| Title: | Automated Trading Using Tweets |
|---|---|
| Description: | Provides an integration to the 'metatrader 5'. The functionalities carry out automated trading using sentiment indexes computed from 'twitter' and/or 'stockwits'. The sentiment indexes are based on the ph.d. dissertation "Essays on Economic Forecasting Models" (Godeiro,2018) <https://repositorio.ufpb.br/jspui/handle/123456789/15198> The integration between the 'R' and the 'metatrader 5' allows sending buy/sell orders to the brokerage. |
| Authors: | Lucas Godeiro |
| Maintainer: | Lucas Godeiro <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2024-10-29 04:16:47 UTC |
| Source: | https://github.com/lucasgodeiro/twitterautomatedtrading |
This functions checks if the EA can send order to the plataform trading.
check_frequency(hours_frequency, time_zone)check_frequency(hours_frequency, time_zone)
hours_frequency |
The vector containing the hours of operations. |
time_zone |
The time zone. |
A logical vector TRUE if the EA can compute the sentiment.
time_zone <- "Brazil/East" hour_freq <- generate_trade_frequency(9,17,10) check_freq <- check_frequency(hours_frequency = hour_freq, time_zone = time_zone)time_zone <- "Brazil/East" hour_freq <- generate_trade_frequency(9,17,10) check_freq <- check_frequency(hours_frequency = hour_freq, time_zone = time_zone)
This functions closes a open position.
Close_Position(actual_decision)Close_Position(actual_decision)
actual_decision |
The current position status("BUY IT NOW","SELL IT NOW", "SELL IT NOW CLOSE", "BUY IT NOW CLOSE" ). |
A vector with the new decision.
decision <- 'SELL IT NOW' decision <- Close_Position(actual_decision = decision)decision <- 'SELL IT NOW' decision <- Close_Position(actual_decision = decision)
generate_trade_frequency function
generate_trade_frequency(initial_time, final_time, freq_trade)generate_trade_frequency(initial_time, final_time, freq_trade)
initial_time |
The time the algorithm starts trading. |
final_time |
The time the algorithm ends trading. |
freq_trade |
The frequency which the algorithm recalculates the sentiment index. |
A vector containing the hours of operation.
hours_candle_10 <- generate_trade_frequency(9,17,10) #For example, for 17:30, you should use minutes/60, i.e. 17.5 hours_candle_20 <- generate_trade_frequency(9,17.5,10)hours_candle_10 <- generate_trade_frequency(9,17,10) #For example, for 17:30, you should use minutes/60, i.e. 17.5 hours_candle_20 <- generate_trade_frequency(9,17.5,10)
get_sentiment_index_tweets
get_sentiment_index_tweets( ntweets, time_tweet, terms_list, time_zone, positive_dictionary, negative_dictionary, sentiment_index_type, freq_tweets )get_sentiment_index_tweets( ntweets, time_tweet, terms_list, time_zone, positive_dictionary, negative_dictionary, sentiment_index_type, freq_tweets )
ntweets |
Number of tweets to be searched |
time_tweet |
Time in hours where the tweets will be filtered |
terms_list |
Terms to be searched |
time_zone |
The time zone |
positive_dictionary |
The list of positive terms of the dictionary |
negative_dictionary |
The list of negative terms of the dictionarya tibble with the words counting |
sentiment_index_type |
The sentiment type to be used according to the dictionary, positive, negative or both. Default is both, positive and negative |
freq_tweets |
The frequency of the sentiment index in minutes. |
A list with: (1) - the sentiment index, (2) a tibble with the words counting, (3) a tibble with the negative words counting and (4) the positive and negative words in a xts object
## Not run: #Not run: ntweets <- 500 time_tweet <- 6 terms_list <- c("IBOVESPA OR bovespa OR ibov OR petroleo OR $SPX OR $SPY OR $EWZ") time_zone <- "Brazil/East" positive_dictionary <- my_dictionary[['positive_terms']] negative_dictionary <- my_dictionary[['negative_terms']] freq_tweets <- '15 mins' sentiment_index <- get_sentiment_tweets(ntweets = ntweets, terms_list = terms_list, time_tweet = time_tweet, time_zone = time_zone, positive_dictionary = positive_dictionary, negative_dictionary = negative_dictionary, freq_tweets = freq_tweets ) sent_idx <- sentiment_index[[1]] sent_wrd <- sentiment_index[[2]] sent_pos <- sentiment_index[[3]] sent_neg <- sentiment_index[[4]] ## End(Not run)## Not run: #Not run: ntweets <- 500 time_tweet <- 6 terms_list <- c("IBOVESPA OR bovespa OR ibov OR petroleo OR $SPX OR $SPY OR $EWZ") time_zone <- "Brazil/East" positive_dictionary <- my_dictionary[['positive_terms']] negative_dictionary <- my_dictionary[['negative_terms']] freq_tweets <- '15 mins' sentiment_index <- get_sentiment_tweets(ntweets = ntweets, terms_list = terms_list, time_tweet = time_tweet, time_zone = time_zone, positive_dictionary = positive_dictionary, negative_dictionary = negative_dictionary, freq_tweets = freq_tweets ) sent_idx <- sentiment_index[[1]] sent_wrd <- sentiment_index[[2]] sent_pos <- sentiment_index[[3]] sent_neg <- sentiment_index[[4]] ## End(Not run)
This function computes the sentiment based on bullish and bearish tag from stocktwits using the last 30 twits.
get_sentiment_stocktwits(stock_symbol, path_twits, sentiment_index_type)get_sentiment_stocktwits(stock_symbol, path_twits, sentiment_index_type)
stock_symbol |
A vector with the stocks symbols. |
path_twits |
The path where the Json files will be stored. |
sentiment_index_type |
The sentiment type to be used according to the dictionary, positive, negative or both. Default is both, positive and negative |
A numeric value with the value of the sentiment index.
## Not run: #Not run: path_twits <- 'your path' symbols <- c("EWZ", "SPX", "SPY", "USO") stocktwits_index <- get_sentiment_stocktwits(stock_symbol = symbols, path_twits = path_twits) ## End(Not run)## Not run: #Not run: path_twits <- 'your path' symbols <- c("EWZ", "SPX", "SPY", "USO") stocktwits_index <- get_sentiment_stocktwits(stock_symbol = symbols, path_twits = path_twits) ## End(Not run)
This function computes the sentiment from tweets. Remind to connect with twitter using your API Key.
get_sentiment_tweets( ntweets, time_tweet, terms_list, time_zone, positive_dictionary, negative_dictionary, sentiment_index_type )get_sentiment_tweets( ntweets, time_tweet, terms_list, time_zone, positive_dictionary, negative_dictionary, sentiment_index_type )
ntweets |
Number of tweets to be searched |
time_tweet |
Time in hours where the tweets will be filtered |
terms_list |
Terms to be searched |
time_zone |
The time zone |
positive_dictionary |
The list of positive terms of the dictionary |
negative_dictionary |
The list of negative terms of the dictionarya tibble with the words counting |
sentiment_index_type |
The sentiment type to be used according to the dictionary, positive, negative or both. Default is both, positive and negative |
A list with: (1) - the sentiment index, (2) a tibble with the words counting, (3) a tibble with the negative words counting and (4
