AlgoAnalyzer
AlgoAnalyzer is a package for designing your trading strategies and run simulations to backtest your strategies and check their robustness
User Guide
Note for Indian Equtites append .NS to the end of the stock symbol
Eg: For YESBANK enter the Ticker name as YESBANK.NS
You can define multiple strategies and back test them simultaneously
Submit your strategy as a list of dictionary following the below format
[
{
"Ticker": "YESBANK.NS", # This is the Symbol Name
"Capital": 100000, # Define the capital you want to trade with
"Method": "EMA", # Define the technical indicator you want to use
"Short_Term_Period": 20, # Define the short term period for the technical indicator
"Long_Term_Period": 50, # Define the long term period for the technical indicator
"look_back_period": "1y", # Avalible Lookback periods are “1d”, “5d”, “1mo”, “3mo”, “6mo”, “1y”, “2y”, “5y”, “10y”, “ytd”, “max”
"interval": "1d",
"stop_loss": 5 # Define stoploss for damage control
}
]
Understanding the Keys defined in the Dictionary
-
Ticker: This is the Symbol Name
-
Capital: Define the capital you want to trade with
-
Method: Define the technical indicator you want to use (As of now the package supports these indicators)
-
Short_Term_Period: Define the short term period for the technical indicator(for CrossOver Strategy)
-
Long_Term_Period: Define the long term period for the technical indicator(for CrossOver Strategy)
-
look_back_period: The time period for which you want to backtest your strategy
-
interval: Define the data interval for the technical indicator (It is generally advised to use 1d interval unless you to setup an Intraday strategy)
-
stop_loss: Define stoploss for damage control to override you strategy . This is denoted as % so keep the stop_loss value between 0 and 100
Once you have defined your strategy, you can run the simulation by passing initialzing the runner class and then invoking the get_res() method.
Charts will be generated for each strategy and the charts will be saved as interactive html files in the same directory as the python file.
Final Results will be saved in a {timestamp}_results.json file in the same directory as the python file.
Sample code to demonstrate the whole process
from AlgoAnalyzer import Analyzer as a
if __name__ == "__main__":
Jobs = [
{
"Ticker": "YESBANK.NS",
"Capital": 100000,
"Method": "EMA",
"Short_Term_Period": 20,
"Long_Term_Period": 50,
# Avalible Lookback periods are “1d”, “5d”, “1mo”, “3mo”, “6mo”, “1y”, “2y”, “5y”, “10y”, “ytd”, “max”
"look_back_period": "1y",
"interval": "1d",
# "stop_loss": 5
},
{
"Ticker": "ZEEMEDIA.NS",
"Capital": 100000,
"Method": "SMA",
"Short_Term_Period": 5,
"Long_Term_Period": 10,
# Avalible Lookback periods are “1d”, “5d”, “1mo”, “3mo”, “6mo”, “1y”, “2y”, “5y”, “10y”, “ytd”, “max”
"look_back_period": "6mo",
"interval": "1d",
# "stop_loss": 5
},
{
"Ticker": "ZOMATO.NS",
"Capital": 100000,
"Method": "MACD",
# Avalible Lookback periods are “1d”, “5d”, “1mo”, “3mo”, “6mo”, “1y”, “2y”, “5y”, “10y”, “ytd”, “max”
"look_back_period": "1y",
"interval": "1d",
},
]
