├── .gitattributes ├── .gitignore ├── CHANGELOG.md ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── GIT_PUSH.bat ├── LICENSE ├── LendingBot ├── CLASS │ ├── DATABASE.py │ ├── MYSQL.py │ └── POLONIEXAPI.py ├── helper │ ├── config.py │ ├── requestcsv.py │ ├── telegramsend.py │ └── tradingfunctions.py ├── lending.py ├── telegrambot.py ├── test.py └── trading.py ├── SECURITY.md ├── config └── .gitignore └── install.sh /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | config.ini 2 | *.pyc 3 | *.csv 4 | *.h5 5 | *.pkl -------------------------------------------------------------------------------- /CHANGELOG.md: -------------------------------------------------------------------------------- 1 | 2 | # Change Log 3 | 4 | ## [0.0.1] - 2021-08-23 5 | 6 | ### Added 7 | - trading.py 8 | - insert CSV Request [Issue 8](https://github.com/woehrer12/LendingBot/issues/8) 9 | ### Changed 10 | - main.py to lending.py 11 | ### Fixed 12 | - bug with Issues link 13 | - remove trailing spaces [Issue 2](https://github.com/woehrer12/LendingBot/issues/2) 14 | 15 | The format is based on [Keep a Changelog](http://keepachangelog.com/) 16 | and this project adheres to [Semantic Versioning](http://semver.org/). 17 | -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Contributor Covenant Code of Conduct 2 | 3 | ## Our Pledge 4 | 5 | We as members, contributors, and leaders pledge to make participation in our 6 | community a harassment-free experience for everyone, regardless of age, body 7 | size, visible or invisible disability, ethnicity, sex characteristics, gender 8 | identity and expression, level of experience, education, socio-economic status, 9 | nationality, personal appearance, race, religion, or sexual identity 10 | and orientation. 11 | 12 | We pledge to act and interact in ways that contribute to an open, welcoming, 13 | diverse, inclusive, and healthy community. 14 | 15 | ## Our Standards 16 | 17 | Examples of behavior that contributes to a positive environment for our 18 | community include: 19 | 20 | * Demonstrating empathy and kindness toward other people 21 | * Being respectful of differing opinions, viewpoints, and experiences 22 | * Giving and gracefully accepting constructive feedback 23 | * Accepting responsibility and apologizing to those affected by our mistakes, 24 | and learning from the experience 25 | * Focusing on what is best not just for us as individuals, but for the 26 | overall community 27 | 28 | Examples of unacceptable behavior include: 29 | 30 | * The use of sexualized language or imagery, and sexual attention or 31 | advances of any kind 32 | * Trolling, insulting or derogatory comments, and personal or political attacks 33 | * Public or private harassment 34 | * Publishing others' private information, such as a physical or email 35 | address, without their explicit permission 36 | * Other conduct which could reasonably be considered inappropriate in a 37 | professional setting 38 | 39 | ## Enforcement Responsibilities 40 | 41 | Community leaders are responsible for clarifying and enforcing our standards of 42 | acceptable behavior and will take appropriate and fair corrective action in 43 | response to any behavior that they deem inappropriate, threatening, offensive, 44 | or harmful. 45 | 46 | Community leaders have the right and responsibility to remove, edit, or reject 47 | comments, commits, code, wiki edits, issues, and other contributions that are 48 | not aligned to this Code of Conduct, and will communicate reasons for moderation 49 | decisions when appropriate. 50 | 51 | ## Scope 52 | 53 | This Code of Conduct applies within all community spaces, and also applies when 54 | an individual is officially representing the community in public spaces. 55 | Examples of representing our community include using an official e-mail address, 56 | posting via an official social media account, or acting as an appointed 57 | representative at an online or offline event. 58 | 59 | ## Enforcement 60 | 61 | Instances of abusive, harassing, or otherwise unacceptable behavior may be 62 | reported to the community leaders responsible for enforcement at 63 | julian.woehrer@gmail.com. 64 | All complaints will be reviewed and investigated promptly and fairly. 65 | 66 | All community leaders are obligated to respect the privacy and security of the 67 | reporter of any incident. 68 | 69 | ## Enforcement Guidelines 70 | 71 | Community leaders will follow these Community Impact Guidelines in determining 72 | the consequences for any action they deem in violation of this Code of Conduct: 73 | 74 | ### 1. Correction 75 | 76 | **Community Impact**: Use of inappropriate language or other behavior deemed 77 | unprofessional or unwelcome in the community. 78 | 79 | **Consequence**: A private, written warning from community leaders, providing 80 | clarity around the nature of the violation and an explanation of why the 81 | behavior was inappropriate. A public apology may be requested. 82 | 83 | ### 2. Warning 84 | 85 | **Community Impact**: A violation through a single incident or series 86 | of actions. 87 | 88 | **Consequence**: A warning with consequences for continued behavior. No 89 | interaction with the people involved, including unsolicited interaction with 90 | those enforcing the Code of Conduct, for a specified period of time. This 91 | includes avoiding interactions in community spaces as well as external channels 92 | like social media. Violating these terms may lead to a temporary or 93 | permanent ban. 94 | 95 | ### 3. Temporary Ban 96 | 97 | **Community Impact**: A serious violation of community standards, including 98 | sustained inappropriate behavior. 99 | 100 | **Consequence**: A temporary ban from any sort of interaction or public 101 | communication with the community for a specified period of time. No public or 102 | private interaction with the people involved, including unsolicited interaction 103 | with those enforcing the Code of Conduct, is allowed during this period. 104 | Violating these terms may lead to a permanent ban. 105 | 106 | ### 4. Permanent Ban 107 | 108 | **Community Impact**: Demonstrating a pattern of violation of community 109 | standards, including sustained inappropriate behavior, harassment of an 110 | individual, or aggression toward or disparagement of classes of individuals. 111 | 112 | **Consequence**: A permanent ban from any sort of public interaction within 113 | the community. 114 | 115 | ## Attribution 116 | 117 | This Code of Conduct is adapted from the [Contributor Covenant][homepage], 118 | version 2.0, available at 119 | https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. 