from otree.api import Currency as c, currency_range
from ._builtin import Page, WaitPage
import pandas as pd
from .models import Constants
import locale
from django import forms
import django
from django.conf import settings
settings.configure()  # You can pass default settings here if needed
django.setup()
class TradeForm(forms.Form):
    asset_name = forms.CharField(widget=forms.HiddenInput())
    action = forms.ChoiceField(choices=[('buy', 'Buy'), ('sell', 'Sell')])
    amount = forms.FloatField()
    round_number = forms.IntegerField(widget=forms.HiddenInput())

    def __init__(self, *args, **kwargs):
        super(TradeForm, self).__init__(*args, **kwargs)
        # your custom initialization code here

class InstructionPage(Page):
    form_model = 'player'
    form_fields = ['treatment_group']

    def is_displayed(self):
        return self.round_number == 1

    def vars_for_template(self):
        return dict(
            treatment_group=getattr(self.player, 'treatment_group')
        )
class TwitterPage(Page):
    form_model = 'player'
    form_fields = ['attention_check1']
    def is_displayed(self):
        return self.round_number == 2 and self.player.treatment_group == "control"
    def vars_for_template(self):

        return dict(
            attention_check1=getattr(self.player, 'attention_check1')
        )
class TwitterPageStimuli(Page):
    form_model = 'player'
    form_fields = ['attention_check1']
    def is_displayed(self):
        return self.round_number == 2 and self.player.treatment_group == "treatment_1"
    def vars_for_template(self):

        return dict(
            attention_check1=getattr(self.player, 'attention_check1')
        )
class TwitterPageDisclosure(Page):
    form_model = 'player'
    form_fields = ['attention_check1']
    def is_displayed(self):
        return self.round_number == 2 and self.player.treatment_group == "treatment_2"
    def vars_for_template(self):

        return dict(
            attention_check1=getattr(self.player, 'attention_check1')
        )
class TwitterPagePrebunk(Page):
    form_model = 'player'
    form_fields = ['attention_check1']
    def is_displayed(self):
        return self.round_number == 2 and self.player.treatment_group == "treatment_3"
    def vars_for_template(self):

        return dict(
            attention_check1=getattr(self.player, 'attention_check1')
        )
class TwitterPageInoculation(Page):
    form_model = 'player'
    form_fields = ['attention_check1']
    def is_displayed(self):
        return self.round_number == 2 and self.player.treatment_group == "treatment_4"
    def vars_for_template(self):

        return dict(
            attention_check1=getattr(self.player, 'attention_check1')
        )
class TwitterPageAll(Page):
    form_model = 'player'
    form_fields = ['attention_check1']
    def is_displayed(self):
        return self.round_number == 2 and self.player.treatment_group == "treatment_5"
    def vars_for_template(self):

        return dict(
            attention_check1=getattr(self.player, 'attention_check1')
        )
class TwitterPage2(Page):
    form_model = 'player'
    form_fields = ['attention_check1']
    def is_displayed(self):
        return self.round_number == 6 and self.player.treatment_group == "control"
    def vars_for_template(self):

        return dict(
            attention_check1=getattr(self.player, 'attention_check1')
        )
class TwitterPage2Stimuli(Page):
    form_model = 'player'
    form_fields = ['attention_check1']
    def is_displayed(self):
        return self.round_number == 6 and self.player.treatment_group == "treatment_1"
    def vars_for_template(self):

        return dict(
            attention_check1=getattr(self.player, 'attention_check1')
        )
class TwitterPage2Disclosure(Page):
    form_model = 'player'
    form_fields = ['attention_check1']
    def is_displayed(self):
        return self.round_number == 6 and self.player.treatment_group == "treatment_2"
    def vars_for_template(self):

        return dict(
            attention_check1=getattr(self.player, 'attention_check1')
        )
class TwitterPage2Prebunk(Page):
    form_model = 'player'
    form_fields = ['attention_check1']
    def is_displayed(self):
        return self.round_number == 6 and self.player.treatment_group == "treatment_3"
    def vars_for_template(self):

        return dict(
            attention_check1=getattr(self.player, 'attention_check1')
        )
class TwitterPage2Inoculation(Page):
    form_model = 'player'
    form_fields = ['attention_check1']
    def is_displayed(self):
        return self.round_number == 6 and self.player.treatment_group == "treatment_4"
    def vars_for_template(self):

