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Commit34c0a41

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Merge pull requestx4nth055#61 from MikeNotInTheFlesh/master
Fix order of pred_future and true_future in stock price prediction code
2 parentse59575e +2563a68 commit34c0a41

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3 files changed

+22
-20
lines changed

3 files changed

+22
-20
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‎.gitignore‎

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machine-learning/stock-prediction/.ipynb_checkpoints/stock_prediction-checkpoint.ipynb

‎machine-learning/stock-prediction/stock_prediction.ipynb‎

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

‎machine-learning/stock-prediction/test.py‎

Lines changed: 14 additions & 14 deletions
Original file line numberDiff line numberDiff line change
@@ -21,16 +21,16 @@ def plot_graph(test_df):
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defget_final_df(model,data):
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"""
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This function takes the `model` and `data` dict to
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construct a final dataframe that includes the features along
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This function takes the `model` and `data` dict to
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construct a final dataframe that includes the features along
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with true and predicted prices of the testing dataset
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"""
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# if predicted future price is higher than the current,
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# if predicted future price is higher than the current,
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# then calculate the true future price minus the current price, to get the buy profit
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buy_profit=lambdacurrent,true_future,pred_future:true_future-currentifpred_future>currentelse0
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buy_profit=lambdacurrent,pred_future,true_future:true_future-currentifpred_future>currentelse0
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# if the predicted future price is lower than the current price,
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# then subtract the true future price from the current price
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sell_profit=lambdacurrent,true_future,pred_future:current-true_futureifpred_future<currentelse0
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sell_profit=lambdacurrent,pred_future,true_future:current-true_futureifpred_future<currentelse0
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X_test=data["X_test"]
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y_test=data["y_test"]
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# perform prediction and get prices
@@ -47,16 +47,16 @@ def get_final_df(model, data):
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test_df.sort_index(inplace=True)
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final_df=test_df
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# add the buy profit column
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final_df["buy_profit"]=list(map(buy_profit,
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final_df["adjclose"],
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final_df[f"adjclose_{LOOKUP_STEP}"],
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final_df["buy_profit"]=list(map(buy_profit,
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final_df["adjclose"],
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final_df[f"adjclose_{LOOKUP_STEP}"],
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final_df[f"true_adjclose_{LOOKUP_STEP}"])
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# since we don't have profit for last sequence, add 0's
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)
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# add the sell profit column
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final_df["sell_profit"]=list(map(sell_profit,
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final_df["adjclose"],
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final_df[f"adjclose_{LOOKUP_STEP}"],
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final_df["sell_profit"]=list(map(sell_profit,
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final_df["adjclose"],
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final_df[f"adjclose_{LOOKUP_STEP}"],
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final_df[f"true_adjclose_{LOOKUP_STEP}"])
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# since we don't have profit for last sequence, add 0's
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)
@@ -79,8 +79,8 @@ def predict(model, data):
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# load the data
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data=load_data(ticker,N_STEPS,scale=SCALE,split_by_date=SPLIT_BY_DATE,
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shuffle=SHUFFLE,lookup_step=LOOKUP_STEP,test_size=TEST_SIZE,
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data=load_data(ticker,N_STEPS,scale=SCALE,split_by_date=SPLIT_BY_DATE,
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shuffle=SHUFFLE,lookup_step=LOOKUP_STEP,test_size=TEST_SIZE,
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feature_columns=FEATURE_COLUMNS)
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# construct the model
@@ -129,4 +129,4 @@ def predict(model, data):
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ifnotos.path.isdir(csv_results_folder):
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os.mkdir(csv_results_folder)
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csv_filename=os.path.join(csv_results_folder,model_name+".csv")
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final_df.to_csv(csv_filename)
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final_df.to_csv(csv_filename)

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