diff --git a/DataLoader.py b/DataLoader.py
index 9d9052b..8491358 100644
--- a/DataLoader.py
+++ b/DataLoader.py
@@ -17,7 +17,7 @@
 # symbols = all_active_listings['symbol'].unique()
 
 #for testing
-symbols = ['IBM', 'MSFT', 'FB', 'AAPL', 'QQQ', 'AAP', 'ATHM', 'VIPS']
+symbols = ['IBM', 'MSFT', 'FB']
 
 #returns a generator, so the calls don't happen until 'write_alpha_results' is called
 stock_data = au.get_alpha_stock_data(
@@ -28,17 +28,18 @@
     max_threads = 7
 )
 
-sma_data = au.get_alpha_technical_data(
-    function = 'SMA',
-    symbols = symbols, 
+technical_data = au.get_alpha_technical_data(
+    functions = [
+        'SMA', 'EMA', 'MACD', 'STOCH', 'RSI', 'BBANDS'
+    ],
+    symbols = symbols,
+    api_key = api_key,
     interval = 'daily',
     time_period = 60, 
     series_type = 'close', 
-    api_key = api_key,
     max_threads = 7
 )
 
-
 au.write_alpha_results(
     results = stock_data, 
     symbols = symbols,
@@ -46,8 +47,7 @@
 )
 
 au.write_alpha_results(
-    results = sma_data, 
+    results = technical_data, 
     symbols = symbols,
     dest_path = "technical_data/"
 )
-
diff --git a/alpha_utils.py b/alpha_utils.py
index 0341db4..db8c2a4 100644
--- a/alpha_utils.py
+++ b/alpha_utils.py
@@ -68,15 +68,33 @@ def get_alpha_listings(
     df = alpha_csv_to_dataframe(response)
     return df
 
-def alpha_csv_to_dataframe(response):
-    decoded_content = response.content.decode('utf-8')
-    cr = csv.reader(decoded_content.splitlines(), delimiter=',')
-    df = pd.DataFrame(cr)
-    header, df = df.iloc[0], df[1:]
-    df.columns = header
-    df.reset_index(drop = True, inplace=True)
-    df.columns.name = None
-    return df
+def alpha_csv_to_dataframe(responses):
+    output = []
+    if isinstance(responses, list) != True:
+        responses = [responses]
+    for response in responses:
+        decoded_content = response.content.decode('utf-8')
+        cr = csv.reader(decoded_content.splitlines(), delimiter=',')
+        df = pd.DataFrame(cr)
+        header, df = df.iloc[0], df[1:]
+        df.columns = header
+        df.reset_index(drop = True, inplace=True)
+        df.columns.name = None
+        output.append(df)
+    return output
+
+def request_alpha_data(urls) -> requests.Response:
+    session = requests.Session()
+    retry = Retry(
+        connect = CONNECT_RETRIES, 
+        backoff_factor = BACKOFF_FACTOR
+    )
+    adapter = HTTPAdapter(max_retries=retry)
+    session.mount('https://', adapter)
+    if isinstance(urls, list):
+        return [session.get(url) for url in urls]
+    else:
+        return session.get(urls)
 
 ###############################################################################################
 
@@ -85,17 +103,18 @@ def get_alpha_stock_data(
     base_url: str = ALPHA_BASE_URL, 
     output_size: str = 'compact', max_threads: int = 5
 ) -> Iterator:
-    """Multi-threaded function for getting stock data from Alpha Vantage API
+    """Multi-threaded function for getting stock price data from Alpha Vantage API.
+    This wrapper is for the functions in "Stock Time Series" (see AV API docs)
     Parameters
     -----------
-    base_url: str
-        the alpha vantage URL for the API
     function: str
-        the 'function' or endpoint you want to call. See AV API docs for more info.
+        A function or endpoint you want to call. See AV API docs for more info.
     symbols: Iterable
         An iterable objects of stock ticker symbols (strings)
     api_key: str
         The Alpha Vantage API key
+    base_url: str
+        the alpha vantage URL for the API
     output_size: str
         'compact' or 'full'. See AV API docs for more info
     max_threads: int
@@ -114,29 +133,29 @@ def get_alpha_stock_data(
             '&datatype=', data_type
         )
         urls.append(''.join(map(str, sequence)))
-    urls
 
     executor = ThreadPoolExecutor(max_threads)
     for result in executor.map(request_alpha_data, urls):
         yield alpha_csv_to_dataframe(result)
 
