refactor: convert for loops into list and dict comprehensions (OpenBB…

…-finance#2493)

custom_pre_commit/check_doc.py

@@ -69,11 +69,7 @@ def main(ignore_files: Optional[str], ignore_commands: Optional[str]):
     with open(main_yaml_filename) as yaml:
         lines = yaml.read()
 
-    undocumented = []
-    for command in clean_commands:
-        if command not in lines:
-            undocumented.append(command)
-
+    undocumented = [command for command in clean_commands if command not in lines]
     if not undocumented:
         sys.exit(0)
     else:

custom_pre_commit/check_reserved_args.py

@@ -46,9 +46,7 @@ def process_file(file_path: str, exp: str):
 
 # pylint:disable=anomalous-backslash-in-string
 def main():
-    expressions = []
-    for sa in RESERVED_ARGS:
-        expressions.append(rf'("-{sa}(.|\n)*?\--.*?\S*)')  # noqa: W605
+    expressions = [f'("-{sa}(.|\n)*?\\--.*?\\S*)' for sa in RESERVED_ARGS]
     exp = f"""({"|".join(expressions)})"""
 
     base = os.path.abspath(os.path.dirname(__file__))

openbb_terminal/common/behavioural_analysis/stocktwits_model.py

@@ -89,11 +89,10 @@ def get_trending() -> pd.DataFrame:
     """
     result = requests.get("https://api.stocktwits.com/api/2/trending/symbols.json")
     if result.status_code == 200:
-        l_symbols = []
-        for symbol in result.json()["symbols"]:
-            l_symbols.append(
-                [symbol["symbol"], symbol["watchlist_count"], symbol["title"]]
-            )
+        l_symbols = [
+            [symbol["symbol"], symbol["watchlist_count"], symbol["title"]]
+            for symbol in result.json()["symbols"]
+        ]
 
         df_trending = pd.DataFrame(
             l_symbols, columns=["Ticker", "Watchlist Count", "Name"]

openbb_terminal/common/quantitative_analysis/qa_model.py

@@ -635,10 +635,7 @@ def get_omega(
     threshold = np.linspace(threshold_start, threshold_end, 50)
     df = pd.DataFrame(threshold, columns=["threshold"])
 
-    omega_list = []
-    for i in threshold:
-        omega_list.append(get_omega_ratio(data, i))
-
+    omega_list = [get_omega_ratio(data, i) for i in threshold]
     df["omega"] = omega_list
 
     return df

openbb_terminal/common/quantitative_analysis/qa_view.py

@@ -1175,7 +1175,6 @@ def display_omega(
     threshold_end: float
         annualized target return threshold end of plotted threshold range
     """
-
     df = qa_model.get_omega(data, threshold_start, threshold_end)
 
     # Plotting

openbb_terminal/cryptocurrency/defi/terramoney_fcd_model.py

@@ -162,23 +162,22 @@ def get_validators(sortby: str = "votingPower", ascend: bool = True) -> pd.DataF
     """
 
     response = _make_request("staking")["validators"]
-    results = []
-    for validator in response:
-        results.append(
-            {
-                "accountAddress": validator["accountAddress"],
-                "validatorName": validator["description"].get("moniker"),
-                "tokensAmount": denominate_number(validator["tokens"]),
-                "votingPower": round(
-                    (float(validator["votingPower"].get("weight")) * 100), 2
-                ),
-                "commissionRate": round(
-                    (float(validator["commissionInfo"].get("rate", 0)) * 100), 2
-                ),
-                "status": validator["status"],
-                "uptime": round((float(validator.get("upTime", 0)) * 100), 2),
-            }
-        )
+    results = [
+        {
+            "accountAddress": validator["accountAddress"],
+            "validatorName": validator["description"].get("moniker"),
+            "tokensAmount": denominate_number(validator["tokens"]),
+            "votingPower": round(
+                (float(validator["votingPower"].get("weight")) * 100), 2
+            ),
+            "commissionRate": round(
+                (float(validator["commissionInfo"].get("rate", 0)) * 100), 2
+            ),
+            "status": validator["status"],
+            "uptime": round((float(validator.get("upTime", 0)) * 100), 2),
+        }
+        for validator in response
+    ]
 
