This project uses Spark framework to run Monte Carlo simulation on historical stock prices. Historical data is read from CSV files and dift for a symbol is calculated. We then use random distribution on the last known price to estimate future prices for a set number of days throughout a number of iterations. A a broker class then selects one of these simulations for each stock symbol at random and invests money (dynamically) on them. Profits and losses are recorded at the end of the simulation in a tabular format.
The scripts in https://github.com/user501254/BD_STTP_2016.git can be used for semi-automated local Hadoop and Spark setup or for setup on AWS Amazon Elastic Compute Cloud (EC2), Google Compute Engine (GCE).
- About the Alpha Vantage API
- Some Important Files
- Application Design
- Setup Instructions
- Usage Details
- Visualization and Demo
- Map Reduce Implementation
Alpha Vantage APIs are grouped into four categories: (1) Stock Time Series Data, (2) Physical and Digital/Crypto Currencies (e.g., Bitcoin), (3) Technical Indicators, and (4) Sector Performances. For this project, we use the Stock Time Series Data to get historical prices for a given stock.
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API Documentation: https://www.alphavantage.co/documentation/
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Get Free API Key: https://www.alphavantage.co/support/#api-key
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Sample data for MSFT symbol:
timestamp,open,high,low,close,volume 2019-11-20,150.3100,150.8400,148.4600,149.6200,23118304 2019-11-19,150.8800,151.3300,150.2000,150.3900,23935700 2019-11-18,150.0700,150.5500,148.9800,150.3400,21534000 . . . 1999-11-23,89.2500,91.3750,88.3750,89.6250,70787400 1999-11-22,89.6250,90.3718,88.4380,89.8130,90596600
hw3/src/main/resources/bin
downloadsymboldata.sh bash script to download historical data for a given stock symbol (eg. MSFT)
downloadsymbolslist.sh bash script to download a list of known symbols on NASDQ NASDAQ
hw3/src/main/scala/com/ashessin/cs441/hw3/stocksim/
InvestmentBroker.scala for selecting a random simuation path and making investments
MontecarloSimulation core to Monte Carlo Simulation on stock data obtained from CSV file
RunMontecarloSimulation.scala utility object file to connect above two classes
The simulation approach is from the book "Python for Finance: Analyze Big Financial Data, Yves Hilpisch". Also, https://www.youtube.com/watch?v=3gcLRU24-w0&feature=youtu.be&t=204.
First we read the historical (closing) prices for a symbol from CSV into a DataFrame. Additional columns are computed on the fly and added to the dataframe. These are:
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change - daily stock price change for a given symbol
(price(n) - price(n-1)) -
pct_change - daily percentage change for a given symbol
(price(n) - price(n-1)) / price(n-1) -
log_returns - natural log of pct_change + 1
ln(((price(n) - price(n-1)) / price(n-1)) + 1)Table: Historical data for MSFT symbol with additional columns +----------+------+-----------+--------------------+--------------------+ | timestamp| close| change| pct_change| log_returns| +----------+------+-----------+--------------------+--------------------+ |1999-11-22|89.813| null| null| null| |1999-11-23|89.625|-0.18800354|-0.00209327750580...|-0.00209547147341...| |1999-11-24|91.688| 2.0630035|0.023018170600156904| 0.02275724888423533| |1999-11-26|91.125|-0.56300354|-0.00614042751834165|-0.00615935747519754| |1999-11-29|90.188|-0.93699646|-0.01028254002700...|-0.01033577055278...| +----------+------+-----------+--------------------+--------------------+ only showing top 5 rows
We will use data in log_returns column to calculate overall stock price drift and standard deviation for use in our
Monte Carlo simulation. Using logarithmic return is better, see why:
https://quantivity.wordpress.com/2011/02/21/why-log-returns/
Next we calculate the variance, stddev, mean and drift for stock returns and use these values to estimate the expected
returns over a random distribution for a set number of days (rows) and iterations (array size in valueArray column).
Here, the drift is defined as:
drift = mean(log_returns) - 0.5 * variance(log_returns)
And expected return (Z Score) is calculated as:
exp(drift + deviation * distribution.inverseCumulativeProbability(value)
2019-11-25 00:46:53,530 -0600 [ForkJoinPool-1-worker-5] INFO (RunMontecarloSimulation.scala:46) - MSFT stock returns variance: 4.511899231076514E-4
2019-11-25 00:46:53,531 -0600 [ForkJoinPool-1-worker-5] INFO (RunMontecarloSimulation.scala:47) - MSFT stock returns deviation: 0.021241231675862192
2019-11-25 00:46:53,531 -0600 [ForkJoinPool-1-worker-5] INFO (RunMontecarloSimulation.scala:48) - MSFT stock returns mean: 1.0144483073271711E-4
2019-11-25 00:46:53,536 -0600 [ForkJoinPool-1-worker-5] INFO (RunMontecarloSimulation.scala:49) - MSFT stock returns drift: -1.241501308211086E-4
Table: Expected future returns (Z scores)
+--------------------+
| valueArray|
+--------------------+
|[1.05515136005395...|
|[1.11660235627113...|
|[0.98645836268787...|
|[0.92650393370421...|
|[1.04098867890015...|
+--------------------+
only showing top 5 rows
We then take the last known closing price and multiply it with our Expected future returns.