## Not run: #Not run: ntweets <- 500 time_tweet <- 6 terms_list <- c("IBOVESPA OR bovespa OR ibov OR petroleo OR $SPX OR $SPY OR $EWZ") time_zone <- "Brazil/East" positive_dictionary <- my_dictionary[['positive_terms']] negative_dictionary <- my_dictionary[['negative_terms']] sentiment_index <- get_sentiment_tweets(ntweets = ntweets, terms_list = terms_list, time_tweet = time_tweet, time_zone = time_zone, positive_dictionary = positive_dictionary, negative_dictionary = negative_dictionary ) sent_idx <- sentiment_index[[1]] sent_wrd <- sentiment_index[[2]] sent_pos <- sentiment_index[[3]] sent_neg <- sentiment_index[[4]] ## End(Not run)## Not run: #Not run: ntweets <- 500 time_tweet <- 6 terms_list <- c("IBOVESPA OR bovespa OR ibov OR petroleo OR $SPX OR $SPY OR $EWZ") time_zone <- "Brazil/East" positive_dictionary <- my_dictionary[['positive_terms']] negative_dictionary <- my_dictionary[['negative_terms']] sentiment_index <- get_sentiment_tweets(ntweets = ntweets, terms_list = terms_list, time_tweet = time_tweet, time_zone = time_zone, positive_dictionary = positive_dictionary, negative_dictionary = negative_dictionary ) sent_idx <- sentiment_index[[1]] sent_wrd <- sentiment_index[[2]] sent_pos <- sentiment_index[[3]] sent_neg <- sentiment_index[[4]] ## End(Not run)
Function to test if the internet connection is available
havingIP(operational_system)havingIP(operational_system)
operational_system |
The operational system. |
A logical vector TRUE if internet connection is available.
## Not run: internet <- havingIP() ## End(Not run)## Not run: internet <- havingIP() ## End(Not run)
A simple list containing a dictionary with positive and negative words(English and Portuguese).
my_dictionarymy_dictionary
A list with 2 components.
The positive words.
The negative words.
This function defines the operations hours of the EA.
operation_hours(start_time, end_time, time_zone)operation_hours(start_time, end_time, time_zone)
start_time |
The time that the EA should start to trade. |
end_time |
The time that the EA should stop to trade and close the open positions. |
time_zone |
The time zone. |
A logical variable TRUE if the Expert Advisor can trade.
time_zone <- "Brazil/East" op_hours<- operation_hours(start_time = 9.5, end_time = 17, time_zone = time_zone)time_zone <- "Brazil/East" op_hours<- operation_hours(start_time = 9.5, end_time = 17, time_zone = time_zone)
This function starts the Algorithm and sends the ordes to txt file that will be read for the Expert Advisor in the Metatrader 5.
Start_Trading( consumer_key, consumer_secret, access_token, access_secret, path_decision, ntweets, terms_list, time_tweet, time_zone, positive_dictionary, negative_dictionary, stock_symbol, path_twits, Operation_Hours1, Operation_Hours2, Operation_Hours3, start_time1, start_time2, start_time3, end_time1, end_time2, end_time3, Day_Trade, nap_time_error, initial_time, final_time, freq_trade, w_twitter, w_stocktwits, Sentiment_Index_Threshold, Use_Delta_Sentiment, Signal_File_Name, use_sentiment_tweets, freq_tweets )Start_Trading( consumer_key, consumer_secret, access_token, access_secret, path_decision, ntweets, terms_list, time_tweet, time_zone, positive_dictionary, negative_dictionary, stock_symbol, path_twits, Operation_Hours1, Operation_Hours2, Operation_Hours3, start_time1, start_time2, start_time3, end_time1, end_time2, end_time3, Day_Trade, nap_time_error, initial_time, final_time, freq_trade, w_twitter, w_stocktwits, Sentiment_Index_Threshold, Use_Delta_Sentiment, Signal_File_Name, use_sentiment_tweets, freq_tweets )
consumer_key |
Api Twitter Consumer Key |
consumer_secret |
Api Twitter Consumer Secret |
access_token |
Api Twitter access token |
access_secret |
