From 6150096dcc55a6f117766d001823005df90e5820 Mon Sep 17 00:00:00 2001 From: Ben Liu Date: Wed, 27 Dec 2023 08:50:30 +0800 Subject: [PATCH] added work on get average yield --- lib_const.py | 2 + lib_data.py | 47 +++- lib_logic.py | 33 +-- main.py | 119 ++++----- main_avg_yld.py | 98 +++++++ poetry.lock | 674 +++++++++++++++++++++++++++++++++++++++++++++++- pyproject.toml | 3 + 7 files changed, 877 insertions(+), 99 deletions(-) create mode 100644 main_avg_yld.py diff --git a/lib_const.py b/lib_const.py index affd3e1..3dc00f5 100644 --- a/lib_const.py +++ b/lib_const.py @@ -26,6 +26,8 @@ def get_pool_filename(pool_address, token0=None, token1=None): file_name = 'output/pool_data_' + file_name_addon + '.csv' return file_name +def get_crypto_price_filename(token = None): + return 'output/price_data_all_token.csv' # date_begin = datetime.strptime(date_begin_yyyymmdd, '%Y%m%d') # date_end = datetime.strptime(date_end_yyyymmdd, '%Y%m%d') \ No newline at end of file diff --git a/lib_data.py b/lib_data.py index fe484d0..ea3fca0 100644 --- a/lib_data.py +++ b/lib_data.py @@ -1,8 +1,24 @@ import requests from datetime import datetime, timedelta import pandas as pd +import lib_const +def get_crypto_price_data_csv(date_begin=datetime(2000, 1,1), date_end=datetime(3000,1,1)): + + file_name = lib_const.get_crypto_price_filename() + df=pd.read_csv(file_name) + df['date'] = pd.to_datetime(df['date']) + + if(isinstance(date_begin, str)) : + date_begin = datetime.strptime(date_begin, "%Y-%m-%d") + if(isinstance(date_end, str)) : + date_end = datetime.strptime(date_end, "%Y-%m-%d") + df = df[ (df['date'] >= date_begin) & (df['date'] <= date_end) ] + + df.set_index('date', inplace=True) + return df + # API each time can only get 100 recoreds, hence break down the retrieve into year-month def get_uniswap_v3_data_limit100(pool_address, from_timestamp, to_timestamp): # Uniswap V3 Subgraph endpoint @@ -44,7 +60,7 @@ def last_day_of_month(year, month): return last_day_of_month -def get_uniswap_v3_data_year(pool_address, years): +def download_uniswap_v3_data_year(pool_address, years): pool_df=pd.DataFrame() # Get Uniswap V3 data @@ -60,7 +76,26 @@ def get_uniswap_v3_data_year(pool_address, years): return pool_df -def get_crypto_price(symbol, token, start_date, end_date, vs_currency='usd'): + + + + +def get_uniswap_pool_data_csv(pool_address, date_begin=datetime(2000, 1,1), date_end=datetime(3000,1,1)): + data_file_name = lib_const.get_pool_filename(pool_address) + df = pd.read_csv(data_file_name) + + if(isinstance(date_begin, str)) : + date_begin = datetime.strptime(date_begin, "%Y-%m-%d") + if(isinstance(date_end, str)) : + date_end = datetime.strptime(date_end, "%Y-%m-%d") + + df['date'] = pd.to_datetime(df['date'], unit='s') + df = df[(df['date'] >= date_begin) & (df['date'] <= date_end)] + df = df.sort_values(by='date',ascending=False) + return df + + +def download_crypto_price(symbol, token, start_date, end_date, vs_currency='usd'): url = f"https://api.coingecko.com/api/v3/coins/{symbol}/market_chart" params = { 'vs_currency': vs_currency, @@ -102,12 +137,12 @@ def get_crypto_price(symbol, token, start_date, end_date, vs_currency='usd'): load_all_pool_related_data = True if load_all_pool_related_data: # getting pool fee/vol related data - years = [2022, 2023] + years = [2021, 2022, 2023] result_df = pd.DataFrame() for pool_info in lib_const.pool_info_list: pool_address = pool_info[0] - result_df = get_uniswap_v3_data_year(pool_address, years) + result_df = download_uniswap_v3_data_year(pool_address, years) file_name = lib_const.get_pool_filename(pool_address, token0=pool_info[1], token1=pool_info[2]) print("save