     df = pd.DataFrame(results)
     if not df.empty:

openbb_terminal/cryptocurrency/discovery/dappradar_model.py

@@ -135,16 +135,16 @@ def get_top_dexes(sortby: str = "", limit: int = 10) -> pd.DataFrame:
         "oaGJtZGxjeVp6YjNKMFBYUnZkR0ZzVm05c2RXMWxTVzVHYVdGMEptOXlaR1Z5UFdSbGMyTW1iR2x0YVhROU1qWT0="
     )
     if data:
-        arr = []
-        for dex in data["dapps"]:
-            arr.append(
-                [
-                    dex["name"],
-                    dex["category"],
-                    dex["statistic"]["userActivity"],
-                    dex["statistic"]["totalVolumeInFiat"],
-                ]
-            )
+        arr = [
+            [
+                dex["name"],
+                dex["category"],
+                dex["statistic"]["userActivity"],
+                dex["statistic"]["totalVolumeInFiat"],
+            ]
+            for dex in data["dapps"]
+        ]
+
         df = pd.DataFrame(arr, columns=DEX_COLUMNS)
         if sortby in DEX_COLUMNS:
             df = df.sort_values(by=sortby, ascending=False)
@@ -177,16 +177,16 @@ def get_top_games(sortby: str = "", limit: int = 10) -> pd.DataFrame:
         "oYldWekpuTnZjblE5ZFhObGNpWnZjbVJsY2oxa1pYTmpKbXhwYldsMFBUSTI="
     )
     if data:
-        arr = []
-        for dex in data["dapps"]:
-            arr.append(
-                [
-                    dex["name"],
-                    dex["category"],
-                    dex["statistic"]["userActivity"],
-                    dex["statistic"]["totalVolumeInFiat"],
-                ]
-            )
+        arr = [
+            [
+                dex["name"],
+                dex["category"],
+                dex["statistic"]["userActivity"],
+                dex["statistic"]["totalVolumeInFiat"],
+            ]
+            for dex in data["dapps"]
+        ]
+
         df = pd.DataFrame(
             arr,
             columns=DEX_COLUMNS,
@@ -220,17 +220,17 @@ def get_top_dapps(sortby: str = "", limit: int = 10) -> pd.DataFrame:
         "2Y21SbGNqMWtaWE5qSm14cGJXbDBQVEky"
     )
     if data:
-        arr = []
-        for dex in data["dapps"]:
-            arr.append(
-                [
-                    dex["name"],
-                    dex["category"],
-                    dex["activeProtocols"],
-                    dex["statistic"]["userActivity"],
-                    dex["statistic"]["totalVolumeInFiat"],
-                ]
-            )
+        arr = [
+            [
+                dex["name"],
+                dex["category"],
+                dex["activeProtocols"],
+                dex["statistic"]["userActivity"],
+                dex["statistic"]["totalVolumeInFiat"],
+            ]
+            for dex in data["dapps"]
+        ]
+
         df = pd.DataFrame(
             arr,
             columns=DAPPS_COLUMNS,

openbb_terminal/cryptocurrency/due_diligence/coinpaprika_model.py

@@ -432,10 +432,7 @@ def basic_coin_info(symbol: str = "btc-bitcoin") -> pd.DataFrame:
         "proof_type",
         "contract",
     ]
-    results = {}
-    for key in keys:
-        results[key] = coin.get(key)
-
+    results = {key: coin.get(key) for key in keys}
     try:
         tags = ", ".join(t.get("name") for t in tags)
         parent = coin.get("parent") or {}

openbb_terminal/cryptocurrency/due_diligence/pycoingecko_model.py

@@ -599,9 +599,7 @@ def get_market_data(self) -> pd.DataFrame:
             "price_change_percentage_1y",
             "market_cap_change_24h",
         ]
-        single_stats = {}
-        for col in market_single_columns:
-            single_stats[col] = market_data.get(col)
+        single_stats = {col: market_data.get(col) for col in market_single_columns}
         single_stats.update(denominated_data)
 
         if (
@@ -644,10 +642,7 @@ def get_all_time_high(self, currency: str = "usd") -> pd.DataFrame:
             "ath_change_percentage",
         ]
 
