Minor updates 3

README.md

@@ -29,15 +29,15 @@ This mini QuantTrading project will have the following features:
 
 ### TradeStrategy x 2
 
-#### Strategy 1: Trend Following.
+#### Strategy 1: Trend Following
 
 When price rises above an absolute threshold `p_change_threshold` percent, default to 2%. 
 
 Buy and hold for `hold_stock_threshold` days, default to 20.
 
 [Source File](./alpha/strategies/strategy_1.ipynb) & [Demo Usage](./alpha/strategies/strategy_1_usage.ipynb).
 
-#### Strategy 2: Reversion.
+#### Strategy 2: Mean Reversion
 
 When price falls consecutively for two days, and cumulatively more than `p_change_threshold` percent in total, default to 10%.
 

alpha/strategies/strategy_2.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "code",
       "execution_count": 22,
       "outputs": [],
-      "source": "# %%file strategy_2.py\n\nimport import_ipynb\nfrom strategy_base import TradeStrategyBase\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nclass TradeStrategy2(TradeStrategyBase):\n    \"\"\"\n    Trade Strategy 2: Reversion Strategy. \n    When price falls two days in a row, and cumulatively more than x percent (_p_change_threshold) in total, default to 10%.\n    Buy and hold for a number of days (_hold_stock_threshold) days, default to 20. \n    \"\"\"\n    # Absolute threshold for buy in\n    _p_change_threshold \u003d 10\n    # Number of days to hold after Buy\n    _hold_stock_threshold \u003d 10\n    \n    def __init__(self, p_change_threshold:float \u003d 10.0, hold_stock_threshold:int \u003d 10, should_log:bool \u003d False, should_plot:bool \u003d False, axs \u003d None):\n        \"\"\"\n        Initialize Strategy with given parameters\n        :type p_change_threshold: float\n        :type hold_stock_threshold: int \n        :type should_log: bool\n        :type should_plot: bool\n        :param p_change_threshold: Absolute threshold for a stock to rise in two days. E.g. 7.5% \u003d 7.5 \n        :param hold_stock_threshold: Number of days to hold a stock. E.g. 20 days \u003d 20\n        :param should_log Whether or not log the strategy\n        :param should_plot Whether or not plot the strategy\n        \"\"\"\n        self._p_change_threshold \u003d p_change_threshold\n        self._hold_stock_threshold \u003d hold_stock_threshold\n        self._should_log \u003d should_log\n        self._should_plot \u003d should_plot\n        self._axs \u003d axs\n        \n        self._held_tradedays \u003d pd.DataFrame()\n        self._previous_tradeday \u003d None\n        self._held_period_days \u003d 0\n        \n    def trade(self, date:pd.Timestamp, tradeday: pd.Series):\n        \"\"\"\n        Perform Analysis, Decide on Trade, and Execute Trade\n        :type date: pd.Timestamp\n        :type tradeday: pd.Series\n        :param date: The pandas DataFrame date\n        :param tradeday: The pandas DateFrame Series representing a trade day\n        :return: \n        \"\"\"\n            \n        \"\"\"\n        Firstly, Increment holding date count\n        \"\"\"\n        if self._held_period_days !\u003d 0:\n            # Increment date\n            self._held_period_days +\u003d 1\n            # Is currently holding, attach new tradeday in DataFrame\n            self._held_tradedays \u003d self._held_tradedays.append(tradeday)\n        \n        \"\"\"\n        Secondly, Buying\n        \"\"\"\n        if self._evaluate_buying(date\u003ddate,\n                                 tradeday\u003dtradeday):\n            self._execute_buying(date\u003ddate,\n                                 tradeday\u003dtradeday)\n        \n        \"\"\"\n        Lastly, Selling\n        \"\"\"\n        if self._evaluate_selling(date\u003ddate,\n                                  tradeday\u003dtradeday):\n            self._execute_selling(date\u003ddate,\n                                  tradeday\u003dtradeday)\n        \n    def _evaluate_buying(self, date:pd.Timestamp, tradeday: pd.Series):\n        \"\"\"\n        Evaluate whether or not BUY should be executed\n        :type date: pd.Timestamp\n        :type tradeday: pd.Series\n        :param date: The pandas DataFrame date\n        :param tradeday: The pandas DateFrame Series representing a trade day\n        \"\"\"\n        trade_day: pd.Series \u003d tradeday.copy() # Create a copy() in case we need to change it.