a.Analyzer(Jobs).get_res()
It is important to have this code snippet in your file since multiprocessing module is used internally and not having this could possibly lead to errors
if __name__ == "__main__":
Output as obtained in the {timestamp}_results.json file
{
"summary": [
{
"10020_YESBANK.NS_EMA": {
"Net_PL": -3487.9485216140747,
"Buy_Signals": [
{
"Shares": 7751,
"Date": "2021-09-27T00:00:00",
"Investment_Value": 99987.89704322815,
"Action": "Buy",
"Buy_Price": 12.899999618530273
},
{
"Shares": 6893,
"Date": "2021-12-09T00:00:00",
"Investment_Value": 96502.0,
"Action": "Buy",
"Buy_Price": 14.0
}
],
"Sell_Signals": [
{
"Shares": 7751,
"Date": "2021-11-26T00:00:00",
"Investment_Value": 96499.94852161407,
"Action": "Sell",
"Sell_Price": 12.449999809265137,
"Net_PL": -3487.9485216140747
}
],
"Job_ID": 10020,
"Job_details": {
"Ticker": "YESBANK.NS",
"Capital": 100000,
"Method": "EMA",
"Short_Term_Period": 20,
"Long_Term_Period": 50,
"look_back_period": "1y",
"interval": "1d"
},
"Current_Investment": {
"Shares": 6893,
"Date": "2021-12-09T00:00:00",
"Investment_Value": 96502.0,
"Action": "Buy",
"Buy_Price": 14.0
},
"Chart": [
"YESBANK_50_20_EMA_10020_2022-01-14T17_50_02_377762.html"
]
}
},
{
"22996_ZEEMEDIA.NS_SMA": {
"Net_PL": 41241.543095588684,
"Buy_Signals": [
{
"Shares": 11049,
"Date": "2021-09-02T00:00:00",
"Investment_Value": 99993.4521074295,
"Action": "Buy",
"Buy_Price": 9.050000190734863
},
{
"Shares": 11597,
"Date": "2021-11-11T00:00:00",
"Investment_Value": 151920.70442390442,
"Action": "Buy",
"Buy_Price": 13.100000381469727
},
{
"Shares": 10619,
"Date": "2021-12-07T00:00:00",
"Investment_Value": 132206.5479745865,
"Action": "Buy",
"Buy_Price": 12.449999809265137
}
],
"Sell_Signals": [
{
"Shares": 11049,
"Date": "2021-10-12T00:00:00",
"Investment_Value": 151923.75,
"Action": "Sell",
"Sell_Price": 13.75,
"Net_PL": 51930.297892570496
},
{
"Shares": 11597,
"Date": "2021-11-22T00:00:00",
"Investment_Value": 132205.79557609558,
"Action": "Sell",
"Sell_Price": 11.399999618530273,
"Net_PL": -19714.908847808838
},
{
"Shares": 10619,
"Date": "2021-12-22T00:00:00",
"Investment_Value": 141232.7020254135,
"Action": "Sell",
"Sell_Price": 13.300000190734863,
"Net_PL": 9026.154050827026
}
],
"Job_ID": 22996,
"Job_details": {
"Ticker": "ZEEMEDIA.NS",
"Capital": 100000,
"Method": "SMA",
"Short_Term_Period": 5,
"Long_Term_Period": 10,
"look_back_period": "6mo",
"interval": "1d"
},
"Chart": [
"ZEEMEDIA_10_5_SMA_22996_2022-01-14T17_50_01_931633.html"
]
}
},
{
"21828_ZOMATO.NS_MACD": {
"Net_PL": -16501.861557006836,
"Buy_Signals": [
{
"Shares": 706,
"Date": "2021-07-29T00:00:00",
"Investment_Value": 99934.30215454102,
"Action": "Buy",
"Buy_Price": 141.5500030517578
},
{
"Shares": 694,
"Date": "2021-08-13T00:00:00",
"Investment_Value": 95320.90423583984,
"Action": "Buy",
"Buy_Price": 137.35000610351562
},
{
"Shares": 681,
"Date": "2021-08-18T00:00:00",
"Investment_Value": 91900.94792175293,
"Action": "Buy",
"Buy_Price": 134.9499969482422
},
{
"Shares": 644,
"Date": "2021-08-31T00:00:00",
"Investment_Value": 86650.20196533203,