 def get_alpha_technical_data(
-    function: str, symbols: Iterable, api_key: str,
+    functions: Iterable, symbols: Iterable, api_key: str,
     base_url: str = ALPHA_BASE_URL, 
     interval: str = 'daily', time_period: int = 60, series_type: str = 'close', 
     max_threads: int = 5
 ) -> Iterator:
     """Multi-threaded function for getting technical data from Alpha Vantage API
+    This wrapper is for the functions in "Technical Indicators" (see AV API docs).
     Parameters
     -----------
-    base_url: str
-        the alpha vantage URL for the API
-    function: str
-        the 'function' or endpoint you want to call. See AV API docs for more info.
+    functions: Iterable
+        A list of functions or endpoints you want to call. See AV API docs for more info.
     symbols: Iterable
         An iterable objects of stock ticker symbols (strings)
     api_key: str
         The Alpha Vantage API key
+    base_url: str
+        the alpha vantage URL for the API
     interval: str
         1min, 5min, 15min, 30min, 60min, daily, weekly, monthly.   
     time_period: int
@@ -153,37 +172,34 @@ def get_alpha_technical_data(
     data_type = 'csv' #most memory efficient
     urls = []
     for symbol in symbols:
-        sequence = (
-            base_url, 'function=', function, '&symbol=', symbol, 
-            '&interval=', interval, '&time_period=', time_period, 
-            '&series_type=', series_type, '&apikey=', api_key, '&datatype=', data_type
-        )
-        urls.append(''.join(map(str, sequence)))
-    urls
+        url_list = []
+        for function in functions:
+            if function == 'VWAP' and interval not in ['daily', 'weekly', 'monthly']:
+                raise ValueError(
+                    'VWAP is only available for intraday time series intervals'
+                )
+            sequence = (
+                base_url, 'function=', function, '&symbol=', symbol, 
+                '&interval=', interval, '&time_period=', time_period, 
+                '&series_type=', series_type, '&apikey=', api_key, '&datatype=', data_type
+            )
+            url_list.append(''.join(map(str, sequence)))
+        urls.append(url_list)
 
     executor = ThreadPoolExecutor(max_threads)
-    for result in executor.map(request_alpha_data, urls):
-        yield alpha_csv_to_dataframe(result)
-
-
-def request_alpha_data(url):
-    session = requests.Session()
-    retry = Retry(
-        connect = CONNECT_RETRIES, 
-        backoff_factor = BACKOFF_FACTOR
-    )
-    adapter = HTTPAdapter(max_retries=retry)
-    session.mount('https://', adapter)
-    return session.get(url)
+    for result_list in executor.map(request_alpha_data, (url_list for url_list in urls)):
+        yield alpha_csv_to_dataframe(result_list)
 
 def write_alpha_results(results: Iterator, symbols: Iterable, dest_path: str) -> None:
     """Writes elements in an Iterator - with the stock ticker as an added column - to a folder as a csv
     Parameters
     -----------
     results: Iterator
-        This should be the raw JSON output from the API as an Iterator object, one element for each symbol
+        This should be the raw output from the API as an Iterator object.
+        Each element should correspond to a stock symbol in 'symbols' and be a list of DataFrames, 
+        where each DataFrame in that sub-list corresponds to a function in functions.
     symbols: Iterable
-        An iterable objects of stock ticker symbols (strings)
+        An iterable object of stock ticker symbols (strings)
     Returns
     --------
     pd.DataFrame
@@ -192,17 +208,26 @@ def write_alpha_results(results: Iterator, symbols: Iterable, dest_path: str) ->
     """
     os.makedirs(dest_path, exist_ok = True)
     for i, result in enumerate(results):
-        temp_df = None
-        if isinstance(result, pd.DataFrame):
-            temp_df = result
+        symbol_df = None
+        if isinstance(result, list) != True:
+            result = [result]
+        for j, df in enumerate(result):
+            if isinstance(df, pd.DataFrame) != True:
+                raise Exception("stock_data must be an Iterator of pandas DataFrames")
+            temp_df = df
             temp_df['symbol'] = symbols[i]
             temp_df = reorder_last_to_first(temp_df)
             temp_df = clean_alpha_cols(temp_df)
-            temp_df.to_csv(os.path.join(dest_path, symbols[i] + '.csv'), index=False)
-        else:
-            raise Exception("stock_data must be an Iterator of pandas DataFrames")
+            if symbol_df is None:
+                symbol_df = temp_df
+            else:
+                symbol_df = pd.merge(symbol_df, temp_df, on = ['symbol', 'timestamp'])
+
+        symbol_df.to_csv(os.path.join(dest_path, symbols[i] + '.csv'), index=False)
     return None
 
+##########################################################################################
+
 def clean_alpha_cols(df: pd.DataFrame) -> pd.DataFrame:
     """Cleans up column names coming out of the Alpha Vantage API
     Parameters
@@ -217,6 +242,7 @@ def clean_alpha_cols(df: pd.DataFrame) -> pd.DataFrame:
     new_cols = [re.sub("[0-9]\\.\s", "", col) for col in new_cols]
     new_cols = [re.sub("\s", "_", col) for col in new_cols]
     df.columns = new_cols
+    df.columns = ['symbol', 'timestamp'] + df.columns.tolist()[2:]
     return df
 
 def reorder_last_to_first(df: pd.DataFrame) -> pd.DataFrame:
diff --git a/technical_data/.gitempty b/technical_data/.gitempty
new file mode 100644
index 0000000..f4e505f
--- /dev/null
+++ b/technical_data/.gitempty
@@ -0,0 +1 @@
+#placeholder for stock data
\ No newline at end of file