\n        if self._held_period_days \u003d\u003d 0 and self._previous_tradeday is not None:\n            # trade_day.change \u003c 0 : Tells if price dropped\n            is_today_price_down \u003d trade_day.p_change \u003c 0\n            # Check if yesterday was down\n            is_yesterday_price_down \u003d self._previous_tradeday.p_change  \u003c 0\n            \n            # Sum of all drops\n            total_down_rate \u003d trade_day.p_change + self._previous_tradeday.p_change\n        \n            # Set previous_tradeday to today\n            self._previous_tradeday \u003d tradeday\n            \n            if is_today_price_down and \\\n                is_yesterday_price_down and \\\n                    np.abs(total_down_rate) \u003e self._p_change_threshold:\n                return True\n            else:\n                return False\n        else:\n            # Set previous_tradeday to today\n            self._previous_tradeday \u003d tradeday\n            return False\n        \n    def _execute_buying(self, date:pd.Timestamp, tradeday: pd.Series):\n        \"\"\"\n        How BUY should be executed\n        :type date: pd.Timestamp\n        :type tradeday: pd.Series\n        :param date: The pandas DataFrame date\n        :param tradeday: The pandas DateFrame Series representing a trade day\n        \"\"\"\n        if self._should_log:\n            print(\"Bought on {} at ${}.\".format(date, tradeday.close))\n        self._held_period_days +\u003d 1 # Start holding date count\n        self._held_tradedays \u003d pd.DataFrame(tradeday).T # Transpose of Series\n        self._last_buy_date \u003d date\n\n    def _evaluate_selling(self, date:pd.Timestamp, tradeday: pd.Series):\n        \"\"\"\n        Evaluate whether or not SELL should be executed\n        :type date: pd.Timestamp\n        :type tradeday: pd.Series\n        :param date: The pandas DataFrame date\n        :param tradeday: The pandas DateFrame Series representing a trade day\n        \"\"\"\n        if self._held_period_days \u003e\u003d self._hold_stock_threshold:\n            # When holding period exceeds hold_stock_threshold, sell stocks\n            return True\n        else:\n            return False\n            \n    def _execute_selling(self, date:pd.Timestamp, tradeday: pd.Series):\n        \"\"\"\n        How SELL should be executed\n        :type date: pd.Timestamp\n        :type tradeday: pd.Series\n        :param date: The pandas DataFrame date\n        :param tradeday: The pandas DateFrame Series representing a trade day\n        \"\"\" \n        if self._should_log:\n            print(\"Sold on {} at ${}.\".format(date, tradeday.close))\n        if self._should_plot:\n            self._plot_trade(axs\u003dself._axs,\n                             buy_date\u003dself._last_buy_date, \n                             sell_date\u003ddate)\n        self._previous_tradeday \u003d None # Reset last tradeday\n        self._held_period_days \u003d 0  # Reset holding count \n        self._last_buy_date \u003d None # Reset last buy date\n\n    def _plot_trade(self, axs, buy_date, sell_date):\n\n        print(\"Todo: Plotting {} {}\".format(buy_date, sell_date))\n\n        # buy_tradeday \u003d self._held_tradedays[buy_date.date()]\n        # sell_tradeday \u003d self._held_tradedays[sell_date.date()]\n        # print(\"Bought at {}, Sold at {}\".format(buy_tradeday.close, sell_tradeday.close))\n\n        # drawer \u003d plt if axs is None else axs\n        # \n        # # Mark red for up and green for down, slicing using start and end\n        # if sell_tradeday.close \u003c buy_tradeday.close:\n        #   drawer.fill_between(self._held_tradedays.index[buy_date:sell_date], 0,\n        #                   self._held_tradedays[\u0027close\u0027][buy_date:sell_date], color\u003d\u0027red\u0027,\n        #                   alpha\u003d.38)\n        #   is_win \u003d False\n        # else:\n        #   drawer.fill_between(amex_df.index[buy_date:sell_date], 0,\n        #                   amex_df[\u0027close\u0027][buy_date:sell_date], color\u003d\u0027green\u0027,\n        #                   alpha\u003d.38)\n        #   is_win \u003d True\n        # \n        # return is_win\n        \n    @property\n    def trade_profit(self):\n        if \u0027p_change\u0027 in self._held_tradedays.columns:\n            profit_array \u003d self._held_tradedays.p_change/100 + 1\n            profit_rate \u003d np.product(profit_array)-1\n            return profit_rate\n        else:\n            return 0\n    \n    @property\n    def p_change_threshold(self):\n        return self._p_change_threshold\n    \n    @property\n    def keep_stock_threshold(self):\n        return self._hold_stock_threshold\n        ",
+      "source": "# %%file strategy_2.py\n\nimport import_ipynb\nfrom strategy_base import TradeStrategyBase\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nclass TradeStrategy2(TradeStrategyBase):\n    \"\"\"\n    Trade Strategy 2: Mean Reversion. \n    When price falls two days in a row, and cumulatively more than x percent (_p_change_threshold) in total, default to 10%.\n    Buy and hold for a number of days (_hold_stock_threshold) days, default to 20. \n    \"\"\"\n    # Absolute threshold for buy in\n    _p_change_threshold \u003d 10\n    # Number of days to hold after Buy\n    _hold_stock_threshold \u003d 10\n    \n    def __init__(self, p_change_threshold:float \u003d 10.0, hold_stock_threshold:int \u003d 10, should_log:bool \u003d False, should_plot:bool \u003d False, axs \u003d None):\n        \"\"\"\n        Initialize Strategy with given parameters\n        :type p_change_threshold: float\n        :type hold_stock_threshold: int \n        :type should_log: bool\n        :type should_plot: bool\n        :param p_change_threshold: Absolute threshold for a stock to rise in two days. E.g. 7.5% \u003d 7.5 \n        :param hold_stock_threshold: Number of days to hold a stock. E.g. 20 days \u003d 20\n        :param should_log Whether or not log the strategy\n        :param should_plot Whether or not plot the strategy\n        \"\"\"\n        self._p_change_threshold \u003d p_change_threshold\n        self._hold_stock_threshold \u003d hold_stock_threshold\n        self._should_log \u003d should_log\n        self._should_plot \u003d should_plot\n        self._axs \u003d axs\n        \n        self._held_tradedays \u003d pd.DataFrame()\n        self._previous_tradeday \u003d None\n        self._held_period_days \u003d 0\n        \n    def trade(self, date:pd.Timestamp, tradeday: pd.Series):\n        \"\"\"\n        Perform Analysis, Decide on Trade, and Execute Trade\n        :type date: pd.Timestamp\n        :type tradeday: pd.Series\n        :param date: The pandas DataFrame date\n        :param tradeday: The pandas DateFrame Series representing a trade day\n        :return: \n        \"\"\"\n            \n        \"\"\"\n        Firstly, Increment holding date count\n        \"\"\"\n        if self._held_period_days !\u003d 0:\n            # Increment date\n            self._held_period_days +\u003d 1\n            # Is currently holding, attach new tradeday in DataFrame\n            self._held_tradedays \u003d self._held_tradedays.append(tradeday)\n        \n        \"\"\"\n        Secondly, Buying\n        \"\"\"\n        if self._evaluate_buying(date\u003ddate,\n                                 tradeday\u003dtradeday):\n            self._execute_buying(date\u003ddate,\n                                 tradeday\u003dtradeday)\n        \n        \"\"\"\n        Lastly, Selling\n        \"\"\"\n        if self._evaluate_selling(date\u003ddate,\n                                  tradeday\u003dtradeday):\n            self._execute_selling(date\u003ddate,\n                                  tradeday\u003dtradeday)\n        \n    def _evaluate_buying(self, date:pd.Timestamp, tradeday: pd.Series):\n        \"\"\"\n        Evaluate whether or not BUY should be executed\n        :type date: pd.Timestamp\n        :type tradeday: pd.Series\n        :param date: The pandas DataFrame date\n        :param tradeday: The pandas DateFrame Series representing a trade day\n        \"\"\"\n        trade_day: pd.Series \u003d tradeday.copy() # Create a copy() in case we need to change it.