120 | 121 | Community Impact Guidelines were inspired by [Mozilla's code of conduct 122 | enforcement ladder](https://github.com/mozilla/diversity). 123 | 124 | [homepage]: https://www.contributor-covenant.org 125 | 126 | For answers to common questions about this code of conduct, see the FAQ at 127 | https://www.contributor-covenant.org/faq. Translations are available at 128 | https://www.contributor-covenant.org/translations. 129 | -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing 2 | 3 | When contributing to this repository, please first discuss the change you wish to make via issue, 4 | email, or any other method with the owners of this repository before making a change. 5 | 6 | Please note we have a code of conduct, please follow it in all your interactions with the project. 7 | 8 | ## Pull Request Process 9 | 10 | - Ensure any install or build dependencies are removed before the end of the layer when doing a 11 | build. 12 | - Update the README.md with details of changes to the interface, this includes new environment 13 | variables, exposed ports, useful file locations and container parameters. 14 | - Update the Changelog with your changes and the Issue Number 15 | - Make the Pull Pull Request to the devlopment branch 16 | - You may merge the Pull Request in once you have the sign-off of two other developers, or if you 17 | do not have permission to do that, you may request the second reviewer to merge it for you. 18 | -------------------------------------------------------------------------------- /GIT_PUSH.bat: -------------------------------------------------------------------------------- 1 | git init 2 | git add . 3 | git commit -m "first commit" 4 | git branch -M main 5 | git remote add origin https://github.com/mymiracle0118/Lending-Bot.git 6 | git push -u origin main -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /LendingBot/CLASS/DATABASE.py: -------------------------------------------------------------------------------- 1 | from pyArango.connection import Connection 2 | import sys 3 | import datetime 4 | import time 5 | import helper.telegramsend 6 | 7 | class DATABASE(): 8 | 9 | def connect(self): 10 | try: 11 | self.conn = Connection(arangoURL="http://127.0.0.1:8529", username="root", password="openSesame") 12 | except ZeroDivisionError: 13 | print("ZeroficisionError") 14 | except TypeError: 15 | errortext = "TypeError: Connection to Database failed\n " + str(sys.exc_info()) 16 | print(errortext) 17 | 18 | def setup(self): 19 | if not self.conn.hasDatabase(name='Lending'): 20 | db = self.conn.createDatabase(name='Lending') 21 | db = self.conn['Lending'] 22 | 23 | if not db.hasCollection(name='Ticker'): 24 | db.createCollection(name='Ticker') 25 | 26 | if not db.hasCollection(name='BTC'): 27 | db.createCollection(name='BTC') 28 | 29 | if not db.hasCollection(name='ActiveLoans'): 30 | db.createCollection(name='ActiveLoans') 31 | 32 | if not db.hasCollection(name='HistoryLoans'): 33 | db.createCollection(name='HistoryLoans') 34 | 35 | def insertTicker(self,data): 36 | db = self.conn['Lending'] 37 | tickerCollection = db['Ticker'] 38 | tickerDoc = tickerCollection.createDocument() 39 | for keys in data: 40 | tickerDoc[keys] = data[keys] 41 | tickerDoc['TIMESTAMP'] = datetime.datetime.strftime(datetime.datetime.now(),"%Y-%m-%d %H:%M:%S") 42 | tickerDoc.save() 43 | 44 | def insertBTC(self,BTC): 45 | db = self.conn['Lending'] 46 | BTCCollection = db['BTC'] 47 | BTCDoc = BTCCollection.createDocument() 48 | BTCDoc['value'] = BTC 49 | #BTCDoc['TIMESTAMP'] = datetime.datetime.strftime(datetime.datetime.now(),"%Y-%m-%d %H:%M:%S") 50 | BTCDoc['date'] = time.time() 51 | BTCDoc.save() 52 | 53 | def insertActiveLoan(self,data): 54 | db = self.conn['Lending'] 55 | aql = "FOR x IN ActiveLoans FILTER x.id == " + str(data['id']) + " RETURN x" 56 | queryResult = db.AQLQuery(aql, rawResults=True) 57 | if len(queryResult) == 0: 58 | Text = "New Loan: \nID: " + str(data['id']) + "\nCurrency: " + data['currency'] + "\nRate: " + str(data['rate']) + "\nAmount: " + str(data['amount']) + "\nDuration: " + str(data['duration']) 59 | helper.telegramsend.send(Text) 60 | ActiveLoanCollection = db['ActiveLoans'] 61 | ActiveLoanDoc = ActiveLoanCollection.createDocument() 62 | for keys in data: 63 | ActiveLoanDoc[keys] = data[keys] 64 | ActiveLoanDoc.save() 65 | 66 | def insertHistoryLoan(self,data): 67 | db = self.conn['Lending'] 68 | for datakey in data: 69 | aql = "FOR x IN HistoryLoans FILTER x.id == " + str(datakey['id']) + " RETURN x" 70 | queryResult = db.AQLQuery(aql, rawResults=True) 71 | if len(queryResult) == 0: 72 | Text = "New History Loan: \nID: " + str(datakey['id']) + "\nCurrency: " + datakey['currency'] + "\nRate: " + str(datakey['rate']) + "\nAmount: " + str(datakey['amount']) + "\nDuration: " + str(datakey['duration']) + "\nEarned: " + str(datakey['earned']) 73 | helper.telegramsend.send(Text) 74 | HistoryLoanCollection = db['HistoryLoans'] 75 | HistoryLoanDoc = HistoryLoanCollection.createDocument() 76 | for keys in datakey: 77 | HistoryLoanDoc[keys] = datakey[keys] 78 | HistoryLoanDoc.save() 79 | -------------------------------------------------------------------------------- /LendingBot/CLASS/MYSQL.py: -------------------------------------------------------------------------------- 1 | import mysql.connector 2 | import sys 3 | 4 | class MYSQL(): 5 | def getDataBaseConnection(self): 6 | try: 7 | #Datenbankverbindung herstellen 8 | self.mydb = mysql.connector.connect( 9 | host="localhost", 10 | user="root", 11 | password="password", 12 | database="Lending", 13 | auth_plugin='mysql_native_password' 14 | ) 15 | except: 16 | print("Unexpected error Datenbankverbindung functions.py:" + str(sys.exc_info())) 17 | 18 | -------------------------------------------------------------------------------- /LendingBot/CLASS/POLONIEXAPI.py: -------------------------------------------------------------------------------- 1 | import helper.config 2 | from poloniex import Poloniex 3 | 4 | import statistics 5 | 6 | class POLONIEXAPI(): 7 | 8 | def request(self): 9 | conf = helper.config.initconfig() 10 | 11 | self.polo = Poloniex() 12 | self.polo.key = conf["api_key"] 13 | self.polo.secret = conf["secret"] 14 | self.ticker = self.polo('returnTicker') 15 | self.completeBalances = self.polo.returnCompleteBalances() 16 | self.availableAccountBalances = self.polo.returnAvailableAccountBalances() 17 | self.activeLoans = self.polo.returnActiveLoans() 18 | self.LendingHistory = self.polo.returnLendingHistory() #TODO Test 19 | self.OpenLoanOffers = self.polo.returnOpenLoanOffers() #TODO Test 20 | 21 | 22 | 23 | def returnLoanOrders(self,currency): 24 | return self.polo.returnLoanOrders(currency=currency) 25 | 26 | def avgLoanOrders(self,currency): 27 | data = self.returnLoanOrders(currency) 