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.
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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
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
The trend detection purpose is to help you find the most probable Future Market Trend on basis of your indicator :
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,50)
#Printing the result
print("Buy Trend :",TREND['BUY'])
print("Sell Trend :",TREND['SELL'])
Result :
foo@bar:~$ python test.py
Buy Trend : 0
Sell Trend : 0
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 ?
Level = MyMarketPredictor.Support_Resistance_Estimation_Tool(Indicators)
print("Support Level :",Level['Support'])
print("Resistance Level :",Level['Resistance'])
Result :
Support Level : 146.42515227768754
Resistance Level : 147.38794619755853
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 :
# 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" )
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
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" )
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
You can ask questions and join the development discussion: