_price': 19.31, 'bid_size': 703, 'conditions': ['R'], 'symbol': 'QID', 'tape': 'B', 'timestamp': 1627487138545929216})

bar Bar({ 'close': 136.02, 'high': 136.06, 'low': 136.0, 'open': 136.04, 'symbol': 'ALLE', 'timestamp': 1627493640000000000, 'trade_count': 22, 'volume': 712, 'vwap': 136.030153}) bar Bar({ 'close': 15.83, 'high': 15.86, 'low': 15.8218, 'open': 15.825, 'symbol': 'TLRY', 'timestamp': 1627493640000000000, 'trade_count': 327, 'volume': 64326, 'vwap': 15.841783}) bar Bar({ 'close': 53.02, 'high': 53.03, 'low': 53.0, 'open': 53.03, 'symbol': 'TNL', 'timestamp': 1627493640000000000, 'trade_count': 63, 'volume': 2730, 'vwap': 53.02548}) bar Bar({ 'close': 46.09, 'high': 46.1199, 'low': 46.09, 'open': 46.095, 'symbol': 'UBER', 'timestamp': 1627493640000000000, 'trade_count': 101, 'volume': 9629, 'vwap': 46.10465}) bar Bar({ 'close': 93.615, 'high': 94.13, 'low': 93.615, 'open': 94.01, 'symbol': 'BILI', 'timestamp': 1627493640000000000, 'trade_count': 257, 'volume': 16913, 'vwap': 93.93039}) bar Bar({ 'close': 8.565, 'high': 8.57, 'low': 8.45, 'open': 8.4584, 'symbol': 'BTBT', 'timestamp': 1627493640000000000, 'trade_count': 602, 'volume': 213907, 'vwap': 8.510506})

curl --header 'Accept: text/event-stream' https://cloud-sse.iexapis.com/stable/stocksUS\?token\=pk_4c4cea17cf834cafadd2a57e5bd7f2cc curl --header 'Accept: text/event-stream' https://cloud-sse.iexapis.com/stable/stocksUS?token=pk_4c4cea17cf834cafadd2a57e5bd7f2cc

[ (1603704600, 1.75999999999999), (1603705500, 0.775000000000006), (1603706400, 0.730000000000018), (1603707300, 0.449999999999989), (1603708200, 0.370000000000005), (1603709100, 1.01000000000002), (1603710000, 0.490000000000009), (1603710900, 0.89500000000001), (1603711800, 0.629999999999995), (1603712700, 0.490000000000009), (1603713600, 0.27000000000001) ]

TIMESERIES RANGE

ts.range data_close_1MIN:FANG 0 2000000000000000 flushall keys *

test

python3 test-realtime-data.py -t