-        results = {}
-        for column in ath_columns:
-            results[column] = market_data[column].get(currency)
-
+        results = {column: market_data[column].get(currency) for column in ath_columns}
         df = pd.Series(results).to_frame().reset_index()
         df.columns = ["Metric", "Value"]
         df["Metric"] = df["Metric"].apply(
@@ -680,10 +675,7 @@ def get_all_time_low(self, currency: str = "usd") -> pd.DataFrame:
             "atl_date",
             "atl_change_percentage",
         ]
-        results = {}
-        for column in ath_columns:
-            results[column] = market_data[column].get(currency)
-
+        results = {column: market_data[column].get(currency) for column in ath_columns}
         df = pd.Series(results).to_frame().reset_index()
         df.columns = ["Metric", "Value"]
         df["Metric"] = df["Metric"].apply(

openbb_terminal/econometrics/econometrics_view.py

@@ -367,11 +367,6 @@ def display_cointegration_test(
         External axes to plot on
     """
 
-    if isinstance(datasets, pd.DataFrame):
-        new_datasets = {}
-        for column in datasets.columns:
-            new_datasets[column] = datasets[column]
-
     pairs = list(combinations(datasets.keys(), 2))
     result: Dict[str, list] = {}
     z_values: Dict[str, pd.Series] = {}

openbb_terminal/economy/nasdaq_model.py

@@ -24,10 +24,12 @@ def check_country_code_type(list_of_codes: str) -> List[str]:
             "Code"
         ]
     )
-    valid_codes = []
-    for code in list_of_codes.split(","):
-        if code.upper() in nasdaq_codes:
-            valid_codes.append(code.upper())
+    valid_codes = [
+        code.upper()
+        for code in list_of_codes.split(",")
+        if code.upper() in nasdaq_codes
+    ]
+
     if valid_codes:
         return valid_codes
     raise argparse.ArgumentTypeError("No valid codes provided.")

openbb_terminal/etf/etf_controller.py

@@ -277,10 +277,11 @@ def call_load(self, other_args: List[str]):
                 self.etf_holdings = holdings.index[: ns_parser.limit].tolist()
 
                 if "n/a" in self.etf_holdings:
-                    na_tix_idx = []
-                    for idx, item in enumerate(self.etf_holdings):
-                        if item == "n/a":
-                            na_tix_idx.append(str(idx))
+                    na_tix_idx = [
+                        str(idx)
+                        for idx, item in enumerate(self.etf_holdings)
+                        if item == "n/a"
+                    ]
 
                     console.print(
                         f"n/a tickers found at position {','.join(na_tix_idx)}. "

openbb_terminal/forex/forex_helper.py

@@ -238,10 +238,7 @@ def display_candle(
             )
         if ma:
             # Manually construct the chart legend
-            colors = []
-
-            for i, _ in enumerate(ma):
-                colors.append(theme.get_colors()[i])
+            colors = [theme.get_colors()[i] for i, _ in enumerate(ma)]
 
             lines = [Line2D([0], [0], color=c) for c in colors]
             labels = ["MA " + str(label) for label in ma]

