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Summary of Methods used: Awesome H2O\ntutorials: , Market Type\nresearch of Van Tharp Institute: ,\nReinforcement Learning R package:\n.", "License": "MIT + file LICENSE", "URL": "https://vladdsm.github.io/myblog_attempt/topics/lazy%20trading/,\nhttps://github.com/vzhomeexperiments/lazytrade", "BugReports": "https://github.com/vzhomeexperiments/lazytrade/issues", "Encoding": "UTF-8", "LazyData": "true", "Language": "en-US", "RoxygenNote": "7.3.2", "Roxygen": "list(markdown = TRUE)", "Config/pak/sysreqs": "make default-jdk libicu-dev libssl-dev libx11-dev", "Repository": "https://vzhomeexperiments.r-universe.dev", "Date/Publication": "2024-07-17 19:19:17 UTC", "RemoteUrl": "https://github.com/vzhomeexperiments/lazytrade", "RemoteRef": "HEAD", "RemoteSha": "200ada0d888fc75b0cdb605648f0d361ff994245", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-16 07:20:15 UTC", "User": "root" }, "MD5sum": "168b42be8d151ee119755baac10cc42e", "_user": "vzhomeexperiments", "_type": "src", "_file": "lazytrade_0.5.4.tar.gz", "_fileid": "b8f9cdf1c8d0ff8a30e5a717990ba0a2032b07a24c8d51cd0c1d9876d5adcae3", "_filesize": 2430708, "_sha256": "b8f9cdf1c8d0ff8a30e5a717990ba0a2032b07a24c8d51cd0c1d9876d5adcae3", "_created": "2026-05-16T07:20:15.000Z", "_published": "2026-06-02T18:35:05.149Z", "_distro": "noble", "_jobs": [ { "job": 79144503709, "time": 171, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7031548348" }, { "job": 79144504012, "time": 160, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7031547324" }, { "job": 79144503724, "time": 175, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7031546232" }, { "job": 79144503815, "time": 191, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7031546016" }, { "job": 79144503188, "time": 200, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7031530279" }, { "job": 79144502714, "time": 141, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7366598638" }, { "job": 79144503880, "time": 113, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7031542337" }, { "job": 79144503897, "time": 89, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7031539876" }, { "job": 79144503543, "time": 92, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7031540112" } ], "_buildurl": "https://github.com/r-universe/vzhomeexperiments/actions/runs/25955939200", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/vzhomeexperiments/lazytrade", "_commit": { "id": "200ada0d888fc75b0cdb605648f0d361ff994245", "author": "Vladimir Zhbanko ", "committer": "GitHub ", "message": "Merge pull request #40 from vzhomeexperiments/dev_0.5.4\n\nDev 0.5.4", "time": 1721243957 }, "_maintainer": { "name": "Vladimir Zhbanko", "email": "vladimir.zhbanko@gmail.com", "login": "vladdsm", "uuid": 20794126 }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.6.0", "role": "Depends" }, { "package": "readr", "role": "Imports" }, { "package": "stringr", "role": "Imports" }, { "package": "dplyr", "role": "Imports" }, { "package": "tibble", "role": "Imports" }, { "package": "lubridate", "role": "Imports" }, { "package": "ggplot2", "role": "Imports" }, { "package": "grDevices", "role": "Imports" }, { "package": "h2o", "role": "Imports" }, { "package": "ReinforcementLearning", "role": "Imports" }, { "package": "openssl", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "cluster", "role": "Imports" }, { "package": "lifecycle", "role": "Imports" }, { "package": "testthat", "version": ">= 2.1.0", "role": "Suggests" }, { "package": "covr", "role": "Suggests" }, { "package": "magrittr", "role": "Suggests" }, { "package": "data.table", "role": "Suggests" }, { "package": "bit64", "role": "Suggests" } ], "_owner": "vzhomeexperiments", "_selfowned": true, "_usedby": 0, "_updates": [], "_tags": [], "_topics": [ "lazy", "lazytrade" ], "_stars": 23, "_contributors": [ { "user": "vzhomeexperiments", "count": 236, "uuid": 28510230 }, { "user": "vladdsm", "count": 2, "uuid": 20794126 } ], "_userbio": { "uuid": 28510230, "type": "user", "name": "Vladimir Zhbanko", "description": "Senior Engineering Specialist and Instructor. 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], "fields": [ "MagicNumber", "TicketNumber", "OrderStartTime", "OrderCloseTime", "Profit", "Symbol", "OrderType" ], "rows": 121, "table": true, "tojson": true }, { "name": "result_prev", "title": "Table with one column as result from the model prediction", "object": "result_prev", "class": [ "data.frame" ], "fields": [ "predict" ], "rows": 1048, "table": true, "tojson": true }, { "name": "result_R", "title": "Table with predicted price change", "object": "result_R", "class": [ "data.frame" ], "fields": [ "predict" ], "rows": 271, "table": true, "tojson": true }, { "name": "result_R1", "title": "Table with aggregated trade results", "object": "result_R1", "class": [ "data.frame" ], "fields": [ "predict" ], "rows": 600, "table": true, "tojson": true }, { "name": "test_data_pattern", "title": "Table with several columns containing indicator values and Label values", "object": "test_data_pattern", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "LABEL", "V1", "V2", "V3", "V4", 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"title": "Table with trade data and joined market type info", "object": "trading_systemDF", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "MagicNumber.x", "TicketNumber", "OrderStartTime", "OrderCloseTime", "Profit", "Symbol", "OrderType", "MagicNumber.y", "MarketType" ], "rows": 16, "table": true, "tojson": true }, { "name": "x_test_model", "title": "Table with a dataset to test the Model", "object": "x_test_model", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "LABEL", "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10", "X11", "X12", "X13", "X14", "X15", "X16", "X17", "X18", "X19", "X20", "X21", "X22", "X23", "X24", "X25", "X26", "X27", "X28", "X29", "X30", "X31", "X32", "X33", "X34", "X35", "X36", "X37", "X38", "X39", "X40", "X41", "X42", "X43", "X44", "X45", "X46", "X47", "X48", "X49", "X50", "X51", "X52", "X53", "X54", "X55", "X56", "X57", "X58", "X59", "X60", "X61", "X62", "X63", "X64", "X65", "X66", "X67", "X68", "X69", "X70", "X71", "X72", "X73", "X74", "X75" ], "rows": 271, "table": true, "tojson": true }, { "name": "y", "title": "Table with indicators and price change which is used to train model", "object": "y", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10", "X11", "X12", "X13", "X14", "X15", "X16", "X17", "X18", "X19", "LABEL" ], "rows": 2200, "table": true, "tojson": true } ], "_help": [ { "page": "aml_collect_data", "title": "Function to read, transform, aggregate and save data for further retraining of regression model for a single asset", "topics": [ "aml_collect_data" ] }, { "page": "aml_consolidate_results", "title": "Function to consolidate model test results", "topics": [ "aml_consolidate_results" ] }, { "page": "aml_make_model", "title": "Function to train Deep Learning regression model for a single asset", "concept": [ "see https://docs.h2o.ai/h2o-tutorials/latest-stable/tutorials/deeplearning/index.html" ], "topics": [ "aml_make_model" ] }, { "page": "aml_score_data", "title": "Function to score new data and predict change for each single currency pair", "topics": [ "aml_score_data" ] }, { "page": "aml_simulation", "title": "Function to simulate multiple input structures", "topics": [ "aml_simulation" ] }, { "page": "aml_test_model", "title": "Function to test the model and conditionally decide to update existing model for a single currency pair", "topics": [ "aml_test_model" ] }, { "page": "check_if_optimize", "title": "Function check_if_optimize.", "topics": [ "check_if_optimize" ] }, { "page": "create_labelled_data", "title": "Create labelled data", "topics": [ "create_labelled_data" ] }, { "page": "create_transposed_data", "title": "Create Transposed Data", "topics": [ "create_transposed_data" ] }, { "page": "data_trades", "title": "Table with Trade results samples", "topics": [ "data_trades" ] }, { "page": "decrypt_mykeys", "title": "Function that decrypt encrypted content", "topics": [ "decrypt_mykeys" ] }, { "page": "DFR", "title": "Table with predicted price change", "topics": [ "DFR" ] }, { "page": "dlog", "title": "Create log difference distribution", "topics": [ "dlog" ] }, { "page": "encrypt_api_key", "title": "Encrypt api keys", "topics": [ "encrypt_api_key" ] }, { "page": "EURUSDM15X75", "title": "Table with indicator and price change dataset", "topics": [ "EURUSDM15X75" ] }, { "page": "evaluate_macroeconomic_event", "title": "Function used to evaluate market type situation by reading the file with Macroeconomic Events and writing a trigger to the trading robot", "topics": [ "evaluate_macroeconomic_event" ] }, { "page": "get_profit_factorDF", "title": "Function that returns the profit factors of the systems in a form of a DataFrame", "topics": [ "get_profit_factorDF" ] }, { "page": "import_data", "title": "Import Data file with Trade Logs to R.", "topics": [ "import_data" ] }, { "page": "indicator_dataset", "title": "Table with indicator dataset", "topics": [ "indicator_dataset" ] }, { "page": "macd_100", "title": "Table with indicator only used to train model, 128 col 1646 rows", "topics": [ "macd_100" ] }, { "page": "macd_df", "title": "Table with one column indicator dataset", "topics": [ "macd_df" ] }, { "page": "macd_ML60M", "title": "Table with indicator and market type category used to train model", "topics": [ "macd_ML60M" ] }, { "page": "mt_evaluate", "title": "Function to score data and predict current market type using pre-trained classification model", "topics": [ "mt_evaluate" ] }, { "page": "mt_import_data", "title": "Import Market Type related Data to R from the Sandbox", "topics": [ "mt_import_data" ] }, { "page": "mt_make_model", "title": "Function to train Deep Learning Classification model for Market Type recognition", "topics": [ "mt_make_model" ] }, { "page": "mt_stat_evaluate", "title": "Function to prepare and score data, finally predict current market type using pre-trained classification model", "topics": [ "mt_stat_evaluate" ] }, { "page": "mt_stat_transf", "title": "Perform Statistical transformation and clustering of Market Types on the price data", "topics": [ "mt_stat_transf" ] }, { "page": "opt_aggregate_results", "title": "Function to aggregate trading results from multiple folders and files", "topics": [ "opt_aggregate_results" ] }, { "page": "opt_create_graphs", "title": "Function to create summary graphs of the trading results", "topics": [ "opt_create_graphs" ] }, { "page": "policy_tr_systDF", "title": "Table with Market Types and sample of actual policy for those states", "topics": [ "policy_tr_systDF" ] }, { "page": "price_dataset", "title": "Table with price dataset", "topics": [ "price_dataset" ] }, { "page": "price_dataset_big", "title": "Table with price dataset, 30000 rows", "topics": [ "price_dataset_big" ] }, { "page": "profit_factor_data", "title": "Table with Trade results samples", "topics": [ "profit_factor_data" ] }, { "page": "profit_factorDF", "title": "Table with Trade results samples", "topics": [ "profit_factorDF" ] }, { "page": "result_prev", "title": "Table with one column as result from the model prediction", "topics": [ "result_prev" ] }, { "page": "result_R", "title": "Table with predicted price change", "topics": [ "result_R" ] }, { "page": "result_R1", "title": "Table with aggregated trade results", "topics": [ "result_R1" ] }, { "page": "rl_generate_policy", "title": "Function performs Reinforcement Learning using the past data to generate model policy", "topics": [ "rl_generate_policy" ] }, { "page": "rl_generate_policy_mt", "title": "Function performs RL and generates model policy for each Market Type", "topics": [ "rl_generate_policy_mt" ] }, { "page": "rl_log_progress", "title": "Function to retrieve and help to log Q values during RL progress.", "topics": [ "rl_log_progress" ] }, { "page": "rl_log_progress_mt", "title": "Function to retrieve and help to log Q values during RL progress. This function is dedicated to the situations when Market Types are used as a 'states' for the Environment.", "topics": [ "rl_log_progress_mt" ] }, { "page": "rl_record_policy", "title": "Record Reinforcement Learning Policy.", "topics": [ "rl_record_policy" ] }, { "page": "rl_record_policy_mt", "title": "Record Reinforcement Learning Policy for Market Types", "topics": [ "rl_record_policy_mt" ] }, { "page": "rl_write_control_parameters", "title": "Function to find and write the best control parameters.", "topics": [ "rl_write_control_parameters" ] }, { "page": "rl_write_control_parameters_mt", "title": "Function to find and write the best control parameters.", "topics": [ "rl_write_control_parameters_mt" ] }, { "page": "test_data_pattern", "title": "Table with several columns containing indicator values and Label values", "topics": [ "test_data_pattern" ] }, { "page": "to_m", "title": "Convert time series data to matrix with defined number of columns", "topics": [ "to_m" ] }, { "page": "TradeStatePolicy", "title": "Table with Trade States and sample of actual policy for those states", "topics": [ "TradeStatePolicy" ] }, { "page": "trading_systemDF", "title": "Table with trade data and joined market type info", "topics": [ "trading_systemDF" ] }, { "page": "util_find_file_with_code", "title": "R function to find file with specific code within it's content", "topics": [ "util_find_file_with_code" ] }, { "page": "util_find_pid", "title": "R function to find PID of active applications", "topics": [ "util_find_pid" ] }, { "page": "util_generate_password", "title": "R function to generate random passwords for MT4 platform or other needs", "topics": [ "util_generate_password" ] }, { "page": "util_profit_factor", "title": "Calculate Profit Factor", "topics": [ "util_profit_factor" ] }, { "page": "write_command_via_csv", "title": "Write csv files with indicated commands to the external system", "topics": [ "write_command_via_csv" ] }, { "page": "write_ini_file", "title": "Create initialization files to launch MT4 platform with specific configuration", "topics": [ "write_ini_file" ] }, { "page": "x_test_model", "title": "Table with a dataset to test the Model", "topics": [ "x_test_model" ] }, { "page": "y", "title": "Table with indicators and price change which is used to train model", "topics": [ "y" ] } ], "_readme": "https://github.com/vzhomeexperiments/lazytrade/raw/HEAD/README.md", "_rundeps": [ "askpass", "bit", "bit64", "bitops", "cli", "clipr", "cluster", "cpp11", "crayon", "data.table", "dplyr", "farver", "generics", "ggplot2", "glue", "gtable", "h2o", "hash", "hms", "isoband", "jsonlite", "labeling", "lifecycle", "lubridate", "magrittr", "openssl", "pillar", "pkgconfig", "prettyunits", "progress", "R6", "RColorBrewer", "RCurl", "readr", "ReinforcementLearning", "rlang", "S7", "scales", "stringi", "stringr", "sys", "tibble", "tidyselect", "timechange", "tzdb", "utf8", "vctrs", "viridisLite", "vroom", "withr" ], "_score": 5.560384922972016, 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