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def fitModels(new_df, tuning=False):
    """
    From a list of models. Fit and trains them to the dataframe.
    Returns the fitted models.
    """
    # Models
    models = {
        "adaboost":AdaBoostClassifier(random_state=11), 
        "gradboost":GradientBoostingClassifier(random_state=11), 
        "randomforest":RandomForestClassifier(random_state=11), 

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# Other models
from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier

models = {
    "adaboost":AdaBoostClassifier(random_state=11), 

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def balanceDecisions(n_df):
    """
    Rebalances data so that each class/decision is represented equally.
    """
    # Counting each class or decision
    counts = n_df['decision'].value_counts()

    # The lowest number of represented classes
    low_num = counts.min()

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def transformData(df, days=1):
    """
    Transforming data into X variables for training.  Uses percent change and 
    multiplies the percentage by 100 rounded to 2 decimal places.
    """
    # Transforming data
    new_df = df.pct_change(
        days
    ).apply(
        lambda x: round(x*100, 2)

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def getMultipleFunds(tickers, api_token):
    """
    Gets fundamental data from multiple stock tickers given as a list. Returns
    a large dataframe containing the concatenated information for all the given
    tickers.
    """
    
    # Verifying if the list of tickers is compatible
    available = client.get_exchange_symbols("US")

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def formatFundamentals(ticker, dropna=False):
    """
    Formats the given ticker's fundamental and price data. Cleans up the data by dropping
    any empty/nan values if requested.
    """
    
    # Getting fundamental data
    fund_data = getFundamentals(ticker)
    
    # Getting accompanying price data

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def getPrices(df, ticker):
    """
    Gets the stock price at the time for each date in the financial statements for
    the given ticker and dataframe of financial information.
    """
    # Getting stock price at the time
    prices = client.get_prices_eod(ticker, period='d')
    
    prices = pd.DataFrame(prices).set_index('date')[['adjusted_close', 'close', 'volume']]

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def getFundamentals(ticker):
    """
    Returns the fundamental data from the financial data API.  Combines the quarterly balance 
    sheet, cash flow, income statement, and earnings for a specific stock ticker.
    """
    
    # Getting data
    fund_data = client.get_fundamental_equity(ticker)
    
    # Financials

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# API Key
with open("../eodHistoricalData-API.txt", "r") as f:
    api_key = f.read()

# Backtest over 1 year and train on about 1 year's worth of data
train_days = 365

range_of_bt = 365

# Usual Backtest

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def fitModel(new_df):
    """
    Fits model to new data and returns the fitted model to be used for predictions.
    """
    
    # Column name
    col_name = new_df.columns[-1]

    # Variables
    X = new_df.drop(col_name, axis=1)