        return dict(
            attention_check1=getattr(self.player, 'attention_check1')
        )
class TwitterPage2All(Page):
    form_model = 'player'
    form_fields = ['attention_check1']
    def is_displayed(self):
        return self.round_number == 6 and self.player.treatment_group == "treatment_5"
    def vars_for_template(self):

        return dict(
            attention_check1=getattr(self.player, 'attention_check1')
        )
class RedditPage(Page):
    def is_displayed(self):
        return self.round_number == 3 and self.player.treatment_group == "control"
class RedditPageStimuli(Page):
    def is_displayed(self):
        return self.round_number == 3 and self.player.treatment_group == "treatment_1"
class RedditPageDisclosure(Page):
    def is_displayed(self):
        return self.round_number == 3 and self.player.treatment_group == "treatment_2"
class RedditPagePrebunk(Page):
    def is_displayed(self):
        return self.round_number == 3 and self.player.treatment_group == "treatment_3"
class RedditPageInoculation(Page):
    def is_displayed(self):
        return self.round_number == 3 and self.player.treatment_group == "treatment_4"
class RedditPageAll(Page):
    def is_displayed(self):
        return self.round_number == 3 and self.player.treatment_group == "treatment_5"

class RedditPage2(Page):
    def is_displayed(self):
        return self.round_number == 7 and self.player.treatment_group == "control"
class RedditPage2Stimuli(Page):
    def is_displayed(self):
        return self.round_number == 7 and self.player.treatment_group == "treatment_1"
class RedditPage2Disclosure(Page):
    def is_displayed(self):
        return self.round_number == 7 and self.player.treatment_group == "treatment_2"
class RedditPage2Prebunk(Page):
    def is_displayed(self):
        return self.round_number == 7 and self.player.treatment_group == "treatment_3"
class RedditPage2Inoculation(Page):
    def is_displayed(self):
        return self.round_number == 7 and self.player.treatment_group == "treatment_4"
class RedditPage2All(Page):
    def is_displayed(self):
        return self.round_number == 7 and self.player.treatment_group == "treatment_5"
class TikTokPage(Page):
    def is_displayed(self):
        return self.round_number == 4 and self.player.treatment_group == "control"
class TikTokPageStimuli(Page):
    def is_displayed(self):
        return (self.round_number == 4 and
                (self.player.treatment_group in ["treatment_1", "treatment_3", "treatment_4", "treatment_5"]))
class TikTokPageDisclosure(Page):
    def is_displayed(self):
        return self.round_number == 4 and self.player.treatment_group == "treatment_2"
class TikTokPagePrebunk(Page):
    def is_displayed(self):
        return self.round_number == 4 and self.player.treatment_group == "treatment_3"
class TikTokPageInoculation(Page):
    def is_displayed(self):
        return (self.round_number == 4 and
                (self.player.treatment_group in ["treatment_4", "treatment_5"]))

class TikTokPage2(Page):
    def is_displayed(self):
        return self.round_number == 9 and self.player.treatment_group == "control"
class TikTokPage2Stimuli(Page):
    def is_displayed(self):
        return (self.round_number == 9 and
                (self.player.treatment_group in ["treatment_1", "treatment_3", "treatment_4", "treatment_5"]))
class TikTokPage2Disclosure(Page):
    def is_displayed(self):
        return self.round_number == 9 and self.player.treatment_group == "treatment_2"
class TikTokPage2Prebunk(Page):
    def is_displayed(self):
        return self.round_number == 9 and self.player.treatment_group == "treatment_3"
class TikTokPage2Inoculation(Page):
    def is_displayed(self):
        return (self.round_number == 9 and
                (self.player.treatment_group in ["treatment_4", "treatment_5"]))


class StartPage(Page):
    def is_displayed(self):
        return self.round_number <= self.session.num_rounds
    
    def vars_for_template(self):
        is_training_round = self.session.config['training_round'] and self.round_number == 1
        return dict(
            is_training_round=is_training_round,
        )

class TradingPage(Page):
    live_method = 'live_trading_report'
    form_model = 'player'
    form_fields = [
        'buy_count_VOO',
        'buy_count_VTI',
        'buy_count_ZZW',
        'buy_count_MDKS',
        'buy_count_UDX',
        'buy_count_DSLO',
        'buy_count_NOQ',
        'buy_count_XPOW',
        'buy_count_MWQ',
        'buy_count_STCK',
        'buy_count_IUM',
        'buy_count_JUNW',
        'buy_count_ETQ',
        'buy_count_BTC',
        'cash',
    ]

    def get_form_fields(self):
        form_fields = [
            'buy_count_VOO',
            'buy_count_VTI',
            'buy_count_ZZW',
            'buy_count_MDKS',
            'buy_count_UDX',
            'buy_count_DSLO',
            'buy_count_NOQ',
            'buy_count_XPOW',
            'buy_count_MWQ',
            'buy_count_STCK',
            'buy_count_IUM',
            'buy_count_JUNW',
            'buy_count_ETQ',
            'buy_count_BTC',
            'cash',
        ]
        return form_fields # and so on for all fields

    def is_displayed(self):
        return self.round_number <= self.session.num_rounds

    def vars_for_template(self):
        # Define the data for the dataframe with changes again
        data = {
            'Type': ['ETF', 'ETF', 'ETF', 'ETF', 'Stock', 'Stock', 'Stock', 'Stock', 'Stock', 'Stock', 'Crypto',
                     'Crypto', 'Crypto', 'Crypto'],
            'Base_Ticker': ['VOO', 'VTI', 'ZWC', 'PSA.TO', 'SU', 'WEED', 'MFC', 'ENB', 'BTE', 'STCK', 'DELX', 'BITK',
                            'ETHQ', 'BTC'],
            'Modified_Ticker': ['VOO', 'VTI', 'ZZW', 'MDKS', 'UDX', 'DSLO', 'NOQ', 'XPOW', 'MWQ', 'STCK', 'IUM', 'JUNW',
                                'ETQ', 'BTC'],
            'Round_1': ['$364.30', '$206.69', '$17.34', '$50.01', '$26.27', '$40.36', '$27.03', '$45.78', '$1.31',
                        '$12.08', '$0.09', '$1.17', '$9.36', '$58,918.83'],
            'Round_1_Change': ['6.00%', '6.19%', '7.04%', '0.02%', '23.04%', '28.86%', '19.34%', '12.45%', '89.86%',
                               '0.67%', '0.00%', '0.00%', '4.00%', '103.16%'],
            'Round_2': ['$393.52', '$222.82', '$18.19', '$50.01', '$29.69', '$29.98', '$24.40', '$49.63', '$2.39',
                        '$11.20', '$0.50', '$0.66', '$11.11', '$35,040.84'],
            'Round_2_Change': ['8.02%', '7.80%', '4.90%', '0.00%', '13.02%', '-25.72%', '-9.73%', '8.41%', '82.44%',
                               '-7.28%', '455.56%', '-43.59%', '18.70%', '-40.53%'],
            'Round_3': ['$394.40', '$222.06', '$18.15', '$50.00', '$26.26', '$17.55', '$24.38', '$50.46', '$3.45',
                        '$9.92', '$0.52', '$0.37', '$15.59', '$43,790.89'],
            'Round_3_Change': ['0.22%', '-0.34%', '-0.22%', '-0.02%', '-11.55%', '-41.46%', '-0.08%', '1.67%', '44.35%',
                               '-11.43%', '4.00%', '-43.94%', '40.32%', '24.97%'],
            'Round_4': ['$436.57', '$241.44', '$18.95', '$50.01', '$31.65', '$11.04', '$24.11', '$49.41', '$3.91',
                        '$9.09', '$0.46', '$0.27', '$20.05', '$46,306.45'],
            'Round_4_Change': ['10.69%', '8.73%', '4.41%', '0.02%', '20.53%', '-37.09%', '-1.11%', '-2.08%', '13.33%',
                               '-8.37%', '-11.54%', '-27.03%', '28.61%', '5.74%'],
            'Round_5': ['$415.17', '$227.67', '$20.00', '$50.04', '$40.70', '$9.48', '$26.66', '$57.55', '$5.45',
                        '$9.00', '$0.39', '$0.21', '$16.68', '$45,538.68'],
            'Round_5_Change': ['-4.90%', '-5.70%', '5.54%', '0.06%', '28.59%', '-14.13%', '10.58%', '16.47%', '39.39%',
                               '-0.99%', '-15.22%', '-22.22%', '-16.81%', '-1.66%'],
            'Round_6': ['$346.88', '$188.62', '$17.65', '$50.00', '$45.16', '$3.66', '$22.32', '$54.36', '$6.25',
                        '$7.00', '$0.20', '$0.07', '$5.30', '$19,784.73'],
            'Round_6_Change': ['-16.45%', '-17.15%', '-11.75%', '-0.08%', '10.96%', '-61.39%', '-16.28%', '-5.54%',
                               '14.68%', '-22.22%', '-48.72%', '-66.67%', '-68.23%', '-56.55%'],
            'Round_7': ['$328.30', '$179.47', '$16.78', '$50.02', '$38.90', '$3.75', '$21.68', '$51.22', '$5.85',
                        '$6.70', '$0.16', '$0.16', '$7.50', '$19,431.79'],
            'Round_7_Change': ['-5.36%', '-4.85%', '-4.93%', '0.04%', '-13.86%', '2.46%', '-2.87%', '-5.78%', '-6.40%',
                               '-4.29%', '-20.00%', '128.57%', '41.51%', '-1.78%'],
            'Round_8': ['$351.34', '$191.19', '$17.12', '$50.03', '$42.95', '$3.14', '$24.15', '$52.92', '$6.08',
                        '$5.85', '$0.08', '$0.11', '$6.48', '$16,547.50'],
            'Round_8_Change': ['7.02%', '6.53%', '2.03%', '0.02%', '10.41%', '-16.27%', '11.39%', '3.32%', '3.93%',
                               '-12.69%', '-50.00%', '-31.25%', '-13.60%', '-14.84%'],
            'Round_9': ['$376.07', '$204.10', '$17.35', '$50.02', '$41.96', '$2.37', '$24.80', '$51.53', '$5.07',
                        '$6.50', '$0.13', '$0.10', '$9.89', '$28,478.48'],
            'Round_9_Change': ['7.04%', '6.75%', '1.34%', '-0.02%', '2.69%', '-24.52%', '0.97%', '-2.63%', '-16.61%',
                               '11.11%', '62.50%', '-9.09%', '52.62%', '72.10%'],
            'Round_10': ['$407.28', '$220.28', '$17.00', '$50.04', '$38.86', '$0.51', '$25.04', '$49.24', '$4.32',
                         '$6.80', '$0.10', '$0.10', '$10.13', '$30,477.25'],
            'Round 10_Change': ['8.30%', '7.93%', '-2.02%', '0.04%', '-7.39%', '-78.48%', '0.97%', '-4.44%', '-14.79%',
                                '4.62%', '-23.08%', '0.00%', '2.43%', '7.02%']
        }
        data_cap = {
            "Modified_Ticker": ["VOO", "VTI", "ZZW", "MDKS", "UDX", "DSLO", "NOQ", "XPOW", "MWQ", "STCK", "IUM", "JUNW",
                                "ETQ", "BTC"],
            "Round_1_Market_Cap/AUM": ["691.0B", "1.2T", "908.0M", "2.0B", "31.8B", "15.4B", "52.5B", "92.8B", "739.0M",
                                       "99.9M", "8.7M", "19.0M", "22.3M", "1.1T"],
            "Round_2_Market_Cap/AUM": ["753.4B", "1.3T", "1.1B", "1.7B", "36.1B", "11.8B", "47.4B", "100.5B", "1.3B",
                                       "99.9M", "48.2M", "11.1M", "22.3M", "673.1B"],
            "Round_3_Market_Cap/AUM": ["770.0B", "1.3T", "1.2B", "1.8B", "30.8B", "6.9B", "47.4B", "102.2B", "1.9B",
                                       "90.2M", "60.4M", "6.2M", "258.3M", "794.6B"],
            "Round_4_Market_Cap/AUM": ["856.1B", "1.4T", "1.3B", "1.8B", "36.1B", "4.4B", "46.8B", "100.1B", "2.2B",
                                       "82.7M", "54.3M", "4.5M", "316.1M", "892.3B"],
            "Round_5_Market_Cap/AUM": ["841.7B", "1.3T", "1.5B", "1.8B", "46.6B", "3.7B", "51.4B", "116.6B", "3.1B",
                                       "81.8M", "46.0M", "3.5M", "281.8M", "894.1B"],
            "Round_6_Market_Cap/AUM": ["709.8B", "1.1T", "1.5B", "2.0B", "49.6B", "1.5B", "42.6B", "110.1B", "3.5B",
                                       "63.6M", "23.6M", "1.2M", "63.3M", "383.7B"],
            "Round_7_Market_Cap/AUM": ["686.5B", "1.1T", "1.5B", "2.7B", "38.4B", "1.8B", "41.2B", "103.7B", "3.2B",
                                       "61.9M", "20.5M", "2.6M", "15.1M", "375.1B"],
            "Round_8_Market_Cap/AUM": ["744.8B", "1.2T", "1.5B", "3.8B", "42.8B", "1.5B", "45.3B", "107.2B", "3.3B",
                                       "54.0M", "10.7M", "1.8M", "12.3M", "319.6B"],
            "Round_9_Market_Cap/AUM": ["803.5B", "1.2T", "1.6B", "4.3B", "41.0B", "1.2B", "45.9B", "104.3B", "2.8B",
                                       "58.9M", "17.4M", "1.7M", "20.4M", "548.1B"],
            "Round_10_Market_Cap/AUM": ["887.0B", "1.4T", "1.6B", "5.0B", "38.2B", "311.6M", "45.9B", "99.7B", "3.7B",
                                        "61.5M", "13.2M", "1.7M", "22.3M", "591.1B"]
        }

        # Create the dataframe
        df = pd.read_csv('ZTS/Ticker_Values_Stand.csv')
        skip = 1 + 2 * (self.round_number)
        cols = df.iloc[:, skip:skip + 2].copy()
        df = df.iloc[:, 0:3]
        df = pd.concat([df, cols], axis=1)

        # Convert to DataFrame
        df_cap = pd.DataFrame(data_cap)
        skip = (self.round_number)
        cols_cap = df_cap.iloc[:, skip:skip + 1].copy()
        df = pd.concat([df, cols_cap], axis=1)

        current_columns = df.columns.tolist()
        current_columns[3] = 'Round_1'
        current_columns[4] = 'Round_1_Change'
        current_columns[-1] = 'Market_Cap'
        # Assign the modified column labels back to the DataFrame
        df.columns = current_columns
        df['change_type'] = df['Type'] != df['Type'].shift()
        df_to_list_of_dicts = df.to_dict(orient='records')

        data_modified = []
        for row in df_to_list_of_dicts:
            modified_row = row.copy()
            buy_fieldname = 'buy_count_' + row['Modified_Ticker']
            sell_fieldname = 'sell_count_' + row['Modified_Ticker']

            modified_row['buy_fieldname'] = buy_fieldname
            modified_row['sell_fieldname'] = sell_fieldname