Api Twitter access secret |
path_decision |
The path where the txt file with the decision will be saved. Generally it is saved in the 'Common' file at Metaquotes folder(see vignette for instructions). |
ntweets |
see get_sentiment_tweets. |
terms_list |
see get_sentiment_tweets. |
time_tweet |
see get_sentiment_tweets. |
time_zone |
see get_sentiment_tweets. |
positive_dictionary |
see get_sentiment_tweets. |
negative_dictionary |
see get_sentiment_tweets. |
stock_symbol |
see get_sentiment_Stocktwits. |
path_twits |
see get_sentiment_Stocktwits. |
Operation_Hours1 |
The operation hours 1 for day trade. TRUE or FALSE. |
Operation_Hours2 |
The operation hours 2 for day trade. TRUE or FALSE. |
Operation_Hours3 |
The operation hours 3 for day trade. TRUE or FALSE. |
start_time1 |
The start time 1 for day trade. |
start_time2 |
The start time 2 for day trade. |
start_time3 |
The start time 3 for day trade. |
end_time1 |
The end time 1 for day trade. |
end_time2 |
The end time 2 for day trade. |
end_time3 |
The end time 3 for day trade. |
Day_Trade |
True for Day Trade. False for Swing Trade. |
nap_time_error |
The time that the EA should take a nap in case of error. |
initial_time |
The start of operation. |
final_time |
The time which the position in day trade mode must be closed. |
freq_trade |
The time in minutes the EA must recompute the sentiment index and take a decision. |
w_twitter |
The weight of the twitter sentiment index. |
w_stocktwits |
The weight of the stocktwits sentiment index. |
Sentiment_Index_Threshold |
see trade_decision function. |
Use_Delta_Sentiment |
see trade_decision function |
Signal_File_Name |
The Signal File Name. |
use_sentiment_tweets |
TRUE to using get_sentiment_index_tweets |
freq_tweets |
The frequency of the twitter sentiment index in minutes. |
The functions just activate the algorithm.
## Not run: Signal_File_Name <- 'Signal.txt' ntweets <- 5000 time_tweet <- 6 terms_list <- c("IBOVESPA OR bovespa OR ibov OR petroleo OR $SPX OR $SPY OR $EWZ") time_zone <- "Brazil/East" positive_dictionary <- my_dictionary[['positive_terms']] negative_dictionary <- my_dictionary[['negative_terms']] path_twits <- 'your path' stock_symbol <- c("EWZ", "SPX", "SPY", "USO") time_zone <- "Brazil/East" consumer_key <- "your consumer_key" consumer_secret <- "your consumer_secret" access_token <- "your access token" access_secret <- " your access secret " nap_time_error <- 7.7 path_decision <- 'metatrader txt file path' path_twits <- 'your path' initial_time <- 9 final_time <- 17 freq_trade <- 10 Day_Trade <- TRUE Operation_Hours1 <- TRUE start_time1 <- 9 end_time1 <- 17 w_twitter <- 0.9 w_stocktwits <- 0.1 Sentiment_Index_Threshold <- 0.5 use_sentiment_tweets <- FALSE freq_tweets <- '15 mins' Start_Trading(consumer_key = consumer_key, consumer_secret = consumer_secret, access_token = access_token, access_secret = access_secret, path_decision = path_decision, ntweets = ntweets, terms_list = terms_list, time_tweet = time_tweet, time_zone = time_zone, positive_dictionary = positive_dictionary, negative_dictionary = negative_dictionary, stock_symbol = stock_symbol, path_twits = path_twits, Operation_Hours1 = TRUE, Operation_Hours2 = FALSE, Operation_Hours3 = FALSE, start_time1 = start_time1, start_time2 = start_time1, start_time3 = start_time1, end_time1 = end_time1, end_time2 = end_time1, end_time3 = end_time1, Day_Trade = TRUE, nap_time_error = nap_time_error, initial_time = initial_time, final_time = final_time, freq_trade = freq_trade, w_twitter = w_twitter, w_stocktwits = w_stocktwits, Sentiment_Index_Threshold = Sentiment_Index_Threshold, Use_Delta_Sentiment = TRUE, Signal_File_Name = Signal_File_Name, use_sentiment_tweets = use_sentiment_tweets, freq_tweets = freq_tweets) ## End(Not run)## Not run: Signal_File_Name <- 'Signal.txt' ntweets <- 5000 time_tweet <- 6 terms_list <- c("IBOVESPA OR bovespa OR ibov OR petroleo OR $SPX OR $SPY OR $EWZ") time_zone <- "Brazil/East" positive_dictionary <- my_dictionary[['positive_terms']] negative_dictionary <- my_dictionary[['negative_terms']] path_twits <- 'your path' stock_symbol <- c("EWZ", "SPX", "SPY", "USO") time_zone <- "Brazil/East" consumer_key <- "your consumer_key" consumer_secret <- "your consumer_secret" access_token <- "your access token" access_secret <- " your access secret " nap_time_error <- 7.7 path_decision <- 'metatrader txt file path' path_twits <- 'your path' initial_time <- 9 final_time <- 17 freq_trade <- 10 Day_Trade <- TRUE Operation_Hours1 <- TRUE start_time1 <- 9 end_time1 <- 17 w_twitter <- 0.9 w_stocktwits <- 0.1 Sentiment_Index_Threshold <- 0.5 use_sentiment_tweets <- FALSE freq_tweets <- '15 mins' Start_Trading(consumer_key = consumer_key, consumer_secret = consumer_secret, access_token = access_token, access_secret = access_secret, path_decision = path_decision, ntweets = ntweets, terms_list = terms_list, time_tweet = time_tweet, time_zone = time_zone, positive_dictionary = positive_dictionary, negative_dictionary = negative_dictionary, stock_symbol = stock_symbol, path_twits = path_twits, Operation_Hours1 = TRUE, Operation_Hours2 = FALSE, Operation_Hours3 = FALSE, start_time1 = start_time1, start_time2 = start_time1, start_time3 = start_time1, end_time1 = end_time1, end_time2 = end_time1, end_time3 = end_time1, Day_Trade = TRUE, nap_time_error = nap_time_error, initial_time = initial_time, final_time = final_time, freq_trade = freq_trade, w_twitter = w_twitter, w_stocktwits = w_stocktwits, Sentiment_Index_Threshold = Sentiment_Index_Threshold, Use_Delta_Sentiment = TRUE, Signal_File_Name = Signal_File_Name, use_sentiment_tweets = use_sentiment_tweets, freq_tweets = freq_tweets) ## End(Not run)
This function takes as arguments the sentiment indexes and returns the decision.
Trade_Decision( Current_Sentiment_Index, Past_Sentiment_Index, Use_Delta_Sentiment, Sentiment_Index_Threshold, past_decision )Trade_Decision( Current_Sentiment_Index, Past_Sentiment_Index, Use_Delta_Sentiment, Sentiment_Index_Threshold, past_decision )
Current_Sentiment_Index |
The current sentiment index |
Past_Sentiment_Index |
The sentiment index in (t-1) |
Use_Delta_Sentiment |
If True the fuction will consider the difference in the sentiment index in the decision. |
Sentiment_Index_Threshold |
The threshold to define if the decision will be following or against the sentiment. |
past_decision |
The last trade decision. |
The vector with the decision.
buy_sell_t1 <- 0.2 buy_sell_t <- 0.5 Use_Delta_Sentiment <- TRUE Sentiment_Index_Threshold <- 0.5 decision <- Trade_Decision(Current_Sentiment_Index = buy_sell_t, Past_Sentiment_Index = buy_sell_t1, Use_Delta_Sentiment = Use_Delta_Sentiment, Sentiment_Index_Threshold = Sentiment_Index_Threshold, past_decision = decision )buy_sell_t1 <- 0.2 buy_sell_t <- 0.5 Use_Delta_Sentiment <- TRUE Sentiment_Index_Threshold <- 0.5 decision <- Trade_Decision(Current_Sentiment_Index = buy_sell_t, Past_Sentiment_Index = buy_sell_t1, Use_Delta_Sentiment = Use_Delta_Sentiment, Sentiment_Index_Threshold = Sentiment_Index_Threshold, past_decision = decision )