Pre-Reveal Tests

"""
Update parameters
"""
COLLECTION = "MekaVerse"
ST_DEVS = 3  # set number of st devs away from mean offer
MINT_PRICE = 0.2
TOKEN_COL = "TOKEN_ID"

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

from honestnft_utils import config

sns.set_style("darkgrid")


"""
Plot params
"""
plt.rcParams.update(
    {
        "figure.facecolor": "white",
        "savefig.facecolor": "white",
        "figure.autolayout": True,
        "figure.figsize": [15, 5],
    }
)


"""
Import pre-reveal sales and bids
"""
bids = pd.read_csv(f"{config.PRE_REVEAL_BIDS_FOLDER}/{COLLECTION}_pre-reveal_bids.csv")
sales = pd.read_csv(
    f"{config.PRE_REVEAL_SALES_FOLDER}/{COLLECTION}_pre-reveal_sales.csv"
)


"""
Helper functions
"""


def get_mean(offers: pd.Series) -> float:
    return np.mean(offers)  # type: ignore


def get_st_dev(offers: pd.Series) -> float:
    return np.std(offers)  # type: ignore


def find_anomalous_sale(sale_price: float, mean: float, std: float) -> bool:
    if sale_price > (mean + ST_DEVS * std):
        return True
    else:
        return False


def percentage_change(col1: pd.Series, col2: pd.Series) -> pd.Series:
    return ((col2 - col1) / col1) * 100

"""
Print overpaid tokens relative to mean offer price
"""

overpaid_tokens = list()

for token in bids[TOKEN_COL].unique():
    offers = bids[bids[TOKEN_COL] == token]["OFFER"]
    if len(offers) > 5:
        mean = get_mean(offers)
        std = get_st_dev(offers)
        if len(sales[sales[TOKEN_COL] == token]) > 0:
            sale_price = sales[sales[TOKEN_COL] == token].iloc[0]["PRICE"]
            rank = sales[sales[TOKEN_COL] == token].iloc[0]["RANK"]
            user = sales[sales[TOKEN_COL] == token].iloc[0]["USER"]
            if find_anomalous_sale(sale_price, mean, std):
                print(
                    f"Token: {token}, Mean Offer: {mean}, Sale Price: {sale_price}, Rank: {rank}, User: {user}"
                )
                overpaid_tokens.append(token)
        else:
            continue

Token: 1545, Mean Offer: 5.1698318014423075, Sale Price: 8.44, Rank: 3655, User: 0x2f7fdc55cf9c62202576bf390fb03d762ac220a1
Token: 1798, Mean Offer: 4.484020833333333, Sale Price: 6.66, Rank: 4895, User: 0x467a44f1d5ac72fea19be7a6680abfaa14f2992a
Token: 174, Mean Offer: 1.3214516129032257, Sale Price: 6.0, Rank: 4864, User: 0xea253aa1737ca26099a169e6310b95ba42cf0a7e
Token: 613, Mean Offer: 3.254, Sale Price: 6.799, Rank: 5169, User: 0xa252bc234d2ebd1ba545dd46002b15915ae835d2

"""
Show distribution of offers and sale prices
"""

for token in overpaid_tokens:

    token_bids = bids[bids[TOKEN_COL] == token].copy()
    token_sales = sales[sales[TOKEN_COL] == token].copy()

    ax = sns.kdeplot(token_bids["OFFER"], fill=True)

    xmin, xmax, ymin, ymax = plt.axis()

    for index, row in token_sales.iterrows():
        plt.plot(
            [row["PRICE"], row["PRICE"]],
            [0, ymax],
            "r",
            label="SALE PRICE",
            linestyle="dashed",
        )

    plt.title(token)
    plt.show()

"""
Show offers that are significantly above mint price
"""

bids["MINT"] = MINT_PRICE
bids["OVER_MINT_PERC"] = percentage_change(bids["MINT"], bids["OFFER"])
bids.sort_values(by="OVER_MINT_PERC", ascending=False, inplace=True)
bids.head(40)

	Unnamed: 0	TOKEN_ID	USER	OFFER	DATE	RANK	MINT	OVER_MINT_PERC
843	843	1537	0x6b3205edec4db12a337eb8cda07e8bd1ac05bf46	8.200000	2021-10-10T14:08:49.942530	4106	0.2	4000.000000
1051	1051	197	0x85e2bd74a2f987fa93204c51a5b848039240d41a	7.800000	2021-10-10T05:38:45.246644	4421	0.2	3800.000000
847	847	1497	0x3896e573f5ed623876a4f98551a61daf3862f6f4	7.607249	2021-10-10T14:05:47.821228	5655	0.2	3703.624449
1013	1013	6309	0x39d0792ad8a277af90b91c32eabc2aef37f8c151	7.560000	2021-10-10T08:05:41.814990	6985	0.2	3680.000000
1048	1048	197	0x85e2bd74a2f987fa93204c51a5b848039240d41a	7.536900	2021-10-10T05:49:43.465671	4421	0.2	3668.450000
1100	1100	1545	0x86eaa58f41ba0437498581ce9eda504ba6cf5ab9	7.350000	2021-10-10T02:53:07.518731	3655	0.2	3575.000000
867	867	1497	0xaeb3fea566c8bcb7ae6826a03f818d9d386169ba	7.244999	2021-10-10T13:40:34.910681	5655	0.2	3522.499475
990	990	2019	0xb4cfb411252a80b35b6b73737ff11f510d0f5928	7.244000	2021-10-10T08:49:43.536124	242	0.2	3522.000000
1113	1113	6309	0x9a8b7ad7c7bc8502c0e31547de03ba864a444c1a	7.200000	2021-10-10T02:20:52.100388	6985	0.2	3500.000000
1064	1064	6844	0x9a8b7ad7c7bc8502c0e31547de03ba864a444c1a	7.200000	2021-10-10T04:55:47.269648	3375	0.2	3500.000000
1107	1107	6391	0x9a8b7ad7c7bc8502c0e31547de03ba864a444c1a	7.200000	2021-10-10T02:42:12.670765	6961	0.2	3500.000000
1109	1109	6368	0x9a8b7ad7c7bc8502c0e31547de03ba864a444c1a	7.200000	2021-10-10T02:35:57.733484	5117	0.2	3500.000000
1053	1053	197	0xac5eed1aa33250d755ccd837e683254a56373cb6	7.178000	2021-10-10T05:35:16.006723	4421	0.2	3489.000000
796	796	7657	0xaeb3fea566c8bcb7ae6826a03f818d9d386169ba	7.067025	2021-10-10T17:03:19.982690	5181	0.2	3433.512500
1095	1095	4423	0x9a8b7ad7c7bc8502c0e31547de03ba864a444c1a	7.050000	2021-10-10T03:05:06.247173	6444	0.2	3425.000000
1075	1075	4663	0x9a8b7ad7c7bc8502c0e31547de03ba864a444c1a	7.050000	2021-10-10T04:13:21.324647	3744	0.2	3425.000000
1091	1091	4457	0x9a8b7ad7c7bc8502c0e31547de03ba864a444c1a	7.050000	2021-10-10T03:14:27.801584	7591	0.2	3425.000000
795	795	614	0xaeb3fea566c8bcb7ae6826a03f818d9d386169ba	7.034999	2021-10-10T17:03:28.606101	2879	0.2	3417.499475
1103	1103	1545	0x65c35542db19e4978544655d416bed5cbcd5f500	7.000000	2021-10-10T02:48:22.324154	3655	0.2	3400.000000
521	521	1537	0x3e5d248bc1b369b3497f63ff40380b00c610ff58	7.000000	2021-10-11T09:55:17.066798	4106	0.2	3400.000000
1060	1060	2879	0x9a8b7ad7c7bc8502c0e31547de03ba864a444c1a	7.000000	2021-10-10T05:21:25.677457	4588	0.2	3400.000000
1086	1086	6722	0x6ed0e0a9fdb1e48eb8d09b10b327ee510efb7007	7.000000	2021-10-10T03:29:40.626167	3866	0.2	3400.000000
1102	1102	1545	0x65c35542db19e4978544655d416bed5cbcd5f500	7.000000	2021-10-10T02:48:23.873891	3655	0.2	3400.000000
926	926	1497	0x3896e573f5ed623876a4f98551a61daf3862f6f4	6.899999	2021-10-10T10:50:08.760850	5655	0.2	3349.999500
1058	1058	197	0xd3fd48b20d80101af6fa0666e9ed5a44768e5f30	6.835500	2021-10-10T05:31:06.566704	4421	0.2	3317.750000
1057	1057	197	0xd3fd48b20d80101af6fa0666e9ed5a44768e5f30	6.835500	2021-10-10T05:31:07.848802	4421	0.2	3317.750000
841	841	7657	0x3896e573f5ed623876a4f98551a61daf3862f6f4	6.730500	2021-10-10T14:12:14.754584	5181	0.2	3265.250000
788	788	614	0x201cedc1a286150a809f9f1a9e4c05c33ae154b1	6.702000	2021-10-10T17:30:12.970148	2879	0.2	3251.000000
928	928	614	0x3896e573f5ed623876a4f98551a61daf3862f6f4	6.699999	2021-10-10T10:48:18.926117	2879	0.2	3249.999500
778	778	6391	0x201cedc1a286150a809f9f1a9e4c05c33ae154b1	6.673000	2021-10-10T18:06:27.550156	6961	0.2	3236.500000
780	780	1497	0x201cedc1a286150a809f9f1a9e4c05c33ae154b1	6.673000	2021-10-10T17:56:33.062259	5655	0.2	3236.500000
777	777	6844	0x9f1a8e2d468098a784e03d349f6a784aea041b9c	6.671000	2021-10-10T18:08:34.123086	3375	0.2	3235.500000
781	781	1673	0x9f1a8e2d468098a784e03d349f6a784aea041b9c	6.671000	2021-10-10T17:54:32.518140	3437	0.2	3235.500000
787	787	614	0x9f1a8e2d468098a784e03d349f6a784aea041b9c	6.671000	2021-10-10T17:33:15.472341	2879	0.2	3235.500000
779	779	6391	0x9f1a8e2d468098a784e03d349f6a784aea041b9c	6.671000	2021-10-10T17:59:27.897834	6961	0.2	3235.500000
791	791	4457	0x9f1a8e2d468098a784e03d349f6a784aea041b9c	6.671000	2021-10-10T17:20:40.560796	7591	0.2	3235.500000
782	782	1605	0x9f1a8e2d468098a784e03d349f6a784aea041b9c	6.671000	2021-10-10T17:53:10.471459	7421	0.2	3235.500000
790	790	4663	0x9f1a8e2d468098a784e03d349f6a784aea041b9c	6.671000	2021-10-10T17:24:47.738939	3744	0.2	3235.500000
783	783	1537	0x9f1a8e2d468098a784e03d349f6a784aea041b9c	6.671000	2021-10-10T17:51:48.467531	4106	0.2	3235.500000
784	784	1497	0x9f1a8e2d468098a784e03d349f6a784aea041b9c	6.671000	2021-10-10T17:51:00.300600	5655	0.2	3235.500000