moved strat stuff into one file so to not deal with imports

README.md

@@ -126,8 +126,8 @@ You should of course use your own strategy, but as an example.
 
 ```python
 import cira
-from cira.strategy.strategy import Randomness
-from cira.strategy.backtest import back_test
+from cira.strategy import Randomness
+from cira.strategy import back_test
 from datetime import datetime
 import pandas as pd
 

cira/strategy/backtest.py → cira/strategy.py

@@ -1,7 +1,95 @@
+from typing import List
+import pickle
 import pandas as pd
 import numpy as np
-from typing import List
-from .strategy import Strategy, ByAndHold
+import random
+import schedule
+import time
+
+
+class Strategy:
+    def __init__(self, name) -> None:
+        self.name = name
+
+    def iterate(
+        self,
+        feature_data: pd.DataFrame,
+        prices: pd.DataFrame,
+        portfolio: np.ndarray,
+        cash=float,
+    ) -> np.ndarray:
+        """
+        Takes in feature data, then returns allocation prediction.
+        """
+        raise NotImplementedError
+
+    def save(self, file_path):
+        """
+        Save strategy to pickle file
+        usage:
+            strategy.fit(train_data)
+            strategy.save('./model.pkl')
+        """
+        with open(file_path, "wb") as file:
+            pickle.dump(self, file)
+
+    @classmethod
+    def load(cls, file_path):
+        """
+        Load in strategy from pickle file
+        usage:
+            strategy = Strategy.load('./model.pkl')
+            predictions = strategy.predict(test_data)
+        """
+        with open(file_path, "rb") as file:
+            return pickle.load(file)
+
+
+class Randomness(Strategy):
+    def __init__(self, lower: int = -1, upper: int = 1, seed=0) -> None:
+        super().__init__(name="Randomness")
+        random.seed(seed)
+        self.a = lower
+        self.b = upper
+        self.allocation = []
+
+    def iterate(
+        self,
+        feature_data: pd.DataFrame,
+        prices: pd.DataFrame,
+        portfolio: np.ndarray,
+        cash=float,
+    ) -> np.ndarray:
+        al = np.array(
+            [random.randint(self.a, self.b) for _ in range(len(prices.keys()))]
+        )
+        self.allocation.append(al)
+        return al
+
+
+class ByAndHold(Strategy):
+    def __init__(self) -> None:
+        super().__init__(name="ByAndHold")
+        self.is_first = True
+        self.allocation = []
+
+    def iterate(
+        self,
+        feature_data: pd.DataFrame,
+        prices: pd.DataFrame,
+        portfolio: np.ndarray,
+        cash=float,
+    ) -> np.ndarray:
+        if self.is_first:
+            self.is_first = False
+            amount = cash / len(prices.keys())
+            amount *= 0.96
+            al = (amount // prices.values).astype(np.int64)[0]
+            self.allocation.append(al)
+            return al
+        al = np.array([0] * len(prices.keys()))
+        self.allocation.append(al)
+        return al
 
 
 FEE_RATE = 0.004 # this is what alpaca takes 
@@ -104,3 +192,35 @@ def back_test_against_buy_and_hold(
         capital=capital,
         use_fees=use_fees,
     )
+
+
+class Scheduler:
+    def __init__(self) -> None:
+        pass
+
+    def add_daily_job(self, func_name) -> None:
+        schedule.every(1).days.do(func_name)
+
+    def add_daily_job_at(self, func_name, time_HM: str = "12:00") -> None:
+        schedule.every().day.at(time_HM).do(func_name)
+
+    def add_hour_job(self, func_name) -> None:
+        schedule.every(1).hour.do(func_name)
+
+    def add_minute_job(self, func_name) -> None:
+        schedule.every(1).minute.do(func_name)
+
+    def add_daily_job_at_time_EDT(self, func_name, time_HM: str = "12:00") -> None:
+        schedule.every().day.at(time_HM, "America/New_York").do(func_name)
+
+    def get_all_jobs(self):
+        return schedule.jobs
+
+    def clear_all_jobs(self) -> None:
+        schedule.clear()
+
+    def run(self):
+        """runs the scheduler for ever"""
+        while True:
+            schedule.run_pending()
+            time.sleep(1)

examples/linear.ipynb

@@ -271,7 +271,7 @@
     "from typing import List\n",
     "from pandas import DataFrame\n",
     "\n",
-    "from cira.strategy.strategy import Strategy\n",
+    "from cira.strategy import Strategy\n",
     "\n",
     "\n",
     "class MyStrat(Strategy):\n",
@@ -330,7 +330,7 @@
     }
    ],
    "source": [
-    "from cira.strategy.backtest import back_test_against_buy_and_hold as back_test\n",
+    "from cira.strategy import back_test_against_buy_and_hold as back_test\n",
     "\n",
     "strat = MyStrat(model, risk=0.05)\n",
     "prices_df = my_dataframe = pd.DataFrame(test_data[target], columns=['close'])\n",
@@ -455,16 +455,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2024-03-04 20:40:28.480870 new position size: [-93] of symbol MSFT\n",
-      "ratio:1.0076733\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "trade()"
    ]
@@ -473,31 +464,10 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[Every 1 day at 20:36:00 do trade() (last run: [never], next run: 2024-03-04 20:36:00)]\n",
-      "[-93]\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[58], line 8\u001b[0m\n\u001b[1;32m      5\u001b[0m scheduler\u001b[38;5;241m.\u001b[39madd_daily_job_at(trade, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m20:36\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28mprint\u001b[39m(scheduler\u001b[38;5;241m.\u001b[39mget_all_jobs())\n\u001b[0;32m----> 8\u001b[0m \u001b[43mscheduler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Programs/repositories/cira/cira/strategy/scheduler.py:34\u001b[0m, in \u001b[0;36mScheduler.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m     33\u001b[0m     schedule\u001b[38;5;241m.\u001b[39mrun_pending()\n\u001b[0;32m---> 34\u001b[0m     \u001b[43mtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "\n",
-    "from cira.strategy.scheduler import Scheduler\n",
+    "from cira.strategy import Scheduler\n",
     "\n",
     "scheduler = Scheduler()\n",
     "scheduler.clear_all_jobs()\n",