data:",file_name ) result_df.to_csv(file_name, index=False) @@ -138,11 +173,11 @@ def get_crypto_price(symbol, token, start_date, end_date, vs_currency='usd'): for token in lib_const.price_token_list: token_name = token[0] token_ticker = token[1] - df_price = get_crypto_price(token_name, token_ticker , start_date, end_date) + df_price = download_crypto_price(token_name, token_ticker , start_date, end_date) print(f'get token {token_ticker} price in usd' ) df_price_btc = pd.DataFrame() if(token_ticker != 'BTC'): - df_price_btc = get_crypto_price(token_name, token_ticker , start_date, end_date, vs_currency='btc') + df_price_btc = download_crypto_price(token_name, token_ticker , start_date, end_date, vs_currency='btc') print(f'get token {token_ticker} price in btc' ) df = pd.concat([df, df_price, df_price_btc], ignore_index=True) diff --git a/lib_logic.py b/lib_logic.py index 1624ec7..9229911 100644 --- a/lib_logic.py +++ b/lib_logic.py @@ -2,7 +2,7 @@ import numpy as np import pandas as pd from datetime import datetime -import lib_const +import lib_data # Get impermanent loss, qty_change_token0, and token1. # code also works with array input @@ -69,9 +69,6 @@ def get_impermanent_loss_given_range(price_change, price_range_down): return imp_loss - - - def get_opposite_bin_limit_with_same_liquidity(price_change): return -price_change/(1+price_change) @@ -85,37 +82,17 @@ def get_liquidity_boost_given_range(prince_range, benchmark = -0.5): def get_ETHBTC_poolyield_daily(date_begin_yyyymmdd = "2009-01-01", date_end_yyyymmdd = "3000-01-01"): + pool_address = '0xcbcdf9626bc03e24f779434178a73a0b4bad62ed' - data_file_name = lib_const.get_pool_filename(pool_address) - df = pd.read_csv(data_file_name) - - date_begin = datetime.strptime(date_begin_yyyymmdd, '%Y-%m-%d') - date_end = datetime.strptime(date_end_yyyymmdd, '%Y-%m-%d') - - - # Convert 'date' column to YYYYMMDD format - df['date'] = pd.to_datetime(df['date'], unit='s') - df = df.sort_values(by='date',ascending=False) - - df = df[(df['date'] >= date_begin) & (df['date'] <= date_end)] + df= lib_data.get_uniswap_pool_data_csv(pool_address, date_begin_yyyymmdd, date_end_yyyymmdd) df['daily_fee_rate'] = df['feesUSD'] / df['tvlUSD'] return np.average(df['daily_fee_rate']) def get_pool_performance_statistic(pool_address, token0, token1, fee_bps, year = -1): - data_file_name = lib_const.get_pool_filename(pool_address) - # data_file_name = 'output/output_' + pool_address + '.csv' - df = pd.read_csv(data_file_name) - - # Convert 'date' column to YYYYMMDD format - df['date_int'] = df['date'] - df['date'] = pd.to_datetime(df['date'], unit='s') - df = df.sort_values(by='date',ascending=False) - - #df['date'] = pd.to_datetime(df['date'], unit='s').dt.strftime('%Y%m%d') - + df= lib_data.get_uniswap_pool_data_csv(pool_address) df['year'] = df['date'].dt.strftime('%Y') df['YYYYMM'] = df['date'].dt.strftime('%Y%m') @@ -144,8 +121,6 @@ def get_pool_performance_statistic(pool_address, token0, token1, fee_bps, year = total_pct_price_change = np.exp(total_log_price_change) -1 total_lp_yield = df['daily_fee_rate'].sum() - - df['accum_fee_rate_7d'] = df['daily_fee_rate'].rolling(window=7).sum() df['price_change_7d'] = df['token0Price'].pct_change(7) diff --git a/main.py b/main.py index 0f778b3..165c924 100644 --- a/main.py +++ b/main.py @@ -1,68 +1,60 @@ import pandas as pd import numpy as np -from datetime import datetime import matplotlib.pyplot as plt -import lib_logic as lib_liq -import lib_const +import lib_logic as lib_lgc +import lib_data print("If you don't have data downloadeded and put as CSV in output folder, please run