\n        if self._held_period_days \u003d\u003d 0 and self._previous_tradeday is not None:\n            # trade_day.change \u003c 0 : Tells if price dropped\n            is_today_price_down \u003d trade_day.p_change \u003c 0\n            # Check if yesterday was down\n            is_yesterday_price_down \u003d self._previous_tradeday.p_change  \u003c 0\n            \n            # Sum of all drops\n            total_down_rate \u003d trade_day.p_change + self._previous_tradeday.p_change\n        \n            # Set previous_tradeday to today\n            self._previous_tradeday \u003d tradeday\n            \n            if is_today_price_down and \\\n                is_yesterday_price_down and \\\n                    np.abs(total_down_rate) \u003e self._p_change_threshold:\n                return True\n            else:\n                return False\n        else:\n            # Set previous_tradeday to today\n            self._previous_tradeday \u003d tradeday\n            return False\n        \n    def _execute_buying(self, date:pd.Timestamp, tradeday: pd.Series):\n        \"\"\"\n        How BUY should be executed\n        :type date: pd.Timestamp\n        :type tradeday: pd.Series\n        :param date: The pandas DataFrame date\n        :param tradeday: The pandas DateFrame Series representing a trade day\n        \"\"\"\n        if self._should_log:\n            print(\"Bought on {} at ${}.\".format(date, tradeday.close))\n        self._held_period_days +\u003d 1 # Start holding date count\n        self._held_tradedays \u003d pd.DataFrame(tradeday).T # Transpose of Series\n        self._last_buy_date \u003d date\n\n    def _evaluate_selling(self, date:pd.Timestamp, tradeday: pd.Series):\n        \"\"\"\n        Evaluate whether or not SELL should be executed\n        :type date: pd.Timestamp\n        :type tradeday: pd.Series\n        :param date: The pandas DataFrame date\n        :param tradeday: The pandas DateFrame Series representing a trade day\n        \"\"\"\n        if self._held_period_days \u003e\u003d self._hold_stock_threshold:\n            # When holding period exceeds hold_stock_threshold, sell stocks\n            return True\n        else:\n            return False\n            \n    def _execute_selling(self, date:pd.Timestamp, tradeday: pd.Series):\n        \"\"\"\n        How SELL should be executed\n        :type date: pd.Timestamp\n        :type tradeday: pd.Series\n        :param date: The pandas DataFrame date\n        :param tradeday: The pandas DateFrame Series representing a trade day\n        \"\"\" \n        if self._should_log:\n            print(\"Sold on {} at ${}.\".format(date, tradeday.close))\n        if self._should_plot:\n            self._plot_trade(axs\u003dself._axs,\n                             buy_date\u003dself._last_buy_date, \n                             sell_date\u003ddate)\n        self._previous_tradeday \u003d None # Reset last tradeday\n        self._held_period_days \u003d 0  # Reset holding count \n        self._last_buy_date \u003d None # Reset last buy date\n\n    def _plot_trade(self, axs, buy_date, sell_date):\n\n        print(\"Todo: Plotting {} {}\".format(buy_date, sell_date))\n\n        # buy_tradeday \u003d self._held_tradedays[buy_date.date()]\n        # sell_tradeday \u003d self._held_tradedays[sell_date.date()]\n        # print(\"Bought at {}, Sold at {}\".format(buy_tradeday.close, sell_tradeday.close))\n\n        # drawer \u003d plt if axs is None else axs\n        # \n        # # Mark red for up and green for down, slicing using start and end\n        # if sell_tradeday.close \u003c buy_tradeday.close:\n        #   drawer.fill_between(self._held_tradedays.index[buy_date:sell_date], 0,\n        #                   self._held_tradedays[\u0027close\u0027][buy_date:sell_date], color\u003d\u0027red\u0027,\n        #                   alpha\u003d.38)\n        #   is_win \u003d False\n        # else:\n        #   drawer.fill_between(amex_df.index[buy_date:sell_date], 0,\n        #                   amex_df[\u0027close\u0027][buy_date:sell_date], color\u003d\u0027green\u0027,\n        #                   alpha\u003d.38)\n        #   is_win \u003d True\n        # \n        # return is_win\n        \n    @property\n    def trade_profit(self):\n        if \u0027p_change\u0027 in self._held_tradedays.columns:\n            profit_array \u003d self._held_tradedays.p_change/100 + 1\n            profit_rate \u003d np.product(profit_array)-1\n            return profit_rate\n        else:\n            return 0\n    \n    @property\n    def p_change_threshold(self):\n        return self._p_change_threshold\n    \n    @property\n    def keep_stock_threshold(self):\n        return self._hold_stock_threshold\n        ",
       "metadata": {
         "pycharm": {
           "metadata": false,