            data_modified.append(modified_row)
        previous_round_number = self.subsession.round_number - 1
        tickers = [
            'VOO', 'VTI', 'ZZW', 'MDKS', 'UDX', 'DSLO', 'NOQ',
            'XPOW', 'MWQ', 'STCK', 'IUM', 'JUNW', 'ETQ', 'BTC'
        ]
        initial_values = {}
        if previous_round_number == 0:
            for ticker in tickers:
                initial_values[f'buy_count_{ticker}_initial'] = 0
        else:
            # Otherwise, retrieve the previous round's player and get the value for each ticker
            previous_player = self.player.in_round(previous_round_number)
            for ticker in tickers:
                initial_values[f'buy_count_{ticker}_initial'] = getattr(previous_player, f'buy_count_{ticker}')
        self.player.update_portfolio_value(pd.DataFrame(data_modified))
        initial_values['cash'] = getattr(self.player, 'cash')
        data = {
            "Modified_Ticker": ["VOO", "VTI", "ZZW", "MDKS", "UDX", "DSLO", "NOQ", "XPOW", "MWQ", "STCK", "IUM", "JUNW",
                                "ETQ", "BTC"],
            "Round_1_Market_Cap/AUM": ["691.0B", "1.2T", "908.0M", "2.0B", "31.8B", "15.4B", "52.5B", "92.8B", "739.0M",
                                       "99.9M", "8.7M", "19.0M", "22.3M", "1.1T"],
            "Round_2_Market_Cap/AUM": ["753.4B", "1.3T", "1.1B", "1.7B", "36.1B", "11.8B", "47.4B", "100.5B", "1.3B",
                                       "99.9M", "48.2M", "11.1M", "22.3M", "673.1B"],
            "Round_3_Market_Cap/AUM": ["770.0B", "1.3T", "1.2B", "1.8B", "30.8B", "6.9B", "47.4B", "102.2B", "1.9B",
                                       "90.2M", "60.4M", "6.2M", "258.3M", "794.6B"],
            "Round_4_Market_Cap/AUM": ["856.1B", "1.4T", "1.3B", "1.8B", "36.1B", "4.4B", "46.8B", "100.1B", "2.2B",
                                       "82.7M", "54.3M", "4.5M", "316.1M", "892.3B"],
            "Round_5_Market_Cap/AUM": ["841.7B", "1.3T", "1.5B", "1.8B", "46.6B", "3.7B", "51.4B", "116.6B", "3.1B",
                                       "81.8M", "46.0M", "3.5M", "281.8M", "894.1B"],
            "Round_6_Market_Cap/AUM": ["709.8B", "1.1T", "1.5B", "2.0B", "49.6B", "1.5B", "42.6B", "110.1B", "3.5B",
                                       "63.6M", "23.6M", "1.2M", "63.3M", "383.7B"],
            "Round_7_Market_Cap/AUM": ["686.5B", "1.1T", "1.5B", "2.7B", "38.4B", "1.8B", "41.2B", "103.7B", "3.2B",
                                       "61.9M", "20.5M", "2.6M", "15.1M", "375.1B"],
            "Round_8_Market_Cap/AUM": ["744.8B", "1.2T", "1.5B", "3.8B", "42.8B", "1.5B", "45.3B", "107.2B", "3.3B",
                                       "54.0M", "10.7M", "1.8M", "12.3M", "319.6B"],
            "Round_9_Market_Cap/AUM": ["803.5B", "1.2T", "1.6B", "4.3B", "41.0B", "1.2B", "45.9B", "104.3B", "2.8B",
                                       "58.9M", "17.4M", "1.7M", "20.4M", "548.1B"],
            "Round_10_Market_Cap/AUM": ["887.0B", "1.4T", "1.6B", "5.0B", "38.2B", "311.6M", "45.9B", "99.7B", "3.7B",
                                        "61.5M", "13.2M", "1.7M", "22.3M", "591.1B"]
        }

        # Convert to DataFrame
        df_cap = pd.DataFrame(data)
        return {
            'data': data_modified,
            **initial_values
        }

    #def before_next_page(self):
        # This method is called before moving to the next page
        #buy_count_VOO_value = self.player.buy_count_VOO
        # ... handle other fields similarly ...


class ProlificIDPage(Page):
    form_model = 'player'
    form_fields = ['prolificid']
    def is_displayed(self):
        return self.round_number == 10

class ThankYouPage(Page):
    def is_displayed(self):
        return self.round_number == 10
class ResultsPage(Page):
    def is_displayed(self):
        return self.round_number <= self.session.num_rounds

    def to_human_readable(self, x):
        return '{:,}'.format(int(x))