openbb_terminal/forex/oanda/oanda_model.py

@@ -393,23 +393,21 @@ def open_positions_request(accountID: str = account) -> Union[pd.DataFrame, bool
     try:
         request = positions.OpenPositions(accountID)
         response = client.request(request)
-        position_data = []
-        for i in range(len(response["positions"])):
-            position_data.append(
-                {
-                    "Instrument": response["positions"][i]["instrument"],
-                    "Long Units": response["positions"][i]["long"]["units"],
-                    "Total Long P/L": response["positions"][i]["long"]["units"],
-                    "Unrealized Long P/L": response["positions"][i]["long"][
-                        "unrealizedPL"
-                    ],
-                    "Short Units": response["positions"][i]["short"]["units"],
-                    "Total Short P/L": response["positions"][i]["short"]["pl"],
-                    "Short Unrealized P/L": response["positions"][i]["short"][
-                        "unrealizedPL"
-                    ],
-                }
-            )
+        position_data = [
+            {
+                "Instrument": response["positions"][i]["instrument"],
+                "Long Units": response["positions"][i]["long"]["units"],
+                "Total Long P/L": response["positions"][i]["long"]["units"],
+                "Unrealized Long P/L": response["positions"][i]["long"]["unrealizedPL"],
+                "Short Units": response["positions"][i]["short"]["units"],
+                "Total Short P/L": response["positions"][i]["short"]["pl"],
+                "Short Unrealized P/L": response["positions"][i]["short"][
+                    "unrealizedPL"
+                ],
+            }
+            for i in range(len(response["positions"]))
+        ]
+
         df_positions = pd.DataFrame.from_dict(position_data)
         return df_positions
     except V20Error as e:
@@ -443,20 +441,20 @@ def pending_orders_request(accountID: str = account) -> Union[pd.DataFrame, bool
     try:
         request = orders.OrdersPending(accountID)
         response = client.request(request)
-        pending_data = []
-        for i in range(len(response["orders"])):
-            pending_data.append(
-                {
-                    "Order ID": response["orders"][i]["id"],
-                    "Instrument": response["orders"][i]["instrument"],
-                    "Price": response["orders"][i]["price"],
-                    "Units": response["orders"][i]["units"],
-                    "Time Created": response["orders"][i]["createTime"][:10]
-                    + " "
-                    + response["orders"][i]["createTime"][11:19],
-                    "Time In Force": response["orders"][i]["timeInForce"],
-                }
-            )
+        pending_data = [
+            {
+                "Order ID": response["orders"][i]["id"],
+                "Instrument": response["orders"][i]["instrument"],
+                "Price": response["orders"][i]["price"],
+                "Units": response["orders"][i]["units"],
+                "Time Created": response["orders"][i]["createTime"][:10]
+                + " "
+                + response["orders"][i]["createTime"][11:19],
+                "Time In Force": response["orders"][i]["timeInForce"],
+            }
+            for i in range(len(response["orders"]))
+        ]
+
         if len(pending_data) == 0:
             return pd.DataFrame()
         df_pending = pd.DataFrame.from_dict(pending_data)
@@ -615,20 +613,20 @@ def get_candles_dataframe(
     try:
         request = instruments.InstrumentsCandles(instrument, params=parameters)
         response = client.request(request)
-        candles_data = []
-        for i in range(len(response["candles"])):
-            candles_data.append(
-                {
-                    "Date": response["candles"][i]["time"][:10]
-                    + " "
-                    + response["candles"][i]["time"][11:19],
-                    "Open": float(response["candles"][i]["mid"]["o"]),
-                    "High": float(response["candles"][i]["mid"]["h"]),
-                    "Low": float(response["candles"][i]["mid"]["l"]),
-                    "Close": float(response["candles"][i]["mid"]["c"]),
-                    "Volume": response["candles"][i]["volume"],
-                }
-            )
+        candles_data = [
+            {
+                "Date": response["candles"][i]["time"][:10]
+                + " "
+                + response["candles"][i]["time"][11:19],
+                "Open": float(response["candles"][i]["mid"]["o"]),
+                "High": float(response["candles"][i]["mid"]["h"]),
+                "Low": float(response["candles"][i]["mid"]["l"]),
+                "Close": float(response["candles"][i]["mid"]["c"]),
+                "Volume": response["candles"][i]["volume"],
+            }
+            for i in range(len(response["candles"]))
+        ]
+
         if len(candles_data) == 0:
             df_candles = pd.DataFrame()
         else: