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Pandas-Market-Predictor-1.0.6
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مشاهده بیشترتوضیحات
Pandas Market Predictor, is a deep learning API written in Python on top of Panda that helping you predict future price (low and high), trend of Financial market assets.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | Pandas-Market-Predictor-1.0.6 |
| نام | Pandas-Market-Predictor |
| نسخه کتابخانه | 1.0.6 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | - |
| ایمیل نویسنده | Ouedraogo Somkieta Rahim Alex <s.r.a.ouedraogo@gmail.com> |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/Pandas-Market-Predictor/ |
| مجوز | - |
# Pandas Market Predictor
Pandas Market Predictor, is a deep learning API written in Python on top of Panda that helping you predict future price (low and min), trend of Financial market assets.
## About Pandas Market Predictor
Pandas Market Predictor , is a Technical Analysis API written in Python.
It was developed with a focus on enabling fast experimentation.
*Being able to go from idea to result as fast as possible is key to doing good research.*
Pandas Market Predictor is:
- **Simple**
- **Flexible**
- **Powerful**
## First contact with Pandas Market Predictor
The core data structures of Pandas Market Predictor are __Historical Market Data__ and __Technical Indicator__ .
A sample Data Set should be :
| Open | High | Low | Close | Volume | Indicator1 | Indicator2 |
|-----:|------:|----:|-----:|-------:|-----------:|-----------:|
|0.93767|0.93791|0.93618|0.9363|69414.0|0.9363860952540013|0.9365316260340849|
|0.9363|0.93764|0.93566|0.93666|23461.0|0.936477396836001|0.9365549667551604|
|0.93666|0.93798|0.93561|0.93724|26907.0|0.9367315978906674|0.936679518254222|
You can build your data set by using Pandas-TA lib : https://github.com/twopirllc/pandas-ta
For installation run :
```
pip install Pandas-Market-Predictor
```
## About Feature
### I.Trend Detection
The trend detection purpose is to help you find the most probable Future Market Trend on basis of your indicator :
````python
from Pandas_Market_Predictor import Pandas_Market_Predictor
import pandas as pd
if __name__ == "__main__" :
# Firt we read the specified data using pandas
df = pd.read_csv('dataset.csv')
df = df.dropna(axis=0) # Data cleaning
# Create predictor
MyMarketPredictor = Pandas_Market_Predictor(df)
# Predict Trend
Indicators = ["Indicator1","Indicator2"]
TREND = MyMarketPredictor.Trend_Detection(Indicators,10)
# 10 is the percentage of standard Deviation to detect
print(MyMarketPredictor.PERCENT_STD) # Print the value of standard deviation percentage
#Printing the result
print("Buy Trend :",TREND['BUY'])
print("Sell Trend :",TREND['SELL'])
````
Result :
```console
foo@bar:~$ python test.py
Buy Trend : 0
Sell Trend : 0
```
### II.The Support Resistance Estimation Tool
The Support Resistance Estimation Tool is as his name indicate permit to estimate the Low and High of an asset
The question is : What is the standard deviation for an up or down trend given the level of indicator that we have for the current period ?
````python
Level = MyMarketPredictor.Support_Resistance_Estimation_Tool(Indicators)
print("Support Level :",Level['Support'])
print("Resistance Level :",Level['Resistance'])
````
Result :
```console
Support Level : 146.42515227768754
Resistance Level : 147.38794619755853
```
#### UPTREND EXEMPLE
### III.The RISK MANAGEMENT TOOL
Even if you make very good prediction and having right 99% of time. The 1% Risk could append a day and reduce all your profit to néant so you
need to have a good risk reward management.
Risk is about 2 things :
#### 1. Determine at witch price your setup is invalide ?
````python
# Risk Reward Ratio 1 / 3 mean i need to won 1 trade over 3 for being profitable
RISK_REWARD_RATIO = 1 / 3
# Stop Loss Calculation Exemple for Up & Down Trend
Stop_Loss_Up = MyMarketPredictor.STOP_LOSS_CALCULATOR("UP",Level['Support'],Level['Resistance'],RISK_REWARD_RATIO ) # For Up Trend
Stop_Loss_Down = MyMarketPredictor.STOP_LOSS_CALCULATOR("DOWN",Level['Support'],Level['Resistance'],RISK_REWARD_RATIO ) # For Up Down
# Printing Result
print("The Stop Loss Level for up Trend is", Stop_Loss_Up , "for",RISK_REWARD_RATIO ,"RISK_REWARD_RATIO" )
print("The Stop Loss Level for down Trend is", Stop_Loss_Down , "for",RISK_REWARD_RATIO ,"RISK_REWARD_RATIO" )
````
```console
The Stop Loss Level for up Trend is 146.10422097106388 for 0.3333333333333333 RISK_REWARD_RATIO
The Stop Loss Level for down Trend is 147.7088775041822 for 0.3333333333333333 RISK_REWARD_RATIO
```
#### 2. Determine at witch price to exit ?
````python
Trade_Efficiency_Factor = 1 - RISK_REWARD_RATIO
Take_Profit_Up = MyMarketPredictor.Take_Profit_CALCULATOR("UP",Level['Support'],Level['Resistance'],Trade_Efficiency_Factor)
Take_Profit_Down = MyMarketPredictor.Take_Profit_CALCULATOR("UP",Level['Support'],Level['Resistance'],Trade_Efficiency_Factor)
print("The Take Profit Level for up Trend is", Take_Profit_Up , "for",Trade_Efficiency_Factor ,"Trade_Efficiency_Factor" )
print("The Take Profit Level for down Trend is", Take_Profit_Down , "for",Trade_Efficiency_Factor ,"Trade_Efficiency_Factor" )
````
```console
The Take Profit Level for up Trend is 147.06701489093487 for 0.6666666666666667 Trade_Efficiency_Factor
The Take Profit Level for down Trend is 147.06701489093487 for 0.6666666666666667 Trade_Efficiency_Factor
```
---
## Support
You can ask questions and join the development discussion:
- [Facebook page](https://www.facebook.com/globalanalysistech) .
---
نیازمندی
| مقدار | نام |
|---|---|
| - | numpy |
| - | pandas |
| - | Artificial-Neural-Network-Classifier |
| - | Awesome-Linear-Regression |
| - | Auto-Gradient-Boosting |
| - | importlib-metadata |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl Pandas-Market-Predictor-1.0.6:
pip install Pandas-Market-Predictor-1.0.6.whl
نصب پکیج tar.gz Pandas-Market-Predictor-1.0.6:
pip install Pandas-Market-Predictor-1.0.6.tar.gz