Table: Expected future stock prices for MSFT (valueArray)
+--------------------+
| valueArray|
+--------------------+
|[149.619995117187...|
|[147.738231038638...|
|[152.448257240264...|
|[150.628174249042...|
|[151.305624221947...|
+--------------------+
only showing top 5 rows
Since at this point the values for each iteration on a given day is stored as an Row[Array[Double]], we explode it.
Table: Expected future stock prices for MSFT (for x days(rows), and 6 iterations(columns))
+------------------+------------------+------------------+------------------+------------------+------------------+
| c_1| c_2| c_3| c_4| c_5| c_6|
+------------------+------------------+------------------+------------------+------------------+------------------+
| 149.6199951171875| 149.6199951171875| 149.6199951171875| 149.6199951171875| 149.6199951171875| 149.6199951171875|
| 147.7382310386384|149.57309488489616|151.38360286904722|143.93718738358982|148.10418911170942|151.51272810879016|
|152.44825724026444|146.94372665656476|148.17021606888645|144.78228352084327|152.19283708684793| 153.2640550593828|
|150.62817424904205|144.39749699725647|149.00135167825218|144.68636798388823|149.40161433218884|147.66921778183877|
| 151.3056242219476|143.73321953105386|156.29962733093882| 149.1660503436783|146.19719683832446|147.26748850075634|
+------------------+------------------+------------------+------------------+------------------+------------------+
only showing top 5 rows
The above process is done for each stock symbol (eg. AMZN,AAPL,MSFT) in the portfolio. Finally, we make use of a dummy investment stratagy to buy and sell stocks on each simulation day. On the first day, equal amount of money is allocated for buying each of the symbols in the portfolio. There after, stocks are bought/sold when the mean percentage change in the past days of simulation is greater/less than 0 respectively for each symbol.
Buying happens incrementally, where as selling is cumulative. On the last day, all stocks are sold and profit/loss is booked.
Table: Investment details for each simulation day (some columns hidden and values truncated for display)
+---+-----------+------------------+-+--------------------+--------------+-------------+------------------+-------+
| id|stockSymbol| predictedValue|.| mean_pct_change|quantityBought| marketCost| remainingFunds|outlook|
+---+-----------+------------------+-+--------------------+--------------+-------------+------------------+-------+
| 1| AMZN| 1745.530029296875|.| 0.0| 1.0| 1745.587...5| 8254.469970703125| BUY|
| 1| AAPL|263.19000244140625|.| 0.0| 12.0| 3158.287...5| 5096.18994140625| BUY|
| 1| MSFT| 149.6199951171875|.| 0.0| 22.0| 3291.612...5| 1804.550048828125| BUY|
| 2| AMZN|1821.1505181370044|.|0.021661182440553723| 0.0| 0.0| 1804.550048828125| BUY|
| 2| AAPL|245.86861972824198|.|-0.03290661224303...| -12.0|2950.4203...5| 4754.973485567029| SELL|
| 2| MSFT|151.51272810879016|.|0.006325133850325965| 10.0|1515.1201...6|3239.8462044791268| BUY|
| 3| AMZN|1842.0135754749651|.|0.011039052943434985| 0.0| 0.0|3239.8462044791268| BUY|
| 3| AAPL|236.26625334697493|.|-0.02398715993518...| -0.0| 0.0|3239.8462044791268| SELL|
| 3| MSFT| 153.2640550593828|.|0.005961358867846343| 7.0|1072.8479...6|2166.9978190634474| BUY|
| 4| AMZN| 1907.220146046128|.|0.011609666873831307| 0.0| 0.0|2166.9978190634474| BUY|
| 4| AAPL|247.32655790614575|.|0.005706430855499625| 2.0| 494.6591...5|1672.3447032511558| BUY|
| 4| MSFT|147.66921778183877|.|-0.00763580090905...| -39.0| 5759.071...2|7431.4441967428675| SELL|
| 5| AMZN|1972.7715829167523|.|0.009195962784168149| 1.0|1972.7752...3| 5458.672613826115| BUY|
| 5| AAPL|244.26782556068173|.|-0.00133215005941...| -2.0|488.53534...6| 5947.208264947478| SELL|
