معرفی شرکت ها
AlgoAnalyzer-0.0.6
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
AlgoAnalyzer is a package for designing your trading strategies and run simulations to backtest your strategies and check their robustness
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | AlgoAnalyzer-0.0.6 |
| نام | AlgoAnalyzer |
| نسخه کتابخانه | 0.0.6 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Jash Shah |
| ایمیل نویسنده | shahjash271@gmail.com |
| آدرس صفحه اصلی | https://github.com/Jash271/AlgoAnalyzer |
| آدرس اینترنتی | https://pypi.org/project/AlgoAnalyzer/ |
| مجوز | MIT |
# AlgoAnalyzer
### AlgoAnalyzer is a package for designing your trading strategies and run simulations to backtest your strategies and check their robustness
<br>
---
## 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)
- ### `EMA`: Exponential Moving Average - Crossover Strategy
- ### `SMA`: Simple Moving Average - Crossover Strategy
- ### `MACD`: Moving Average Convergence Divergence - Crossover Strategy
- ### 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
- ### Avalible Lookback periods are “1d”, “5d”, “1mo”, “3mo”, “6mo”, “1y”, “2y”, “5y”, “10y”, “ytd”, “max”
- ### interval: Define the data interval for the technical indicator (It is generally advised to use 1d interval unless you to setup an Intraday strategy)
- ### Avalible Intervals are “1m”, “5m”, “15m”, “30m”, “1h”, “4h”, “1d”, “1wk”, “1mo”
- ### 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_PL` is the total profit/loss of the strategy.
- ### The `Buy_Signals` is a list of all the buy signals.
- ### Each Buy_Signal has the following fields:
- ### Shares: The number of shares bought.
- ### Date: The date of transaction.
- ### Investment_Value: The value of the investment.
- ### Action: Buy.
- ### Buy_Price`: The price at which the shares are bought.
- ### The `Sell_Signals` is a list of all the sell signals.
- ### Each Sell_Signal has the following fields:
- ### Shares: The number of shares sold.
- ### Date: The date of transaction.
- ### Investment_Value: The value of the investment.
- ### Action: Sell.
- ### Sell_Price: The price at which the shares are sold.
- ### Net_PL: The profit/loss of the transaction.
- ### The `Current_Investment` is the investment that is currently being held.
- ### The `Chart` is a list of files, which is file name of charts generated for your strategy
نیازمندی
| مقدار | نام |
|---|---|
| - | yfinance |
| - | plotly |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl AlgoAnalyzer-0.0.6:
pip install AlgoAnalyzer-0.0.6.whl
نصب پکیج tar.gz AlgoAnalyzer-0.0.6:
pip install AlgoAnalyzer-0.0.6.tar.gz