lib_data.py first!") print("Set load_all_pool_related_data and load_price_related_data = True before run ") -def get_df_daily_fees(date_begin_yyyymmdd = "2009-01-01", date_end_yyyymmdd = "3000-01-01"): - - + +def get_df_daily_fees(date_begin = "2009-01-01", date_end = "3000-01-01"): + pool_address = '0xcbcdf9626bc03e24f779434178a73a0b4bad62ed' - - data_file_name = lib_const.get_pool_filename(pool_address) - df = pd.read_csv(data_file_name) - - date_begin = datetime.strptime(date_begin_yyyymmdd, "%Y-%m-%d") - date_end = datetime.strptime(date_end_yyyymmdd, "%Y-%m-%d") - - # Convert 'date' column to YYYY-MM-DD format - df['date'] = pd.to_datetime(df['date'], unit='s') - df = df.sort_values(by='date',ascending=False) - - df = df[(df['date'] >= date_begin) & (df['date'] <= date_end)] + df = lib_data.get_uniswap_pool_data_csv(pool_address,date_begin, date_end ) df['daily_fee_rate'] = df['feesUSD'] / df['tvlUSD'] df['date_i'] = df['date'] df.set_index('date_i', inplace=True) - return df[['date', 'feesUSD', 'tvlUSD', 'daily_fee_rate']] + return df[[ 'feesUSD', 'tvlUSD', 'daily_fee_rate']] # 'date', -def get_df_daily_price(data_start_yyyy_mm_dd = '2022-12-01'): +def get_df_daily_price(date_begin = '2022-12-01', date_end ="3000-01-01"): # Load the CSV file - df = pd.read_csv('output/price_data_all_token.csv') - date_start = datetime.strptime(data_start_yyyy_mm_dd, "%Y-%m-%d") - - df['date'] = pd.to_datetime(df['date']) - - # Filter rows related to ETH price in terms of BTC - df = df[(df['token'] == 'ETH') & (df['vs_currency'] == 'btc') & (df['date'] >= date_start)] - df["YYYYMM"] = df['date'].dt.strftime('%Y%m') + df = lib_data.get_crypto_price_data_csv(date_begin, date_end) - - df.set_index('date', inplace=True) + # Filter rows related to ETH price in terms of BTC + df = df[(df['token'] == 'ETH') & (df['vs_currency'] == 'btc') ] + df["YYYYMM"] = df.index.strftime('%Y%m') + + df = add_monthly_price_change(df) + df = df[~df.index.duplicated(keep='last')] # Remove duplicates by taking the last value for each date return df - -def get_df_comb_price_fee(df_price, df_fee): - - df = pd.merge(df_price, df_fee, left_index=True, right_index=True) - # Group by 'Token' and the month of the 'date' column +def add_monthly_price_change(df): + # Group by 'Token' and the month of the 'date' column grouped = df.groupby(['token', 'YYYYMM' ]) - + + if 'date' not in df.columns: + df['date'] = df.index + # Calculate the first date and close price of each month for each group df['month_begin_date'] = grouped['date'].transform('min') df['month_last_date'] = grouped['date'].transform('max') + + df['price_month_begin_date'] = grouped['price'].transform('first') df['price_month_end_date'] = grouped['price'].transform('last') df['Price_chg_vs_MM01'] = df['price'] / df['price_month_begin_date'] -1 - df.drop(df[ df['month_last_date'].dt.day < 21 ].index, inplace=True) # filter out months with data < 3 weeks + return df + +def get_df_comb_price_fee(df_price, df_fee): + df = pd.merge(df_price, df_fee, left_index=True, right_index=True) + # df = add_monthly_price_change(df) + # df.drop(df[ df['month_last_date'].dt.day < 21 ].index, inplace=True) # filter out months with data < 3 weeks return df def get_mon_performance_by_range(range_down, df, benchmark_down = -0.3): @@ -73,11 +65,11 @@ def get_mon_performance_by_range(range_down, df, benchmark_down = -0.3): lower_bound = range_down benchmark_lower = benchmark_down - upper_bound = lib_liq.get_opposite_bin_limit_with_same_liquidity(lower_bound) + upper_bound = lib_lgc.get_opposite_bin_limit_with_same_liquidity(lower_bound) ret_columns = ['YYYYMM', 'range_down', 