"Action": "Buy",
"Buy_Price": 134.5500030517578
},
{
"Shares": 600,
"Date": "2021-10-18T00:00:00",
"Investment_Value": 86430.00183105469,
"Action": "Buy",
"Buy_Price": 144.0500030517578
},
{
"Shares": 586,
"Date": "2021-11-10T00:00:00",
"Investment_Value": 79725.30178833008,
"Action": "Buy",
"Buy_Price": 136.0500030517578
},
{
"Shares": 645,
"Date": "2021-12-31T00:00:00",
"Investment_Value": 88622.99606323242,
"Action": "Buy",
"Buy_Price": 137.39999389648438
},
{
"Shares": 627,
"Date": "2022-01-13T00:00:00",
"Investment_Value": 83453.7038269043,
"Action": "Buy",
"Buy_Price": 133.10000610351562
}
],
"Sell_Signals": [
{
"Shares": 706,
"Date": "2021-08-05T00:00:00",
"Investment_Value": 95274.69784545898,
"Action": "Sell",
"Sell_Price": 134.9499969482422,
"Net_PL": -4659.604309082031
},
{
"Shares": 694,
"Date": "2021-08-17T00:00:00",
"Investment_Value": 91955.0,
"Action": "Sell",
"Sell_Price": 132.5,
"Net_PL": -3365.9042358398438
},
{
"Shares": 681,
"Date": "2021-08-23T00:00:00",
"Investment_Value": 86657.25,
"Action": "Sell",
"Sell_Price": 127.25,
"Net_PL": -5243.69792175293
},
{
"Shares": 644,
"Date": "2021-09-20T00:00:00",
"Investment_Value": 86489.20196533203,
"Action": "Sell",
"Sell_Price": 134.3000030517578,
"Net_PL": -161.0
},
{
"Shares": 600,
"Date": "2021-10-25T00:00:00",
"Investment_Value": 79619.99816894531,
"Action": "Sell",
"Sell_Price": 132.6999969482422,
"Net_PL": -6810.003662109375
},
{
"Shares": 586,
"Date": "2021-12-01T00:00:00",
"Investment_Value": 88720.39642333984,
"Action": "Sell",
"Sell_Price": 151.39999389648438,
"Net_PL": 8995.094635009766
},
{
"Shares": 645,
"Date": "2022-01-07T00:00:00",
"Investment_Value": 83366.25,
"Action": "Sell",
"Sell_Price": 129.25,
"Net_PL": -5256.746063232422
}
],
"Job_ID": 21828,
"Job_details": {
"Ticker": "ZOMATO.NS",
"Capital": 100000,
"Method": "MACD",
"look_back_period": "1y",
"interval": "1d"
},
"Current_Investment": {
"Shares": 627,
"Date": "2022-01-13T00:00:00",
"Investment_Value": 83453.7038269043,
"Action": "Buy",
"Buy_Price": 133.10000610351562
},
"Chart": [
"ZOMATO.NS_macd_signal_21828_2022-01-14T17_50_02_041010.html",
"ZOMATO_candle_21828_2022-01-14T17_50_02_349608.html"
]
}
}
]
}
Analyzing the Output
-
The
Net_PLis the total profit/loss of the strategy. -
The
Buy_Signalsis a list of all the buy signals. -
The
Sell_Signalsis a list of all the sell signals. -
The
Current_Investmentis the investment that is currently being held. -
The
Chartis a list of files, which is file name of charts generated for your strategy
1 maintainer
Metadata
1 maintainer
Release files for AlgoAnalyzer 0.0.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| AlgoAnalyzer-0.0.6.tar.gz | 8.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| AlgoAnalyzer-0.0.6-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 20.7 kB
Release files / AlgoAnalyzer-0.0.6.tar.gz
| Download URL | AlgoAnalyzer-0.0.6.tar.gz |
|---|---|
| Size | 8.9 kB |
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Release files / AlgoAnalyzer-0.0.6-py3-none-any.whl
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