    def vars_for_template(self):
        data = {
            'Type': ['ETF', 'ETF', 'ETF', 'ETF', 'Stock', 'Stock', 'Stock', 'Stock', 'Stock', 'Stock', 'Crypto',
                     'Crypto', 'Crypto', 'Crypto'],
            'Base_Ticker': ['VOO', 'VTI', 'ZWC', 'PSA.TO', 'SU', 'WEED', 'MFC', 'ENB', 'BTE', 'STCK', 'DELX', 'BITK',
                            'ETHQ', 'BTC'],
            'Modified_Ticker': ['VOO', 'VTI', 'ZZW', 'MDKS', 'UDX', 'DSLO', 'NOQ', 'XPOW', 'MWQ', 'STCK', 'IUM', 'JUNW',
                                'ETQ', 'BTC'],
            'Round_1': ['$364.30', '$206.69', '$17.34', '$50.01', '$26.27', '$40.36', '$27.03', '$45.78', '$1.31',
                        '$12.08', '$0.09', '$1.17', '$9.36', '$58,918.83'],
            'Round_1_Change': ['6.00%', '6.19%', '7.04%', '0.02%', '23.04%', '28.86%', '19.34%', '12.45%', '89.86%',
                               '0.67%', '0.00%', '0.00%', '4.00%', '103.16%'],
            'Round_2': ['$393.52', '$222.82', '$18.19', '$50.01', '$29.69', '$29.98', '$24.40', '$49.63', '$2.39',
                        '$11.20', '$0.50', '$0.66', '$11.11', '$35,040.84'],
            'Round_2_Change': ['8.02%', '7.80%', '4.90%', '0.00%', '13.02%', '-25.72%', '-9.73%', '8.41%', '82.44%',
                               '-7.28%', '455.56%', '-43.59%', '18.70%', '-40.53%'],
            'Round_3': ['$394.40', '$222.06', '$18.15', '$50.00', '$26.26', '$17.55', '$24.38', '$50.46', '$3.45',
                        '$9.92', '$0.52', '$0.37', '$15.59', '$43,790.89'],
            'Round_3_Change': ['0.22%', '-0.34%', '-0.22%', '-0.02%', '-11.55%', '-41.46%', '-0.08%', '1.67%', '44.35%',
                               '-11.43%', '4.00%', '-43.94%', '40.32%', '24.97%'],
            'Round_4': ['$436.57', '$241.44', '$18.95', '$50.01', '$31.65', '$11.04', '$24.11', '$49.41', '$3.91',
                        '$9.09', '$0.46', '$0.27', '$20.05', '$46,306.45'],
            'Round_4_Change': ['10.69%', '8.73%', '4.41%', '0.02%', '20.53%', '-37.09%', '-1.11%', '-2.08%', '13.33%',
                               '-8.37%', '-11.54%', '-27.03%', '28.61%', '5.74%'],
            'Round_5': ['$415.17', '$227.67', '$20.00', '$50.04', '$40.70', '$9.48', '$26.66', '$57.55', '$5.45',
                        '$9.00', '$0.39', '$0.21', '$16.68', '$45,538.68'],
            'Round_5_Change': ['-4.90%', '-5.70%', '5.54%', '0.06%', '28.59%', '-14.13%', '10.58%', '16.47%', '39.39%',
                               '-0.99%', '-15.22%', '-22.22%', '-16.81%', '-1.66%'],
            'Round_6': ['$346.88', '$188.62', '$17.65', '$50.00', '$45.16', '$3.66', '$22.32', '$54.36', '$6.25',
                        '$7.00', '$0.20', '$0.07', '$5.30', '$19,784.73'],
            'Round_6_Change': ['-16.45%', '-17.15%', '-11.75%', '-0.08%', '10.96%', '-61.39%', '-16.28%', '-5.54%',
                               '14.68%', '-22.22%', '-48.72%', '-66.67%', '-68.23%', '-56.55%'],
            'Round_7': ['$328.30', '$179.47', '$16.78', '$50.02', '$38.90', '$3.75', '$21.68', '$51.22', '$5.85',
                        '$6.70', '$0.16', '$0.16', '$7.50', '$19,431.79'],
            'Round_7_Change': ['-5.36%', '-4.85%', '-4.93%', '0.04%', '-13.86%', '2.46%', '-2.87%', '-5.78%', '-6.40%',
                               '-4.29%', '-20.00%', '128.57%', '41.51%', '-1.78%'],
            'Round_8': ['$351.34', '$191.19', '$17.12', '$50.03', '$42.95', '$3.14', '$24.15', '$52.92', '$6.08',
                        '$5.85', '$0.08', '$0.11', '$6.48', '$16,547.50'],
            'Round_8_Change': ['7.02%', '6.53%', '2.03%', '0.02%', '10.41%', '-16.27%', '11.39%', '3.32%', '3.93%',
                               '-12.69%', '-50.00%', '-31.25%', '-13.60%', '-14.84%'],
            'Round_9': ['$376.07', '$204.10', '$17.35', '$50.02', '$41.96', '$2.37', '$24.80', '$51.53', '$5.07',
                        '$6.50', '$0.13', '$0.10', '$9.89', '$28,478.48'],
            'Round_9_Change': ['7.04%', '6.75%', '1.34%', '-0.02%', '2.69%', '-24.52%', '0.97%', '-2.63%', '-16.61%',
                               '11.11%', '62.50%', '-9.09%', '52.62%', '72.10%'],
            'Round_10': ['$407.28', '$220.28', '$17.00', '$50.04', '$38.86', '$0.51', '$25.04', '$49.24', '$4.32',
                         '$6.80', '$0.10', '$0.10', '$10.13', '$30,477.25'],
            'Round 10_Change': ['8.30%', '7.93%', '-2.02%', '0.04%', '-7.39%', '-78.48%', '0.97%', '-4.44%', '-14.79%',
                                '4.62%', '-23.08%', '0.00%', '2.43%', '7.02%']
        }