28 | dicti = [] 29 | for key in data['offers']: 30 | dicti.append(float(key['rate'])) 31 | return statistics.mean(dicti) 32 | 33 | def createLoanOffer(self,amount, price): 34 | r = self.polo.createLoanOffer("BTC", amount, price, autoRenew=0) 35 | 36 | def completeBalancesSum(self): 37 | sum = 0.0 38 | for btcs in self.completeBalances: 39 | sum = sum + float(self.completeBalances[btcs]['btcValue']) 40 | self.completeBalances 41 | return sum 42 | -------------------------------------------------------------------------------- /LendingBot/helper/config.py: -------------------------------------------------------------------------------- 1 | import configparser 2 | import logging 3 | import sys 4 | import os 5 | 6 | config = configparser.ConfigParser() 7 | configFile = "config/config.ini" 8 | 9 | def initconfig(): 10 | if os.path.isfile(configFile): 11 | pass 12 | else: 13 | create() 14 | #Konfigdatei initialisieren 15 | try: 16 | #Config Datei auslesen 17 | config.read(configFile) 18 | conf = config['DEFAULT'] 19 | return conf 20 | except: 21 | print("Error while loading the Config file:" + str(sys.exc_info())) 22 | logging.error("Error while loading the Config file" + str(sys.exc_info())) 23 | 24 | def create(): 25 | 26 | config['DEFAULT'] = {'API_KEY' : '', 27 | 'Secret' : '', 28 | 'Database' : True, 29 | 'telegramtoken' : '', 30 | 'activeLending' : True, 31 | } 32 | 33 | with open(configFile, 'w') as configfile: 34 | config.write(configfile) 35 | 36 | def setactiveLendingTrue(): 37 | config.set('DEFAULT','activeLending','True') 38 | print("Lending On") 39 | with open('config/config.ini', 'w') as configfile: 40 | config.write(configfile) 41 | 42 | def setactiveLendingFalse(): 43 | config.set('DEFAULT','activeLending','False') 44 | print("Lending Off") 45 | with open('config/config.ini', 'w') as configfile: 46 | config.write(configfile) -------------------------------------------------------------------------------- /LendingBot/helper/requestcsv.py: -------------------------------------------------------------------------------- 1 | import requests 2 | import time 3 | import csv 4 | import os 5 | 6 | 7 | def request(CurrencyPair,Period): 8 | now = time.time() 9 | _1month = 2678400 10 | now_1month = now - _1month 11 | for x in range(72): 12 | url = "https://poloniex.com/public?command=returnChartData¤cyPair="+ CurrencyPair + "&start=" + str(now_1month) + "&end=" + str(now) + "&period=" + Period + "" 13 | resp = requests.get(url=url) 14 | data = resp.json() 15 | 16 | csv_path = './Data/' + CurrencyPair + '_Poloniex_' + str(x) + '_' + Period + '.csv' 17 | with open(csv_path, 'w', newline='') as f: 18 | dict_writer = csv.DictWriter(f, data[0].keys()) 19 | dict_writer.writeheader() 20 | dict_writer.writerows(data) 21 | print("CSV file saved at path {}".format(csv_path)) 22 | f.close() 23 | 24 | now = now - _1month 25 | now_1month = now - _1month 26 | time.sleep(5) 27 | 28 | alle = [] 29 | for y in range(72): 30 | csv_path = './Data/' + CurrencyPair + '_Poloniex_' + str(y) + '_' + Period + '.csv' 31 | with open(csv_path, newline='') as f: 32 | dict_reader = csv.DictReader(f) 33 | for row in dict_reader: 34 | alle.append(row) 35 | os.remove(csv_path) 36 | 37 | csv_path = './Data/' + CurrencyPair + '_Poloniex_all_' + Period + '.csv' 38 | with open(csv_path, 'w', newline='') as f: 39 | dict_writer = csv.DictWriter(f, alle[0].keys()) 40 | dict_writer.writeheader() 41 | dict_writer.writerows(alle) 42 | print("CSV file saved at path {}".format(csv_path)) -------------------------------------------------------------------------------- /LendingBot/helper/telegramsend.py: -------------------------------------------------------------------------------- 1 | import telegram 2 | import helper.config 3 | 4 | conf = helper.config.initconfig() 5 | 6 | bot = telegram.Bot(token=conf['telegramtoken']) 7 | 8 | def send(text): 9 | bot.send_message(353575753, text=text) -------------------------------------------------------------------------------- /LendingBot/helper/tradingfunctions.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import matplotlib.pyplot as plt 4 | from keras.models import Sequential 5 | 6 | def compute_sma(df, window, colname): 7 | '''Computes Simple Moving Average column on a dataframe''' 8 | df[colname] = df['close'].rolling(window=window, center=False).mean() 9 | return(df) 10 | 11 | def compute_rsi(df, window, colname): 12 | '''Computes RSI column for a dataframe. http://stackoverflow.com/a/32346692/3389859''' 13 | series = df['close'] 14 | delta = series.diff().dropna() 15 | u = delta * 0 16 | d = u.copy() 17 | u[delta > 0] = delta[delta > 0] 18 | d[delta < 0] = -delta[delta < 0] 19 | # first value is sum of avg gains 20 | u[u.index[window - 1]] = np.mean(u[:window]) 21 | u = u.drop(u.index[:(window - 1)]) 22 | # first value is sum of avg losses 23 | d[d.index[window - 1]] = np.mean(d[:window]) 24 | d = d.drop(d.index[:(window - 1)]) 25 | rs = u.ewm(com=window - 1,ignore_na=False, 26 | min_periods=0,adjust=False).mean() / d.ewm(com=window - 1, ignore_na=False, 27 | min_periods=0,adjust=False).mean() 28 | df[colname] = 100 - 100 / (1 + rs) 29 | df[colname].fillna(df[colname].mean(), inplace=True) 30 | return(df) 31 | 32 | def compute_variables1(df): 33 | print("Let's compute predictive variables : ") 34 | df["date"] = pd.to_datetime(df["date"]) 35 | df['bodysize'] = df['close'] - df['open'] 36 | df['shadowsize'] = df['high'] - df['low'] 37 | #TODO evtl Reihe erhöhen und auf auswirkungen schauen 38 | for window in [3, 8, 21, 55, 144, 377]: # several Fibonacci numbers 39 | print(window) 40 | df = compute_sma(df, window, colname = 'sma_{}'.format(window)) 41 | df = compute_rsi(df, window, colname = 'rsi_{}'.format(window)) 42 | df["Min_{}".format(window)] = df["low"].rolling(window).min() 43 | df["Max_{}".format(window)] = df["high"].rolling(window).max() 44 | df["volume_{}".format(window)] = df["volume"].rolling(window).mean() 45 | df['percentChange_{}'.format(window)] = df['close'].pct_change(periods = window) 46 | df['RelativeSize_sma_{}'.format(window)] = df['close'] / df['sma_{}'.format(window)] 47 | df['Diff_{}'.format(window)] = df['close'].diff(window) 48 | 49 | # (a) Add modulo 10, 100, 1000, 500, 50 50 | df["Modulo_10"] = df["close"].copy() % 10 51 | df["Modulo_100"] = df["close"].copy() % 100 52 | df["Modulo_1000"] = df["close"].copy() % 1000 53 | df["Modulo_500"] = df["close"].copy() % 500 54 | df["Modulo_50"] = df["close"].copy() % 50 55 | # (b) Add weekday and day of the month 56 | df["WeekDay"] = df["date"].dt.weekday 57 | df["Day"] = df["date"].dt.day 58 | df.dropna(inplace=True) 59 | return(df) 60 | 61 | def check_outcome(df, line, stoploss, takeprofit): 62 | '''0 means we reached stoploss 63 | 1 means we reached takeprofit 64 | -1 means still in between''' 65 | price0 = df["close"].iloc[line] 66 | upper_lim = price0*(1+takeprofit) 67 | down_lim = price0*(1-stoploss) 68 | for i in range(line, df["close"].size): 69 | if df["low"].iloc[i] < down_lim : 70 | return(0) 71 | elif df["high"].iloc[i] > upper_lim : 72 | return(1) 73 | return(-1) 74 | 75 | # def compute_result(df, stoploss, takeprofit): 76 | # df['result'] = 0 77 | # for i in range(df["close"].size): 78 | # if i%500 == 0: 79 | # print(i, '/', df.shape[0]) 80 | # df['result'].iloc[i] = check_outcome(df, i, stoploss, takeprofit) 81 | # return(df) 82 | 83 | from multiprocessing import Pool 84 | 85 | def f(df, stoploss, takeprofit): 86 | print('start') 87 | tmp = [] 88 | for i in range(df["close"].size): 89 | # df['result'].iloc[i] = check_outcome(df, i, stoploss, takeprofit) 90 | tmp.append(check_outcome(df, i, stoploss, takeprofit)) 91 | print("end") 92 | df['result'] = tmp 93 | return df 94 | # https://docs.python.org/3/library/multiprocessing.html 95 | def compute_result(df, stoploss, takeprofit): 96 | global dfs 97 | p = Pool(5) 98 | df['result'] = 0 99 | n_worker = 20 100 | N = df["close"].size 101 | dfs = [] 102 | 103 | print("####",df.shape) 104 | 105 | def adder(_df): 106 | global dfs 107 | dfs.append(_df) 108 | 109 | for i in range(n_worker): 110 | print(int(i*N/n_worker),int((i+1)*N/n_worker)) 111 | _df = df.iloc[int(i*N/n_worker):int((i+1)*N/n_worker)] 112 | # print(_df.head()) 113 | p.apply_async(f, args=(_df, stoploss, takeprofit,), callback=adder) 114 | p.close() 115 | p.join() 116 | 117 | df = pd.concat(dfs) 118 | print("CONCATENATION",df.shape) 119 | return (df) 120 | 121 | 122 | def compute_earnings_loss(stoploss, takeprofit, fees): 123 | '''Compute earnings and loss with given fees, stoploss, takeprofit''' 124 | win = (1-fees)*(1+takeprofit)*(1-fees) -1 125 | loss = (1-fees)*(1-stoploss)*(1-fees) -1 126 | return(win, loss) 127 | 128 | def predict_and_backtest_bullish(df, df_final, model, stoploss, takeprofit, fees, nPCs, plotting = True): 129 | '''This functin takes the test set as input (in both shapes) + the model, computes predictions and probabilities, then compute the earnings according to the fees. Finally it can plot the strategy''' 130 | # Compute predictions on testset 131 | clf = Sequential() 132 | df['preds'] = (clf.predict(df_final.iloc[:, :nPCs]) > 0.5)*1 133 | df['proba1'] = clf.predict(df_final.iloc[:, :nPCs]) 134 | 135 | # keep only the timesteps in which the model predicts a bullish trend 136 | testset1 = df[df['preds'] == 1].copy() 137 | 138 | # Compute earnings column 139 | a = compute_earnings_loss(stoploss, takeprofit, fees) 140 | testset1['EarningsBullish'] = (testset1['preds'] == testset1['result'])*a[0] + (testset1['preds'] != testset1['result'])*a[1] 141 | 142 | if plotting: 143 | # Now plot our trading strategy 144 | plt.plot(pd.to_datetime(testset1['date']), np.cumsum(testset1['EarningsBullish'])) 145 | plt.title('Approach over the test set \n ROI = {} %'.format(100*np.mean(testset1['EarningsBullish']))) 146 | plt.xlabel('Date') 147 | plt.xlabel('Cumulative Earnings') 148 | plt.show() 149 | 150 | # Display the entry points 151 | plt.plot(pd.to_datetime(df['date']), df['close']) 152 | plt.scatter(pd.to_datetime(testset1['date']), testset1['close'], c = (testset1['EarningsBullish']>0)) 153 | plt.title('Entry points \n Yellow = Win, Blue = Loss') 154 | plt.show() 155 | 156 | return(testset1) 157 | 158 | def table_recap(df, stoploss, takeprofit, nPCs, columnA = 'proba1', columnB = 'EarningsBullish'): 159 | ''' Summarize the strategy by steps of 0.05, depending on which column (i.e. strategy) 160 | we choose''' 161 | recap = pd.DataFrame(np.zeros((int(10), 0))) 162 | recap['stoploss'] = stoploss 163 | recap['takeprofit'] = takeprofit 164 | recap['nPCs'] = nPCs 165 | recap['Min'] = [0.5 + k*0.05 for k in range(10)] 166 | recap['Max'] = 1 167 | recap['ROI%'] = 0 168 | recap['nTrades'] = 0 169 | for i in range(len(recap['Min'])): 170 | min, max = recap['Min'].iloc[i], recap['Max'].iloc[i] 171 | df2 = df[(df[columnA] > min) & (df[columnA] < max)] 172 | recap['ROI%'].iloc[i] = 100 * np.mean(df2[columnB]) 173 | recap['nTrades'].iloc[i] = df2.shape[0] 174 | 175 | return(recap) -------------------------------------------------------------------------------- /LendingBot/lending.py: -------------------------------------------------------------------------------- 1 | import CLASS.POLONIEXAPI 2 | import helper.config 3 | import CLASS.DATABASE 4 | from datetime import datetime,timedelta 5 | import os 6 | import time 7 | 8 | Poloniexapi = CLASS.POLONIEXAPI.POLONIEXAPI() 9 | 10 | Database = CLASS.DATABASE.DATABASE() 11 | Database.connect() 12 | Database.setup() 13 | 14 | def cls(): 15 | os.system('cls' if os.name=='nt' else 'clear') 16 | 17 | def printscreen(): 18 | Tickerdata = Poloniexapi.ticker 19 | AvailableBalancedata = Poloniexapi.availableAccountBalances 20 | ActiveLoans = Poloniexapi.activeLoans 21 | OpenLoans = Poloniexapi.OpenLoanOffers 22 | #CompleteBalance = Poloniexapi.completeBalances 23 | cls() 24 | print("LENDING BOT") 25 | print("USDT_BTC: " + Tickerdata["USDT_BTC"]["last"]) 26 | print("") 27 | if 'lending' in AvailableBalancedata: 28 | for key in AvailableBalancedata["lending"]: 29 | if float(AvailableBalancedata["lending"][key]) > 0.0: 30 | print("You have " + AvailableBalancedata["lending"][key] + " " + str(key) + " available") 31 | else: 32 | print("You have nothing available") 33 | print("") 34 | if ActiveLoans != []: 35 | print("Active Loans") 36 | for key in ActiveLoans["provided"]: 37 | present = datetime.utcnow() 38 | future = datetime.strptime(key["date"], '%Y-%m-%d %H:%M:%S') + timedelta(days = key['duration']) 39 | difference = future - present 40 | print("Currency: " + key["currency"] + " Rate: " + key["rate"] + "% Amount: " + key ["amount"] + " Time to End: " + str(difference)) 41 | print("") 42 | if OpenLoans != []: 43 | print("Open Loans") 44 | for key in OpenLoans["BTC"]: 45 | present = datetime.utcnow() 46 | future = datetime.strptime(key["date"], '%Y-%m-%d %H:%M:%S') + timedelta(days = key['duration']) 47 | difference = future - present 48 | print("Rate: " + key["rate"] + "% Amount: " + key ["amount"]) 49 | print("") 50 | print(str(Poloniexapi.completeBalancesSum()) + " BTC") 51 | 52 | 53 | def check(): 54 | AvailableBalancedata = Poloniexapi.availableAccountBalances 55 | ActiveLoans = Poloniexapi.activeLoans 56 | HistoryLoans = Poloniexapi.LendingHistory 57 | if 'lending' in AvailableBalancedata: 58 | if float(AvailableBalancedata['lending']['BTC']) > 0.005: 59 | avg = Poloniexapi.avgLoanOrders('BTC') 60 | completeammount = AvailableBalancedata['lending']['BTC'] 61 | print("Genug BTC zum Lending") 62 | print("{:.8f}".format(avg)) 63 | print(completeammount) 64 | if conf['activeLending'] == "True": 65 | Poloniexapi.createLoanOffer(completeammount,avg) 66 | if conf['Database'] == "True": 67 | for key in ActiveLoans["provided"]: 68 | Database.insertActiveLoan(key) 69 | Database.insertHistoryLoan(HistoryLoans) 70 | Database.insertTicker(Poloniexapi.ticker) 71 | Database.insertBTC(Poloniexapi.ticker["USDT_BTC"]["last"]) 72 | 73 | 74 | 75 | while True: 76 | conf = helper.config.initconfig() 77 | Poloniexapi.request() 78 | printscreen() 79 | check() 80 | 81 | time.sleep(10) -------------------------------------------------------------------------------- /LendingBot/telegrambot.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # pylint: disable=C0116,W0613 3 | # This program is dedicated to the public domain under the CC0 license. 4 | 5 | """ 6 | Simple Bot to reply to Telegram messages. 7 | 8 | First, a few handler functions are defined. Then, those functions are passed to 9 | the Dispatcher and registered at their respective places. 10 | Then, the bot is started and runs until we press Ctrl-C on the command line. 11 | 12 | Usage: 13 | Basic Echobot example, repeats messages. 14 | Press Ctrl-C on the command line or send a signal to the process to stop the 15 | bot. 16 | """ 17 | 18 | import logging 19 | import helper.config 20 | import CLASS.POLONIEXAPI 21 | from datetime import datetime,timedelta 22 | 23 | from telegram import Update, ForceReply, ReplyKeyboardMarkup 24 | from telegram.ext import Updater, CommandHandler, MessageHandler, Filters, CallbackContext 25 | 26 | conf = helper.config.initconfig() 27 | 28 | Poloniexapi = CLASS.POLONIEXAPI.POLONIEXAPI() 29 | 30 | # Enable logging 31 | logging.basicConfig( 32 | format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=logging.INFO 33 | ) 34 | 35 | logger = logging.getLogger(__name__) 36 | 37 | reply_keyboard = [ 38 | ['/status'], 39 | ['/help'], 40 | ['/Lend_on','/Lend_off'] 41 | ] 42 | #markup = ReplyKeyboardMarkup(reply_keyboard, one_time_keyboard=True) 43 | markup = ReplyKeyboardMarkup(reply_keyboard) 44 | 45 | # Define a few command handlers. These usually take the two arguments update and 46 | # context. 47 | def start(update: Update, context: CallbackContext) -> None: 48 | """Send a message when the command /start is issued.""" 49 | user = update.effective_user 50 | update.message.reply_markdown_v2( 51 | fr'Hi {user.mention_markdown_v2()}\!', 52 | reply_markup=markup, 53 | ) 54 | 55 | def status_command(update: Update, context: CallbackContext) -> None: 56 | """Send a message when the command /help is issued.""" 57 | Poloniexapi.request() 58 | AvailableBalancedata = Poloniexapi.availableAccountBalances 59 | ActiveLoans = Poloniexapi.activeLoans 60 | OpenLoans = Poloniexapi.OpenLoanOffers 61 | Text = "" 62 | if 'lending' in AvailableBalancedata: 63 | for key in AvailableBalancedata["lending"]: 64 | if float(AvailableBalancedata["lending"][key]) > 0.0: 65 | Text = Text + ("You have " + AvailableBalancedata["lending"][key] + " " + str(key) + " available\n") 66 | else: 67 | Text = Text +("You have nothing available\n") 68 | if 'lending' in AvailableBalancedata: 69 | for key in AvailableBalancedata["lending"]: 70 | if float(AvailableBalancedata["lending"][key]) > 0.0: 71 | Text = Text + ("You have " + AvailableBalancedata["lending"][key] + " " + str(key) + " available") 72 | if ActiveLoans != []: 73 | Text = Text + "Active Loans:\n" 74 | for key in ActiveLoans["provided"]: 75 | present = datetime.utcnow() 76 | future = datetime.strptime(key["date"], '%Y-%m-%d %H:%M:%S') + timedelta(days = key['duration']) 77 | difference = future - present 78 | Text = Text + ("Currency: " + key["currency"] + " Rate: " + key["rate"] + "% Amount: " + key ["amount"] + " Time to End: " + str(difference)) + "\n" 79 | if OpenLoans != []: 80 | Text = Text + "Open Loans:\n" 81 | for key in OpenLoans["BTC"]: 82 | present = datetime.utcnow() 83 | future = datetime.strptime(key["date"], '%Y-%m-%d %H:%M:%S') + timedelta(days = key['duration']) 84 | difference = future - present 85 | Text = Text + ("Rate: " + key["rate"] + "% Amount: " + key ["amount"]) + "\n" 86 | 87 | Text = Text + (str(Poloniexapi.completeBalancesSum()) + " BTC") 88 | update.message.reply_text(Text,reply_markup=markup) 89 | 90 | def lend_on_command(update: Update, context: CallbackContext) -> None: 91 | helper.config.setactiveLendingTrue() 92 | update.message.reply_text('Lending On',reply_markup=markup) 93 | 94 | def lend_off_command(update: Update, context: CallbackContext) -> None: 95 | helper.config.setactiveLendingFalse() 96 | update.message.reply_text('Lending Off',reply_markup=markup) 97 | 98 | def help_command(update: Update, context: CallbackContext) -> None: 99 | """Send a message when the command /help is issued.""" 100 | update.message.reply_text('/status\n/help',reply_markup=markup) 101 | 102 | 103 | def echo(update: Update, context: CallbackContext) -> None: 104 | """Echo the user message.""" 105 | #update.message.reply_text(update.message.text) 106 | update.message.reply_text("unknown command") 107 | 108 | 109 | def main() -> None: 110 | """Start the bot.""" 111 | # Create the Updater and pass it your bot's token. 112 | updater = Updater(conf["telegramtoken"]) 113 | 114 | # Get the dispatcher to register handlers 115 | dispatcher = updater.dispatcher 116 | 117 | # on different commands - answer in Telegram 118 | dispatcher.add_handler(CommandHandler("start", start)) 119 | dispatcher.add_handler(CommandHandler("help", help_command)) 120 | dispatcher.add_handler(CommandHandler("status", status_command)) 121 | dispatcher.add_handler(CommandHandler("Lend_on", lend_on_command)) 122 | dispatcher.add_handler(CommandHandler("Lend_off", lend_off_command)) 123 | 124 | # on non command i.e message - echo the message on Telegram 125 | dispatcher.add_handler(MessageHandler(Filters.text & ~Filters.command, help_command)) 126 | 127 | # Start the Bot 128 | updater.start_polling() 129 | 130 | # Run the bot until you press Ctrl-C or the process receives SIGINT, 131 | # SIGTERM or SIGABRT. This should be used most of the time, since 132 | # start_polling() is non-blocking and will stop the bot gracefully. 133 | updater.idle() 134 | 135 | 136 | if __name__ == '__main__': 137 | main() 138 | -------------------------------------------------------------------------------- /LendingBot/test.py: -------------------------------------------------------------------------------- 1 | import helper.poloniexApi 2 | 3 | #print(helper.poloniexApi.createLoanOffer(0.01,0.00001600)) 4 | #print(helper.poloniexApi.returnLendingHistory()) 5 | print(helper.poloniexApi.avgLoanOrders()) -------------------------------------------------------------------------------- /LendingBot/trading.py: -------------------------------------------------------------------------------- 1 | ########### PARTS I to III 2 | import os 3 | import pandas as pd 4 | import numpy as np 5 | import pickle as pk 6 | from sklearn.decomposition import PCA 7 | from sklearn.preprocessing import StandardScaler 8 | import matplotlib.pyplot as plt 9 | import requests 10 | import json 11 | import time 12 | import csv 13 | import helper.tradingfunctions 14 | import helper.requestcsv 15 | from keras.models import Sequential 16 | from keras.layers import Dense 17 | from scipy.stats import ttest_1samp 18 | 19 | # Creating subfolders if they don't exist ... 20 | if not os.path.exists('./Data'): 21 | os.makedirs('./Data') 22 | if not os.path.exists('./Models'): 23 | os.makedirs('./Models') 24 | if not os.path.exists('./Results'): 25 | os.makedirs('./Results') 26 | 27 | CurrencyPair = "USDT_BTC" 28 | Period = "7200" 29 | 30 | helper.requestcsv.request(CurrencyPair,Period) 31 | 32 | df = pd.read_csv('./Data/' + CurrencyPair + '_Poloniex_all_' + Period + '.csv') 33 | print("Bug 1", df.shape) 34 | 35 | #df = pd.DataFrame(data) 36 | df["date"] = pd.to_datetime(df["date"],unit='s') 37 | df = df.sort_values(by = 'date') 38 | 39 | # Keep only 5 basic information + the date 40 | df = df[['close', 'date', 'high', 'low', 'open', 'volume']] 41 | 42 | # Define limit between train set and testset 43 | #TODO variable limits 44 | start_validation = '2018-12-21 12:00:00' 45 | start_test = '2019-12-21 12:00:00' 46 | stoploss = 0.05 47 | takeprofit = 0.1 48 | 49 | #TODO Hier eine Schleife bauen, die verschiedene Stoploss and Takeprofits ausprobiert 50 | #################### II - Build the function to compute variables ################# 51 | df = helper.tradingfunctions.compute_variables1(df) 52 | df.to_csv('./Data/DatasetWithVariables.csv', index = False) 53 | print("Bug 2", df.shape) 54 | 55 | #################### III - Compute the output ####################################### 56 | df = helper.tradingfunctions.compute_result(df, stoploss, takeprofit) 57 | df = df[df['result']>=0] # Only keep observations where we also have the result 58 | df.to_csv('./Data/DatasetWithVariablesAndY_stoploss{}_takeprofit{}.csv'.format(stoploss, takeprofit), index = False) 59 | print("Bug 3", df.shape) 60 | 61 | #################### IV - Apply PCA and save results ############################## 62 | # First we define the trainset, validation set, testset. This is important in this step to avoid causality issues. 63 | #TODO mit in der Schleife 64 | k = df.shape[0] 65 | trainset = df[:int(k*.25)] #df[df['date'] < start_validation] 66 | validation_set = df[int(k*.25):int(k*.75)] #df[(df['date'] >= start_validation) & (df['date'] < start_test)] 67 | testset = df[int(k*.75):] #df[df['date'] > start_test] 68 | 69 | trainset.to_csv('./Data/TrainSet_stoploss{}_takeprofit{}.csv'.format(stoploss, takeprofit), index = False) 70 | validation_set.to_csv('./Data/ValidationSet_stoploss{}_takeprofit{}.csv'.format(stoploss, takeprofit), index = False) 71 | testset.to_csv('./Data/TestSet_stoploss{}_takeprofit{}.csv'.format(stoploss, takeprofit), index = False) 72 | 73 | # Display the splitting 74 | # plt.plot(pd.to_datetime(trainset['date']), trainset['close'], c = 'orange') 75 | # plt.plot(pd.to_datetime(validation_set['date']), validation_set['close'], c = 'b') 76 | # plt.plot(pd.to_datetime(testset['date']), testset['close'], c = 'g') 77 | # plt.title('Repartition between trainset, validation set, and test set') 78 | # plt.show() 79 | 80 | # (i) Scale the variables 81 | scale_fct = StandardScaler() 82 | scale_fct.fit(trainset.drop('date', 1).drop('result', 1)) 83 | pk.dump(scale_fct, open('./Models/scaler.pkl','wb')) 84 | 85 | # (ii) Apply PCA 86 | pca = PCA(n_components=trainset.shape[1] - 2) # remove the result and the date 87 | pca.fit(scale_fct.transform(trainset.drop('date', 1).drop('result', 1))) 88 | pk.dump(pca, open('./Models/pca.pkl',"wb")) 89 | 90 | # (iii) Scale PCA components (this accelerates training process in Deep Learning) 91 | pca_scaler = StandardScaler() 92 | pca_scaler.fit(pca.transform(scale_fct.transform(trainset.drop('date', 1).drop('result', 1)))) 93 | pk.dump(pca_scaler, open('./Models/pca_scaler.pkl','wb')) 94 | 95 | # (iv) Save ready-to-use versions (i.e. datasets after applying scalers and PCA) 96 | trainset_final = pd.DataFrame(pca_scaler.transform(pca.transform(scale_fct.transform(trainset.drop('date', 1).drop('result', 1))))) 97 | validation_set_final = pd.DataFrame(pca_scaler.transform(pca.transform(scale_fct.transform(validation_set.drop('date', 1).drop('result', 1))))) 98 | testset_final = pd.DataFrame(pca_scaler.transform(pca.transform(scale_fct.transform(testset.drop('date', 1).drop('result', 1))))) 99 | 100 | trainset_final.to_csv('./Data/TrainSet_final_stoploss{}_takeprofit{}.csv'.format(stoploss, takeprofit), index = False) 101 | validation_set_final.to_csv('./Data/ValidationSet_final_stoploss{}_takeprofit{}.csv'.format(stoploss, takeprofit), index = False) 102 | testset_final.to_csv('./Data/TestSet_final_stoploss{}_takeprofit{}.csv'.format(stoploss, takeprofit), index = False) 103 | 104 | 105 | list_nPCs = [5, 10, 15, 20, 25, 30, 35, 40] 106 | 107 | # (a) Load previously built datasets : we just need train sets and validation sets here 108 | trainset_final = pd.read_csv('./Data/TrainSet_final_stoploss{}_takeprofit{}.csv'.format(stoploss, takeprofit)) 109 | trainset = pd.read_csv('./Data/TrainSet_stoploss{}_takeprofit{}.csv'.format(stoploss, takeprofit)) 110 | 111 | validation_set_final = pd.read_csv('./Data/ValidationSet_final_stoploss{}_takeprofit{}.csv'.format(stoploss, takeprofit)) 112 | validation_set = pd.read_csv('./Data/ValidationSet_stoploss{}_takeprofit{}.csv'.format(stoploss, takeprofit)) 113 | 114 | # (b) Build and train several models different amounts of PCs 115 | for nPCs in list_nPCs: 116 | print(nPCs) 117 | X = trainset_final.iloc[:, :nPCs] 118 | y = trainset["result"] 119 | 120 | # Build model and train it 121 | classifier = Sequential() 122 | #First Hidden Layer 123 | classifier.add(Dense(32, activation='relu', kernel_initializer='random_normal', input_dim=nPCs)) 124 | #Second, third and fourth hidden Layers 125 | classifier.add(Dense(32, activation='relu', kernel_initializer='random_normal')) 126 | classifier.add(Dense(16, activation='relu', kernel_initializer='random_normal')) 127 | classifier.add(Dense(16, activation='relu', kernel_initializer='random_normal')) 128 | classifier.add(Dense(16, activation='relu', kernel_initializer='random_normal')) 129 | classifier.add(Dense(16, activation='relu', kernel_initializer='random_normal')) 130 | 131 | #Output Layer 132 | classifier.add(Dense(1, activation='sigmoid', kernel_initializer='random_normal')) 133 | #Compiling the neural network 134 | classifier.compile(optimizer ='adam',loss='binary_crossentropy', metrics =['accuracy']) 135 | #Fitting the data to the training dataset 136 | classifier.fit(X,y, batch_size=500, epochs=75, verbose =1) 137 | 138 | # pk.dump(classifier, open("./Models/DL_model_{}PC_stoploss{}_takeprofit{}.pkl".format(nPCs, stoploss, takeprofit),"wb")) 139 | classifier.save_weights('./Models/DL_model_{}PC_stoploss{}_takeprofit{}.h5'.format(nPCs, stoploss, takeprofit)) 140 | 141 | # (c) Test onto the testset : we compare all models and store results in a csv file 142 | accuracies, nPCs_list = [], [] 143 | for nPCs in list_nPCs: 144 | print(nPCs) 145 | # Build model and train it 146 | #TODO wird das hier überhaupt abgearbeitet? 