| 5| MSFT|147.26748850075634|.|-0.00207125364572...| -0.0| 0.0| 5947.208264947478| SELL|
| 6| AMZN| 1994.071770222609|.|0.003332175039554328| 0.0| 0.0| 5947.208264947478| BUY|
| 6| AAPL| 253.2189474651591|.|0.005885425479420125| 7.0|1772.5313...6| 4174.675632691364| BUY|
| 6| MSFT|144.31455586087606|.|-0.00368712401664...| -0.0| 0.0| 4174.675632691364| SELL|
| 7| AMZN|1994.2138320221031|.|4.862024441400062E-4| 0.0| 0.0| 4174.675632691364| BUY|
| 7| AAPL| 237.6563380159748|.|-0.00793909683117...| -7.0|1663.5923...5| 5838.269998803187| SELL|
| 7| MSFT|143.54340814327054|.|-0.00129009200442...| -0.0| 0.0| 5838.269998803187| SELL|
| 8| AMZN|2019.9112511464944|.|0.001671524032143...| 0.0| 0.0| 5838.269998803187| BUY|
| 8| AAPL|237.96931824156454|.|-8.27769031745765...| -0.0| 0.0| 5838.269998803187| SELL|
| 8| MSFT|142.29968570007847|.|-0.00124431580036...| -0.0| 0.0| 5838.269998803187| SELL|
| 9| AMZN|1995.7709519117618|.|-0.00114218269209...| -2.0|3991.5423...6| 9829.81190262671| SELL|
| 9| AAPL|244.51648211516803|.|0.002964985518292...| 13.0| 3178.718...4| 6651.097635129527| BUY|
| 9| MSFT|142.50286804611562|.|2.039248568589026...| 15.0|2137.5434...5| 4513.554614437792| BUY|
| 10| AMZN|1980.7407165829306|.|-8.67322492639214...| -0.0| 0.0| 4513.554614437792| SELL|
| 10| AAPL| 238.2054404982603|.|-0.00228453057238...| -13.0|3096.6783...6| 7610.225340915176| SELL|
| 10| MSFT|145.24852981758028|.|0.001928780660240476| 17.0|2469.2264...6| 5141.000334016311| BUY|
| 11| AMZN|1770.6761004314574|.|-0.00972008076455211| -0.0| 0....0| 5141.000334016311| SELL|
| 11| AAPL|243.07697107036714|.|0.001651493827317...| 7.0| 1701.25...7|3439.4615365237414| BUY|
| 11| MSFT| 143.5419340713539|.|-8.92791506315769...| -32.0| 4593.332...5| 8032.803426807066| SELL|
| 12| AMZN| 1834.739585534063|.|0.002205013214403...| 1.0| 1834.706...3| 6198.063841273003| BUY|
| 12| AAPL|241.70242201788227|.|-3.33607955165453...| -7.0|1691.9175...8| 7889.980795398179| SELL|
| 12| MSFT|143.75985101142336|.|5.211248377878655E-5| 18.0|2587.6720...4| 5302.303477192559| BUY|
| 13| AMZN|1879.1428802094172|.|0.002031263731814039| 0.0| 0.0| 5302.303477192559| BUY|
| 13| AAPL|258.35531059537425|.|0.005274207906109...| 6.0|1550.1345...5| 3752.171613620313| BUY|
| 13| MSFT|140.40340522632485|.|-0.00179195966191...| -18.0|2527.2647...2| 6279.43290769416| SELL|
| 14| AMZN|1905.9185583861854|.|0.001162867286783766| 1.0|1905.9185...4| 4373.514349307975| BUY|
| 14| AAPL|263.62091737609353|.|0.001832533428249...| 5.0|1318.1067...7|3055.4097624275073| BUY|
| 14| MSFT| 148.2504573042822|.|0.003864097794877501| 6.0| 889.5093...2|2165.9070186018143| BUY|
| 15| AMZN|1823.1254368937944|.|-0.00281847618986...| -2.0|3646.2588...7| 5812.157892389403| SELL|
| 15| AAPL|279.93822874993515|.|0.004248627330622...| 6.0|1679.6210...8| 4132.528519889793| BUY|
| 15| MSFT| 148.4925006079745|.|3.664508398688171...| 9.0|1336.4370...6| 2796.096014418022| BUY|
| 16| AMZN|1771.3343008127158|.|-0.00195164751708...| -0.0| 0.0| 2796.096014418022| SELL|
| 16| AAPL|291.19639873865896|.|0.002779078096501...| 3.0| 873.5876...9|1922.5068182020452| BUY|
| 16| MSFT|142.51259154580487|.|-0.00249402067290...| -15.0| 2137.607...3| 4060.195691389118| SELL|
| 17| AMZN|1720.7059778289665|.|-0.00179609844029...| -0.0| 0.0| 4060.195691389118| SELL|
| 17| AAPL|291.57635599582824|.|2.402289709615675...| 4.0| 1166.331...3|2893.8902674058054| BUY|
| 17| MSFT|142.95215598987642|.|3.472762061568348...| 6.0| 857.7158...5| 2036.177331466547| BUY|
| 18| AMZN| 1775.108971138221|.|0.001656698427771315| 0.0| 0.0| 2036.177331466547| BUY|
| 18| AAPL|315.07294959087864|.|0.004490273911751226| 2.0| 630.1457...3|1406.0314322847898| BUY|