        # Create the dataframe
        df = pd.read_csv('ZTS/Ticker_Values_Stand.csv')
        skip = 3 + 2 * (self.round_number)
        cols = df.iloc[:, skip:skip + 2].copy()
        df = df.iloc[:, 0:3]
        df = pd.concat([df, cols], axis=1)

        current_columns = df.columns.tolist()
        current_columns[3] = 'Round_1'
        current_columns[4] = 'Round_1_Change'
        # Assign the modified column labels back to the DataFrame
        df.columns = current_columns

        df_to_list_of_dicts = df.to_dict(orient='records')

        data_modified = []
        for row in df_to_list_of_dicts:
            modified_row = row.copy()
            buy_fieldname = 'buy_count_' + row['Modified_Ticker']
            sell_fieldname = 'sell_count_' + row['Modified_Ticker']

            modified_row['buy_fieldname'] = buy_fieldname
            modified_row['sell_fieldname'] = sell_fieldname

            data_modified.append(modified_row)
        previous_round_number = self.subsession.round_number - 1
        tickers = [
            'VOO', 'VTI', 'ZZW', 'MDKS', 'UDX', 'DSLO', 'NOQ',
            'XPOW', 'MWQ', 'STCK', 'IUM', 'JUNW', 'ETQ', 'BTC'
        ]
        initial_values = {}
        if previous_round_number == 0:
            for ticker in tickers:
                initial_values[f'buy_count_{ticker}_initial'] = 0
        else:
            # Otherwise, retrieve the previous round's player and get the value for each ticker
            previous_player = self.player.in_round(previous_round_number)
            for ticker in tickers:
                initial_values[f'buy_count_{ticker}_initial'] = getattr(previous_player, f'buy_count_{ticker}')
        self.player.update_portfolio_value(pd.DataFrame(data_modified))

        return dict(
            cash = self.to_human_readable(self.player.cash),
            share_value = self.to_human_readable(self.player.share_value),
            portfolio_value = self.to_human_readable(self.player.portfolio_value),
            portfolio_profit=self.to_human_readable(self.player.portfolio_profit),
        )

page_sequence = [
                 InstructionPage,
                 TwitterPage,
                 TwitterPageAll,
                 TwitterPageDisclosure,
                 TwitterPageInoculation,
                 TwitterPagePrebunk,
                 TwitterPageStimuli,
                 TwitterPage2,
                 TwitterPage2All,
                 TwitterPage2Disclosure,
                 TwitterPage2Inoculation,
                 TwitterPage2Prebunk,
                 TwitterPage2Stimuli,
                 RedditPage,
                 RedditPageAll,
                 RedditPageDisclosure,
                 RedditPageInoculation,
                 RedditPageStimuli,
                 RedditPagePrebunk,
                 RedditPage2,
                 RedditPage2All,
                 RedditPage2Disclosure,
                 RedditPage2Inoculation,
                 RedditPage2Stimuli,
                 RedditPage2Prebunk,
                 TikTokPage,
                 TikTokPageDisclosure,
                 TikTokPageInoculation,
                 TikTokPagePrebunk,
                 TikTokPageStimuli,
                 TikTokPage2,
                 TikTokPage2Disclosure,
                 TikTokPage2Inoculation,
                 TikTokPage2Prebunk,
                 TikTokPage2Stimuli,
                 TradingPage,
                 ResultsPage,
                 ProlificIDPage,
                 ThankYouPage,
                 ]