147 | clf = Sequential() 148 | #First Hidden Layer 149 | clf.add(Dense(32, activation='relu', kernel_initializer='random_normal', input_dim=nPCs)) 150 | #Second, third and fourth hidden Layers 151 | clf.add(Dense(32, activation='relu', kernel_initializer='random_normal')) 152 | clf.add(Dense(16, activation='relu', kernel_initializer='random_normal')) 153 | clf.add(Dense(16, activation='relu', kernel_initializer='random_normal')) 154 | clf.add(Dense(16, activation='relu', kernel_initializer='random_normal')) 155 | clf.add(Dense(16, activation='relu', kernel_initializer='random_normal')) 156 | 157 | #Output Layer 158 | clf.add(Dense(1, activation='sigmoid', kernel_initializer='random_normal')) 159 | # with open("./Models/DL_model_{}PC_stoploss{}_takeprofit{}.pkl".format(nPCs, stoploss, takeprofit), 'rb') as f: 160 | # clf = pk.load(f) 161 | clf.load_weights('./Models/DL_model_{}PC_stoploss{}_takeprofit{}.h5'.format(nPCs, stoploss, takeprofit)) 162 | # Compute predictions on testset 163 | preds = (clf.predict(validation_set_final.iloc[:, :nPCs]) > 0.5)*1 164 | 165 | # Assess accuracy on Bullish predictions only (because we will only perform Bullish trades IRL) : we prioritize selectivity 166 | validation_set1 = validation_set[preds == 1].copy() 167 | accuracies.append(np.mean(preds == list(validation_set1['result']))) 168 | nPCs_list.append(nPCs) 169 | 170 | recap = pd.DataFrame({'nPCs' : list(nPCs_list), 'Accuracy' : (list(accuracies))}) 171 | recap.to_csv('./Results/Comparative_All_models_stoploss{}_takeprofit{}.csv'.format(stoploss, takeprofit), index = False) 172 | print(recap) 173 | 174 | 175 | ################### (a) Load previously built datasets 176 | fees = 0.00155 # transaction fees : 0.125% for example 177 | 178 | nTrades_mini = 50 # minimal number of trades we want over the test set: this is for second approach 179 | 180 | trainset_final = pd.read_csv('./Data/TrainSet_final_stoploss{}_takeprofit{}.csv'.format(stoploss, takeprofit)) 181 | trainset = pd.read_csv('./Data/TrainSet_stoploss{}_takeprofit{}.csv'.format(stoploss, takeprofit)) 182 | 183 | validation_set_final = pd.read_csv('./Data/ValidationSet_final_stoploss{}_takeprofit{}.csv'.format(stoploss, takeprofit)) 184 | validation_set = pd.read_csv('./Data/ValidationSet_stoploss{}_takeprofit{}.csv'.format(stoploss, takeprofit)) 185 | 186 | testset_final = pd.read_csv('./Data/TestSet_final_stoploss{}_takeprofit{}.csv'.format(stoploss, takeprofit)) 187 | testset = pd.read_csv('./Data/TestSet_stoploss{}_takeprofit{}.csv'.format(stoploss, takeprofit)) 188 | 189 | #################### (b) Basic strategy : pick the best model and bet on bullish trends over the testset 190 | recap = pd.read_csv('./Results/Comparative_All_models_stoploss{}_takeprofit{}.csv'.format(stoploss, takeprofit)).sort_values('Accuracy', ascending = False) 191 | nPCs = recap['nPCs'].iloc[0] 192 | 193 | # with open("./Models/DL_model_{}PC_stoploss{}_takeprofit{}.pkl".format(nPCs, stoploss, takeprofit), 'rb') as f: 194 | # clf = pk.load(f) 195 | clf = Sequential() 196 | #First Hidden Layer 197 | #TODO Läuft hier Deep Leraning überhaupt? 198 | clf.add(Dense(32, activation='relu', kernel_initializer='random_normal', input_dim=nPCs)) 199 | #Second, third and fourth hidden Layers 200 | clf.add(Dense(32, activation='relu', kernel_initializer='random_normal')) 201 | clf.add(Dense(16, activation='relu', kernel_initializer='random_normal')) 202 | clf.add(Dense(16, activation='relu', kernel_initializer='random_normal')) 203 | clf.add(Dense(16, activation='relu', kernel_initializer='random_normal')) 204 | clf.add(Dense(16, activation='relu', kernel_initializer='random_normal')) 205 | 206 | #Output Layer 207 | clf.add(Dense(1, activation='sigmoid', kernel_initializer='random_normal')) 208 | # with open("./Models/DL_model_{}PC_stoploss{}_takeprofit{}.pkl".format(nPCs, stoploss, takeprofit), 'rb') as f: 209 | # clf = pk.load(f) 210 | clf.load_weights('./Models/DL_model_{}PC_stoploss{}_takeprofit{}.h5'.format(nPCs, stoploss, takeprofit)) 211 | # Compute predictions on testset 212 | 213 | def predict_and_backtest_bullish(df, df_final, model, stoploss, takeprofit, fees, plotting = True): 214 | '''This functin takes the test set as input (in both shapes) + the model, computes predictions and probabilities, then compute the earnings according to the fees. Finally it can plot the strategy''' 215 | # Compute predictions on testset 216 | df['preds'] = (clf.predict(df_final.iloc[:, :nPCs]) > 0.5)*1 217 | df['proba1'] = clf.predict(df_final.iloc[:, :nPCs]) 218 | 219 | # keep only the timesteps in which the model predicts a bullish trend 220 | testset1 = df[df['preds'] == 1].copy() 221 | 222 | # Compute earnings column 223 | a = helper.tradingfunctions.compute_earnings_loss(stoploss, takeprofit, fees) 224 | testset1['EarningsBullish'] = (testset1['preds'] == testset1['result'])*a[0] + (testset1['preds'] != testset1['result'])*a[1] 225 | 226 | if plotting: 227 | # Now plot our trading strategy 228 | plt.plot(pd.to_datetime(testset1['date']), np.cumsum(testset1['EarningsBullish'])) 229 | plt.title('Approach over the test set \n ROI = {} %'.format(100*np.mean(testset1['EarningsBullish']))) 230 | plt.xlabel('Date') 231 | plt.xlabel('Cumulative Earnings') 232 | plt.show() 233 | 234 | # Display the entry points 235 | plt.plot(pd.to_datetime(df['date']), df['close']) 236 | plt.scatter(pd.to_datetime(testset1['date']), testset1['close'], c = (testset1['EarningsBullish']>0)) 237 | plt.title('Entry points \n Yellow = Win, Blue = Loss') 238 | plt.show() 239 | 240 | return(testset1) 241 | 242 | testset1 = predict_and_backtest_bullish(testset, testset_final, clf, stoploss, takeprofit, fees, plotting = True) 243 | 244 | # Assess the performance by comparing to if we always traded bullish blindly over the period 245 | a = helper.tradingfunctions.compute_earnings_loss(stoploss, takeprofit, fees) 246 | testset_benchmark = testset.copy() 247 | testset_benchmark['EarningsBullish'] = (testset['result'] == 1)*a[0] + (testset['result'] == 0)*a[1] 248 | avg_return_benchmark = np.mean(testset_benchmark['EarningsBullish']) 249 | 250 | # Now let's look at our approach's performance and std 251 | p_value = ttest_1samp(testset1['EarningsBullish'], popmean = avg_return_benchmark)[1] 252 | print('Our model has an average ROI of {} %, while trading blindly bullish over the same period yielded a ROI of {} %, when we perform statistical testing of difference there is a p-value of {}.'.format(100*np.mean(testset1['EarningsBullish']), 100*avg_return_benchmark, p_value)) 253 | 254 | ################### (c) More evolved strategy : look for the threshold to limit to the cases where p>a (for the best model) 255 | # (i) Identify best threshold 256 | # Load best model 257 | recap = pd.read_csv('./Results/Comparative_All_models_stoploss{}_takeprofit{}.csv'.format(stoploss, takeprofit)).sort_values('Accuracy', ascending = False) 258 | nPCs = recap['nPCs'].iloc[0] 259 | # with open("./Models/DL_model_{}PC_stoploss{}_takeprofit{}.pkl".format(nPCs, stoploss, takeprofit), 'rb') as f: 260 | # clf = pk.load(f) 261 | clf = Sequential() 262 | #First Hidden Layer 263 | clf.add(Dense(32, activation='relu', kernel_initializer='random_normal', input_dim=nPCs)) 264 | #Second, third and fourth hidden Layers 265 | clf.add(Dense(32, activation='relu', kernel_initializer='random_normal')) 266 | clf.add(Dense(16, activation='relu', kernel_initializer='random_normal')) 267 | clf.add(Dense(16, activation='relu', kernel_initializer='random_normal')) 268 | clf.add(Dense(16, activation='relu', kernel_initializer='random_normal')) 269 | clf.add(Dense(16, activation='relu', kernel_initializer='random_normal')) 270 | 271 | #Output Layer 272 | clf.add(Dense(1, activation='sigmoid', kernel_initializer='random_normal')) 273 | # with open("./Models/DL_model_{}PC_stoploss{}_takeprofit{}.pkl".format(nPCs, stoploss, takeprofit), 'rb') as f: 274 | # clf = pk.load(f) 275 | clf.load_weights('./Models/DL_model_{}PC_stoploss{}_takeprofit{}.h5'.format(nPCs, stoploss, takeprofit)) 276 | # Compute predictions on testset 277 | 278 | # Compute predictions on validation_set 279 | validation_set = predict_and_backtest_bullish(validation_set, validation_set_final, clf, stoploss, takeprofit, fees, plotting = False) 280 | 281 | # Compute recapitulative table 282 | recap = helper.tradingfunctions.table_recap(validation_set, stoploss, takeprofit, nPCs) 283 | recap.to_csv('./Results/Recapitulative_result_stoploss{}_takeprofit{}_{}PCs.csv'.format(stoploss, takeprofit, nPCs), index = False) 284 | 285 | # Pick the most profitable 286 | recap = recap.sort_values('ROI%', ascending = False) 287 | recap = recap[recap['nTrades'] > nTrades_mini] 288 | print(recap) 289 | min, max = recap['Min'].iloc[0], recap['Max'].iloc[0] 290 | 291 | 292 | # (ii) Now that we have identified the best threshold, filter the predictions 293 | # ...Finally : we plot our strategy 294 | testset2 = predict_and_backtest_bullish(testset, testset_final, clf, stoploss, takeprofit, fees, plotting = False) 295 | testset2 = testset2[(testset2['proba1'] > min) & (testset2['proba1'] < max)].copy() 296 | 297 | # Now plot our trading strategy 298 | plt.plot(pd.to_datetime(testset2['date']), np.cumsum(testset2['EarningsBullish'])) 299 | plt.title('Approach n°2 over the test set \n ROI = {} %'.format(100*np.mean(testset2['EarningsBullish']))) 300 | plt.xlabel('Date') 301 | plt.xlabel('Cumulative Earnings') 302 | plt.show() 303 | 304 | # Display the entry points 305 | plt.plot(pd.to_datetime(testset['date']), testset['close']) 306 | plt.scatter(pd.to_datetime(testset2['date']), testset2['close'], c = (testset2['EarningsBullish']>0)) 307 | plt.title('Entry points \n Yellow = Win, Blue = Loss') 308 | plt.show() 309 | 310 | # (iii) Assess the performance by comparing to if we always traded bullish over the period 311 | a = helper.tradingfunctions.compute_earnings_loss(stoploss, takeprofit, fees) 312 | testset_benchmark = testset.copy() 313 | testset_benchmark['EarningsBullish'] = (testset['result'] == 1)*a[0] + (testset['result'] == 0)*a[1] 314 | avg_return_benchmark = np.mean(testset_benchmark['EarningsBullish']) 315 | 316 | # Now let's look at our approach's performance and std 317 | p_value = ttest_1samp(testset2['EarningsBullish'], popmean = avg_return_benchmark)[1] 318 | print('Our model has an average ROI of {} %, while trading blindly bullish over the same period yielded a ROI of {} %, when we perform statistical testing of difference there is a p-value of {}.'.format(100*np.mean(testset2['EarningsBullish']), 100*avg_return_benchmark, p_value)) -------------------------------------------------------------------------------- /SECURITY.md: -------------------------------------------------------------------------------- 1 | # Security Policy 2 | 3 | ## Supported Versions 4 | 5 | Use this section to tell people about which versions of your project are 6 | currently being supported with security updates. 7 | 8 | | Version | Supported | 9 | | ------- | ------------------ | 10 | | 1.0.x | :white_check_mark: | 11 | | 0.0.x | :x: | 12 | 13 | ## Reporting a Vulnerability 14 | 15 | Use this section to tell people how to report a vulnerability. 16 | 17 | Tell them where to go, how often they can expect to get an update on a 18 | reported vulnerability, what to expect if the vulnerability is accepted or 19 | declined, etc. 20 | -------------------------------------------------------------------------------- /config/.gitignore: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/VeteranSoftDev/Lending-Bot/37ad4c3418a3024f6af029af1331202db9aa266c/config/.gitignore -------------------------------------------------------------------------------- /install.sh: -------------------------------------------------------------------------------- 1 | #https://www.arangodb.com/download-major/ubuntu/ 2 | #Docker sudo docker run -p 8529:8529 -e ARANGO_ROOT_PASSWORD=openSesame arangodb/arangodb:3.7.12 3 | sudo apt install curl -y 4 | curl -OL https://download.arangodb.com/arangodb37/DEBIAN/Release.key 5 | sudo apt-key add - < Release.key 6 | echo 'deb https://download.arangodb.com/arangodb37/DEBIAN/ /' | sudo tee /etc/apt/sources.list.d/arangodb.list 7 | sudo apt-get install apt-transport-https -y 8 | sudo apt-get update 9 | sudo apt-get install arangodb3=3.7.12-1 10 | git submodule init 11 | git submodule update 12 | sudo apt install python3-pip 13 | pip install --upgrade poloniexapi pyArango pandas sklearn keras tensorflow --------------------------------------------------------------------------------