| 18| MSFT|146.29724832856883|.|0.001301933932500...| 3.0| 438.8906...5| 967.1396872990833| BUY|
| 19| AMZN|1798.5537952264597|.|7.823283782589504E-4| 0.0| 0.0| 967.1396872990833| BUY|
| 19| AAPL|345.16677271885436|.|0.005263373720232...| 0.0| 0.0| 967.1396872990833| BUY|
| 19| MSFT| 141.6084479027531|.|-0.00161830977517...| -9.0| 1274.477...8|2241.6157184238614| SELL|
| 20| AMZN|1840.1019323865196|.|0.001194159355919...| 0.0| 0.0|2241.6157184238614| BUY|
| 20| AAPL| 332.8855455149993|.|-0.00151585933385...| -26.0| 8655.098...2|10896.639901813844| SELL|
| 20| MSFT|141.75591456319827|.|-2.88470342884638...| -0.0| 0.0|10896.639901813844| SELL|
| 20| AMZN|1840.1019323865196|.|0.001194159355919...| -0.0| 0.0|10896.639901813844| SELL|
| 20| AAPL| 332.8855455149993|.|-0.00151585933385...| -0.0| 0.0|10896.639901813844| SELL|
| 20| MSFT|141.75591456319827|.|-2.88470342884638...| -0.0| 0.0|10896.639901813844| SELL|
+---+-----------+------------------+-+--------------------+--------------+-------------+------------------+-------+
Investment amount: 10000.0
Amount at the end of simulation: 10896.639901813844
change: 8.966399018138436%
Something to note, absolute paths are almost always preferred. Make sure to use correct file system URI.
For example, if the file 'hw2/src/main/resources/data/' is on a ordinary filesystem, within the users home directory,
use:
file://$HOME/hw2/src/main/resources/data/
Similarly, for file on HDFS, use hdfs://$HOSTNAME:PORT/some-path/
For S3 bucket, use s3://bucket-name/some-path/
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Setup Hadoop and Spark using bootstrap script and start all services
git clone "https://github.com/user501254/BD_STTP_2016.git"; cd BD_STTP_2016; chmod +x *.sh; InstallHadoop.sh; InstallSpark.sh; start-all.sh -
Clone this repository
git clone https://[email protected]/asing80/hw3.git -
Download the stock data in CSV format
./hw3/src/main/resources/bin/downloadsymboldata.sh $API_KEY $SYMBOL -
Run jar file using
spark-submitspark-submit hw3-assembly-0.1.jar \ hw3/src/main/resources/data/ \ MSFT,AAPL,AMZN 10 10 10000
For AWS EMR, please follow these steps:
https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-launch-custom-jar-cli.html
Input S3 bucket locations should be passed to the JAR file and must be accessible.
java com.ashessin.cs441.hw3.stocksim.Start <option> \
<absolute_csv_directory_path> <symbols> <period> <iterations> <value>
<option>:
[--simulate, RunMontecarloSimulation |
--configFile]
<absolute_csv_directory_path>:
fully qualified csv data directory URI.
example paths:
file:///absolute-path-to-input-directory/
hdfs://localhost:9000/absolute-path-to-input-directory/
s3://bucket-name/absolute-path-to-input-directory/
gs://bucket-name/absolute-path-to-input-directory/
<symbols>
a comma separated list of NASDAQ stock symbols, eg. MSFT,APPAL,AMZN
<period>
period of simulation
<iterations>
number of iterations for each day
<value>
the investment amount to start with
For execution through custom config file, please see src/main/resources/reference.conf for fields.
examples:
java com.ashessin.cs441.hw3.stocksim.Start --configFile \
file:///absolute-path-to-config-file.conf
java com.ashessin.cs441.hw3.stocksim.Start \
--simulate file:///absolute-path-to-input-directory/ \
MSFT,APPAL,AMZN 28 10 10000
java com.ashessin.cs441.hw3.stocksim.Start \
--simulate hdfs://localhost:9000/absolute-path-to-input-directory/ \
MSFT,APPAL,AMZN 28 10 10000
The program in its default configuration produces a number of tables, which can used for visualization.
Some samples along with demo for sample runs on Google Dataproc are available at:
https://asing80.people.uic.edu/cs441/hw3/
TODO