'mon_total_price_chg', 'mon_total_fee_yield', 'coverage_rate', 'boost_factor', 'gross_return', 'imp_loss', 'net_return'] - boost_factor = lib_liq.get_liquidity_boost_given_range(prince_range=lower_bound, benchmark=benchmark_lower) + boost_factor = lib_lgc.get_liquidity_boost_given_range(prince_range=lower_bound, benchmark=benchmark_lower) result_mon = np.empty((len(df_mon_chg), len(ret_columns))) for mon_i in range(len(df_mon_chg)): @@ -94,7 +86,7 @@ def get_mon_performance_by_range(range_down, df, benchmark_down = -0.3): gross_return = mon_total_fee_yield*coverage_rate*boost_factor - imp_loss = lib_liq.get_impermanent_loss_given_range(mon_total_price_chg, lower_bound) + imp_loss = lib_lgc.get_impermanent_loss_given_range(mon_total_price_chg, lower_bound) net_return = (1+gross_return)*(1+imp_loss) -1 @@ -103,6 +95,25 @@ def get_mon_performance_by_range(range_down, df, benchmark_down = -0.3): df_mon_result = pd.DataFrame(data=result_mon, columns=ret_columns) return df_mon_result + +def get_full_range_performance(range_down, df, benchmark_range =-0.3): + array_range_rst = np.empty((len(range_down),4)) + for range_i in range(len(range_down)): + range_down_i = range_down[range_i] + df_i = get_mon_performance_by_range(range_down_i, df, benchmark_down=benchmark_range) + average_gross_return = df_i['gross_return'].median() #.mean() + average_imp_loss = df_i['imp_loss'].median() #mean() + average_net_return = df_i['net_return'].median() #.mean() + array_range_rst[range_i, :] = np.array([range_down_i, average_gross_return,average_imp_loss,average_net_return ]) + + ret_columns = ['range_limit_down', 'gross_fee_gain', 'imp_loss', 'net_gain'] + len(ret_columns) + df_result = pd.DataFrame(data=array_range_rst, columns=ret_columns) + + return df_result + + + def show_simulation_result(df_result, x_column, y_cols_name, main_y_col_name, y_annualise_factor = 12): # multiply with 12 convert from monthly to be yearly @@ -131,34 +142,16 @@ def show_simulation_result(df_result, x_column, y_cols_name, main_y_col_name, y_ - - -def get_full_range_performance(range_down, df, benchmark_range =-0.3): - array_range_rst = np.empty((len(range_down),4)) - for range_i in range(len(range_down)): - range_down_i = range_down[range_i] - df_i = get_mon_performance_by_range(range_down_i, df, benchmark_down=benchmark_range) - average_gross_return = df_i['gross_return'].median() #.mean() - average_imp_loss = df_i['imp_loss'].median() #mean() - average_net_return = df_i['net_return'].median() #.mean() - array_range_rst[range_i, :] = np.array([range_down_i, average_gross_return,average_imp_loss,average_net_return ]) - - ret_columns = ['range_limit_down', 'gross_fee_gain', 'imp_loss', 'net_gain'] - len(ret_columns) - df_result = pd.DataFrame(data=array_range_rst, columns=ret_columns) - - return df_result - - - def main (): - data_start_yyyy_mm_dd = '2022-12-01' + date_begin = '2022-12-01' + date_end = '2023-11-30' range_down = np.arange(-0.5, 0, 0.02) benchmark_range = -0.3 - df_price = get_df_daily_price(data_start_yyyy_mm_dd) - df_fee = get_df_daily_fees(date_begin_yyyymmdd = data_start_yyyy_mm_dd) + df_price = get_df_daily_price(date_begin,date_end=date_end) + print(df_price.head()) + df_fee = get_df_daily_fees(date_begin = date_begin, date_end=date_end) df = get_df_comb_price_fee(df_price, df_fee) print("\n check df") print(df.head()) diff --git a/main_avg_yld.py b/main_avg_yld.py new file mode 100644 index 0000000..2d21d62 --- /dev/null +++ b/main_avg_yld.py @@ -0,0 +1,98 @@ +import numpy as np +import lib_logic as lib_lgc +import main as m + +def create_lp_by_pos_by_lin_interp(range_pos_price_down, range_pos_lp_qty, step = -0.01): + + start = max(range_pos_price_down) + stop = min(range_pos_price_down) + + + range_down = np.arange(start, stop + step, step) + range_down_lp_qty = np.empty(len(range_down)) + + for i in range(1,len(range_pos_price_down)): + pos_begin = int(range_pos_price_down[i-1] / step) -1 + pos_end = int(range_pos_price_down[i] / step) -1 + + # value + value_start = range_pos_lp_qty[i-1] + value_end = range_pos_lp_qty[i] + value_step = (value_end -value_start ) /(pos_end -pos_begin ) + rst = np.arange(value_start, value_end+value_step/2, value_step) + + range_down_lp_qty[pos_begin:(pos_end+1)] =rst + + return range_down, range_down_lp_qty + +def get_all_range_coverage_rate_monthly(range_down, df): + + df_price_chg = df[['Price_chg_vs_MM01' ]][df['month_begin_date'] != df['date']] + total_sample_count = len(df_price_chg) + rst = np.empty(len(range_down)) + for i in range(0, len(range_down)): + range_d = range_down[i] + range_u = lib_lgc.get_opposite_bin_limit_with_same_liquidity(range_d) + + count_within_range =df_price_chg['Price_chg_vs_MM01'].between(range_d, range_u).sum() + coverage_rate= count_within_range / total_sample_count + rst[i] = coverage_rate + + return rst + + +# these are inflection points of liqudity, the value must be multiples of -0.01 (or -1%). +range_pos_price_down = np.array([-0.01, -0.05, -0.2]) +range_pos_lp_qty = np.array([2, 0.66, 0.29]) + +range_down, range_down_lp_qty = create_lp_by_pos_by_lin_interp(range_pos_price_down, range_pos_lp_qty) + +df = m.get_df_daily_price(date_begin = '2022-12-01', date_end ="2023-11-30") +coverage_rate = get_all_range_coverage_rate_monthly(range_down, df) + + +w_tvl = range_down_lp_qty / range_down_lp_qty.sum() +w_tvl_cvg = w_tvl*coverage_rate +w_tvl_cvg = w_tvl_cvg/ w_tvl_cvg.sum() # normalisation + + +avg_d_tvl_only = 1/sum(w_tvl / range_down) +avg_d_tvl_coverage = 1/ sum(w_tvl_cvg / range_down) +print("Range down limit to get market average yield: ", avg_d_tvl_coverage) +print("Range down limit to get market average yield when ignore rang e cross effect (not accurate): ", avg_d_tvl_only) + + +import matplotlib.pyplot as plt + +x = np.concatenate((np.flip(range_down), -1*range_down)) +y = np.concatenate((np.flip(range_down_lp_qty), range_down_lp_qty)) + +print("show range asset qty. ") +plt.plot(x, y, marker='o', linestyle='-') +plt.title('Range (bin) Asset amount vs range_down value') +plt.xlabel('range_down') +plt.ylabel('Range Asset amount') +plt.ylim(0, 3) +plt.grid(True) +plt.show() + +print("show coverage rate. ") +plt.plot(range_down, coverage_rate, marker='o', linestyle='-') +plt.title('coverage_rate vs range_down value') +plt.xlabel('range_down') +plt.ylabel('coverage_rate') + +plt.grid(True) +plt.show() + +print("show range weight. ") +plt.plot(range_down, w_tvl_cvg, label='weight w coverage', marker='o', linestyle='-') +plt.plot(range_down, w_tvl, label='weight w/o coverage', marker='s', linestyle='--') + +plt.title('Comparison of weight with/without coverage') +plt.xlabel('range down limit') +plt.ylabel('normalised weight') + +plt.legend() +plt.grid(True) +plt.show() \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index e6009ce..a97803e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,5 +1,34 @@ # This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +[[package]] +name = "appnope" +version = "0.1.3" +description = "Disable App Nap on macOS >= 10.9" +optional = false +python-versions = "*" +files = [ + {file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"}, + {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"}, +] + 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