Fix order of pred_future and true_future

.gitignore

@@ -0,0 +1,2 @@
+
+machine-learning/stock-prediction/.ipynb_checkpoints/stock_prediction-checkpoint.ipynb

machine-learning/stock-prediction/stock_prediction.ipynb

@@ -346,10 +346,10 @@
     "    \"\"\"\n",
     "    # if predicted future price is higher than the current, \n",
     "    # then calculate the true future price minus the current price, to get the buy profit\n",
-    "    buy_profit  = lambda current, true_future, pred_future: true_future - current if pred_future > current else 0\n",
+    "    buy_profit  = lambda current, pred_future, true_future: true_future - current if pred_future > current else 0\n",
     "    # if the predicted future price is lower than the current price,\n",
     "    # then subtract the true future price from the current price\n",
-    "    sell_profit = lambda current, true_future, pred_future: current - true_future if pred_future < current else 0\n",
+    "    sell_profit = lambda current, pred_future, true_future: current - true_future if pred_future < current else 0\n",
     "    X_test = data[\"X_test\"]\n",
     "    y_test = data[\"y_test\"]\n",
     "    # perform prediction and get prices\n",
@@ -528,9 +528,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.6.6 64-bit",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python36664bitea6884f10f474b21a2a2f022451e0d09"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -542,9 +542,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.8.0"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

machine-learning/stock-prediction/test.py

@@ -21,16 +21,16 @@ def plot_graph(test_df):
 
 def get_final_df(model, data):
     """
-    This function takes the `model` and `data` dict to 
-    construct a final dataframe that includes the features along 
+    This function takes the `model` and `data` dict to
+    construct a final dataframe that includes the features along
     with true and predicted prices of the testing dataset
     """
-    # if predicted future price is higher than the current, 
+    # if predicted future price is higher than the current,
     # then calculate the true future price minus the current price, to get the buy profit
-    buy_profit  = lambda current, true_future, pred_future: true_future - current if pred_future > current else 0
+    buy_profit  = lambda current, pred_future, true_future: true_future - current if pred_future > current else 0
     # if the predicted future price is lower than the current price,
     # then subtract the true future price from the current price
-    sell_profit = lambda current, true_future, pred_future: current - true_future if pred_future < current else 0
+    sell_profit = lambda current, pred_future, true_future: current - true_future if pred_future < current else 0
     X_test = data["X_test"]
     y_test = data["y_test"]
     # perform prediction and get prices
@@ -47,16 +47,16 @@ def get_final_df(model, data):
     test_df.sort_index(inplace=True)
     final_df = test_df
     # add the buy profit column
-    final_df["buy_profit"] = list(map(buy_profit, 
-                                    final_df["adjclose"], 
-                                    final_df[f"adjclose_{LOOKUP_STEP}"], 
+    final_df["buy_profit"] = list(map(buy_profit,
+                                    final_df["adjclose"],
+                                    final_df[f"adjclose_{LOOKUP_STEP}"],
                                     final_df[f"true_adjclose_{LOOKUP_STEP}"])
                                     # since we don't have profit for last sequence, add 0's
                                     )
     # add the sell profit column
-    final_df["sell_profit"] = list(map(sell_profit, 
-                                    final_df["adjclose"], 
-                                    final_df[f"adjclose_{LOOKUP_STEP}"], 
+    final_df["sell_profit"] = list(map(sell_profit,
+                                    final_df["adjclose"],
+                                    final_df[f"adjclose_{LOOKUP_STEP}"],
                                     final_df[f"true_adjclose_{LOOKUP_STEP}"])
                                     # since we don't have profit for last sequence, add 0's
                                     )
@@ -79,8 +79,8 @@ def predict(model, data):
 
 
 # load the data
-data = load_data(ticker, N_STEPS, scale=SCALE, split_by_date=SPLIT_BY_DATE, 
-                shuffle=SHUFFLE, lookup_step=LOOKUP_STEP, test_size=TEST_SIZE, 
+data = load_data(ticker, N_STEPS, scale=SCALE, split_by_date=SPLIT_BY_DATE,
+                shuffle=SHUFFLE, lookup_step=LOOKUP_STEP, test_size=TEST_SIZE,
                 feature_columns=FEATURE_COLUMNS)
 
 # construct the model
@@ -129,4 +129,4 @@ def predict(model, data):
 if not os.path.isdir(csv_results_folder):
     os.mkdir(csv_results_folder)
 csv_filename = os.path.join(csv_results_folder, model_name + ".csv")
-final_df.to_csv(csv_filename)
+final_df.to_csv(csv_filename)