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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "id": "excellent-valentine", | |
| "metadata": {}, | |
| "source": [ | |
| "# Add lags function" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "novel-function", | |
| "metadata": {}, | |
| "source": [ | |
| "***\n", | |
| "_num_lags_ - number of lags to perform on each feature in the data-set \n", | |
| "\n", | |
| "**Input**: num_lags \n", | |
| "**Output**: original data-set extended with lagged features, list of lagged columns<br>\n", | |
| "***\n", | |
| "1. **Function add_lags**(num_lags)\n", | |
| "2.  $result\\gets$ copy training data set\n", | |
| "3.  $laggedCols\\gets []$\n", | |
| "4.  $colsToLag\\gets STORE$ columns from result in list except columns 'price' and 'returns' \n", | |
| "5.  **for** column **IN** colsToLag **do**\n", | |
| "6.   **for** lag **IN** **range**(1, num_lags+1) **do**\n", | |
| "7.    laggedCol = {column}Lag{lag}\n", | |
| "8.    result[laggedCol] = result[column].shift(lag)\n", | |
| "9.    laggedCols.append(laggedCol)\n", | |
| "10.   **end for**\n", | |
| "11.  **end for**\n", | |
| "12.  **return** $result, laggedCols$" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "annoying-newark", | |
| "metadata": {}, | |
| "source": [ | |
| "# Reward function and sub-functions" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "structured-attendance", | |
| "metadata": {}, | |
| "source": [ | |
| "***\n", | |
| "_action_ - list of actions available to agent(hold, buy, sell) **OR** agent's chosen action \n", | |
| "_states_ - numpy arrays of all states \n", | |
| "_data_ - training data set \n", | |
| "_marketDir_ - direction of the market(either market going **up** or **down**) at current time step as opposed to previous time step \n", | |
| "_mutual position column_ - mutual position of the SMAC+BB or MACD+BB strategy at the current time step\n", | |
| "\n", | |
| "**Input**: action \n", | |
| "**Output**: nextState, reward, done, rewardedAction<br>\n", | |
| "***\n", | |
| "1. **Function get_reward**(action)\n", | |
| "2.  index += 1\n", | |
| "3.  **if** $index > length(data set)-1$ **then**\n", | |
| "4.    $index\\gets 0$\n", | |
| "5.  $nextState\\gets$ next state from current index\n", | |
| "7.  $currentDir\\gets$ current market direction based on index\n", | |
| "8.  $currentStratPos\\gets$ current strategy position based on index\n", | |
| "9.  $nextMutualPositions\\gets getNextMutualPositions()$\n", | |
| "10.  $reward\\gets 0$\n", | |
| "11.  $shortlistedActions\\gets []$\n", | |
| "12.  **if** action is list **then** **# agent is exploring the environment with each available action**\n", | |
| "13.   **for** a **in** range(length(action)) **do**\n", | |
| "14.    $currentAction\\gets action[a]$\n", | |
| "15.    **if** currentAction == sell **then** \n", | |
| "16.     **if** currentDir == down **OR** currentStratPos == sell **OR** nextMutualPositions == sell **then**\n", | |
| "17.       $reward\\gets 1$ \n", | |
| "18.       $shortlistedActions.append(sell)$ \n", | |
| "19.      **end if**\n", | |
| "20.     **end if**\n", | |
| "20.    **if** currentAction == buy **then**\n", | |
| "21.     **if** currentDir == up **OR** currentStratPos == buy **OR** nextMutualPositions == buy **then**\n", | |
| "22.       $reward\\gets 1$\n", | |
| "23.       $shortlistedActions.append(buy)$\n", | |
| "24.      **end if**\n", | |
| "25.     **end if**\n", | |
| "25.    **if** currentAction == hold **then**\n", | |
| "25.     **if** currentStratPos == hold **OR** nextMutualPositions == hold **then**\n", | |
| "26.       $reward\\gets 1$\n", | |
| "27.       $shortlistedActions.append(hold)$\n", | |
| "28.      **end if**\n", | |
| "29.     **end if**\n", | |
| "30.    **end for**\n", | |
| "31.   **end if**\n", | |
| "31.  **else if** action is a number **then** **# agent is exploiting the environment with one chosen action**\n", | |
| "32.   **if** currentAction == sell **then**\n", | |
| "33.    **if** currentDir == down **OR** currentStratPos == sell **OR** nextMutualPositions == sell **then**\n", | |
| "34.      $reward\\gets 1$ \n", | |
| "35.      $shortlistedActions.append(sell)$\n", | |
| "19.     **end if**\n", | |
| "20.    **end if**\n", | |
| "36.   **if** currentAction == buy **then**\n", | |
| "21.    **if** currentDir == up **OR** currentStratPos == buy **OR** nextMutualPositions == buy **then**\n", | |
| "22.      $reward\\gets 1$\n", | |
| "23.      $shortlistedActions.append(buy)$\n", | |
| "19.     **end if**\n", | |
| "20.    **end if**\n", | |
| "25.   **if** currentAction == hold **then**\n", | |
| "25.    **if** currentStratPos == hold **OR** nextMutualPositions == hold **then**\n", | |
| "26.      $reward\\gets 1$\n", | |
| "27.      $shortlistedActions.append(hold)$\n", | |
| "19.     **end if**\n", | |
| "20.    **end if**\n", | |
| "20.   **end else if**\n", | |
| "54. $rewardedAction\\gets chooseAction(shortlistedActions)$\n", | |
| "55. $done\\gets$ True **if** index reaches end of data set (environment) **else** False\n", | |
| "56. **return** nextState, reward, done, rewardedAction" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "younger-sweet", | |
| "metadata": {}, | |
| "source": [ | |
| "***\n", | |
| "_nextMutualPositions_ - list of next 5 mutual positions from the current index \n", | |
| "\n", | |
| "\n", | |
| "**Output**: action<br>\n", | |
| "***\n", | |
| "1. **Function getNextMutualPositions**()\n", | |
| "2.  $nextMutualPositions\\gets$ values of next 5 mutual positions from the current index\n", | |
| "3.  $mostOccured\\gets$ most frequent position from the nextMutualPositions list\n", | |
| "4.   **if** mostOccured has only one position **then**\n", | |
| "5.    **return** that position\n", | |
| "6.   **else** **# equal number of most frequent positions (i.e. any two positions have the same number of occurences)**\n", | |
| "7.    **return** 0\n", | |
| "8.   **end if**" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "pretty-character", | |
| "metadata": {}, | |
| "source": [ | |
| "***\n", | |
| "_actions_ - list of actions passed \n", | |
| "\n", | |
| "**Input**: actions \n", | |
| "**Output**: next_state, next_price, reward, done, rewarded_action<br>\n", | |
| "***\n", | |
| "1. **Function chooseAction**(actions)\n", | |
| "2.  **if** actions list is empty **then**\n", | |
| "3.    **return** 0\n", | |
| "4.  **else if** only 1 action in actions list **then**\n", | |
| "5.    **return** that action\n", | |
| "6.  **else if** 2 actions in action list **then**\n", | |
| "7.    **return** numpy.sign(first + second action of actions list)\n", | |
| "8.  **else if** 3 actions in action list **then**\n", | |
| "9.    **return** 0\n", | |
| "10.  **end if**" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "timely-picnic", | |
| "metadata": {}, | |
| "source": [ | |
| "# Sliding window algorithm" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "everyday-inspiration", | |
| "metadata": {}, | |
| "source": [ | |
| "***\n", | |
| "_data_ - data set containing all columns and values\n", | |
| "_iterationNum_ - lopp-incremented number\n", | |
| "_windowSizePlusN_ - number of window size plus number n\n", | |
| "\n", | |
| "**Input**: data, iterationNum, windowSizePlusN \n", | |
| "**Output**: numpy array of arrays of all states from data set<br>\n", | |
| "***\n", | |
| "1. **Function getWindow**(data, iterationNum, windowSizePlusN)\n", | |
| "2.  $index\\gets$ iterationNum - windowSizePlusN + 1\n", | |
| "3.  $slidingWindowArr\\gets$ [] **# length of array will be number of columns in data set * window size**\n", | |
| "4.  **for** columnName, columnValues **in** data set **do**\n", | |
| "5.   $block\\gets$ arrays of size 6 filled with $columnValues$ of each $columnName$ according to **sliding window algorithm** where the first array of each $columnName$ is filled with the first value of that column\n", | |
| "6.   **for** i **in** range($windowSizePlusN$ - 1) **do**\n", | |
| "7.    $slidingWindowArr$.append(next newest element **minus** previous newest element in $block$ array based on index for all arrays of column values of the data set columns)\n", | |
| "8.   **end for**\n", | |
| "9.  **end for**\n", | |
| "10.  **return** numpy.array([$slidingWindowArr$])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "thrown-grove", | |
| "metadata": {}, | |
| "source": [ | |
| "***\n", | |
| "_data_ - data set containing all columns and values \n", | |
| "_windowSize_ - number of window size = 5 in our case \n", | |
| "\n", | |
| "**Input**: data, windowSize \n", | |
| "**Output**: numpy array of arrays of all states from data set<br>\n", | |
| "***\n", | |
| "1. **Function window_diff**(data, windowSize)\n", | |
| "2.  $dataLen\\gets$ length(data)\n", | |
| "3.  $allStates\\gets$ []\n", | |
| "4.  $scaledData\\gets$ sklearn's **StandardScaler** scaled data set\n", | |
| "5.  **for** i **in** range($dataLen$) **do**\n", | |
| "6.   $state\\gets$ $getWindow$(scaledData, i, windowSize + 1)\n", | |
| "7.   $allStates$.append($state$)\n", | |
| "8.  **end for**\n", | |
| "9.  **return** $allStates$" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "presidential-memphis", | |
| "metadata": {}, | |
| "source": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "horizontal-monitoring", | |
| "metadata": {}, | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.8.5" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 5 | |
| } |