Predictoor 5X Boost (#824)

df_py/util/constants.py

@@ -44,6 +44,7 @@
 PREDICTOOR_RELEASE_WEEK = 62
 PREDICTOOR_OCEAN_BUDGET = 37_500
 PREDICTOOR_DF_FIRST_DATE = datetime(2023, 11, 9)
+SAPPHIRE_MAINNET_CHAINID = 23294
 
 # volume
 # Weekly Percent Yield needs to be 1.5717%., for max APY of 125%

df_py/util/dcv_multiplier.py

@@ -34,7 +34,9 @@ def calc_dcv_multiplier(DF_week: int, is_predictoor: bool) -> float:
       DCV_multiplier --
     """
     if is_predictoor:
-        return PREDICTOOR_MULTIPLIER
+        if DF_week < 82:
+            return PREDICTOOR_MULTIPLIER
+        return PREDICTOOR_MULTIPLIER * 5
 
     if DF_week < 9:
         return np.inf

df_py/util/dftool_module.py

@@ -24,6 +24,7 @@
 from df_py.util import blockrange, dispense, get_rate, networkutil, oceantestutil
 from df_py.util.base18 import from_wei, to_wei
 from df_py.util.blocktime import get_fin_block, timestr_to_timestamp
+from df_py.util.constants import SAPPHIRE_MAINNET_CHAINID
 from df_py.util.contract_base import ContractBase
 from df_py.util.dftool_arguments import (
     CHAINID_EXAMPLES,
@@ -437,12 +438,10 @@ def do_calc():
         calc_volume_rewards_from_csvs(csv_dir, start_date, tot_ocean)
 
     if arguments.SUBSTREAM == "predictoor_rose":
-        SAPPHIRE_MAINNET_ID = 23294
-
         predictoor_data = load_predictoor_data_csv(csv_dir)
         print("Loaded predictoor data:", predictoor_data)
         predictoor_rewards = calc_predictoor_rewards(
-            predictoor_data, arguments.TOT_OCEAN, SAPPHIRE_MAINNET_ID
+            predictoor_data, arguments.TOT_OCEAN, SAPPHIRE_MAINNET_CHAINID
         )
         print("Calculated rewards:", predictoor_rewards)
 

df_py/util/dispense.py

@@ -7,6 +7,7 @@
 from web3.main import Web3
 
 from df_py.util.base18 import to_wei
+from df_py.util.constants import SAPPHIRE_MAINNET_CHAINID
 from df_py.util.contract_base import ContractBase
 from df_py.util.logger import logger
 from df_py.util.multisig import send_multisig_tx
@@ -63,7 +64,7 @@ def dispense(
     sts = list(range(N))[::batch_size]  # send in batches to avoid gas issues
 
     LEGACY_TX = False
-    if web3.eth.chain_id == 23294:
+    if web3.eth.chain_id == SAPPHIRE_MAINNET_CHAINID:
         LEGACY_TX = True
 
     def approveAmt(amt):

df_py/util/test/test_dcv_multiplier.py

@@ -71,7 +71,10 @@ def test_calc_dcv_multiplier():
     assert mult(100, False) == 0.001
     assert mult(10000, False) == 0.001
 
+    # PREDICTOOR MULTIPLIERS
     assert mult(-10, True) == 0.201
     assert mult(9, True) == 0.201
     assert mult(12, True) == 0.201
-    assert mult(10000, True) == 0.201
+    assert mult(81, True) == 0.201
+    assert mult(82, True) == 0.201 * 5
+    assert mult(10000, True) == 0.201 * 5

df_py/volume/test/test_calc_rewards.py

@@ -1,12 +1,16 @@
 # pylint: disable=too-many-lines
+from datetime import datetime
 from unittest.mock import MagicMock, patch
 
 import numpy as np
 import pytest
 from enforce_typing import enforce_types
 from pytest import approx
 
-from df_py.util.constants import ZERO_ADDRESS
+from df_py.util.constants import (
+    SAPPHIRE_MAINNET_CHAINID,
+    ZERO_ADDRESS,
+)
 from df_py.volume import csvs
 from df_py.volume.calc_rewards import calc_volume_rewards_from_csvs
 from df_py.volume.reward_calculator import TARGET_WPY, RewardCalculator
@@ -752,3 +756,76 @@ def mock_multipliers(DF_week, is_predictoor):  # pylint: disable=unused-argument
         assert rewards_info[C1][NA][LP1] == approx(60.3, abs=1e-5)
         assert rewards_info[C2][NB][LP2] == 300
         assert rewards_info[C2][NB][LP3] == 300
+
+
+@enforce_types
+def test_volume_reward_calculator_pdr_mult_week81(tmp_path):
+    expected_rewards = 20.1
+    _test_volume_reward_calculator_pdr_mult(
+        tmp_path, DF_week=81, expected_rewards=expected_rewards
+    )
+
+
+@enforce_types
+def test_volume_reward_calculator_pdr_mult_week82(tmp_path):
+    expected_rewards = 20.1 * 5
+    _test_volume_reward_calculator_pdr_mult(
+        tmp_path, DF_week=82, expected_rewards=expected_rewards
+    )
+
+
+@enforce_types
+def _test_volume_reward_calculator_pdr_mult(tmp_path, DF_week, expected_rewards):
+    chain_id = SAPPHIRE_MAINNET_CHAINID
+    stakes = {
+        chain_id: {NA: {LP1: 1e8}},
+    }
+    locked_amts = {
+        chain_id: {NA: {LP1: 1e8}},
+    }
+    volumes = {
+        chain_id: {
+            OCN_ADDR: {
+                NA: 100,
+            }
+        },
+    }
+    owners = {chain_id: {NA: LP2}}
+    symbols = {chain_id: {OCN_ADDR: OCN_SYMB}}
+    rates = {OCN_SYMB: 1.0}
+
+    predictoor_contracts = {NA: {}, NB: {}}
+
+    OCEAN_reward = 1e24
+
+    with patch(
+        "df_py.volume.allocations.load_stakes",
+        return_value=(stakes, locked_amts),
+    ), patch("df_py.volume.csvs.load_nftvols_csvs", return_value=volumes), patch(
+        "df_py.volume.csvs.load_owners_csvs", return_value=owners
+    ), patch(
+        "df_py.volume.csvs.load_symbols_csvs", return_value=symbols
+    ), patch(
+        "df_py.volume.csvs.load_rate_csvs", return_value=rates
+    ), patch(
+        "df_py.volume.reward_calculator.query_predictoor_contracts",
+        return_value=predictoor_contracts,
+    ), patch(
+        "df_py.volume.reward_calculator.DEPLOYER_ADDRS",
+        {chain_id: ""},
+    ), patch(
+        "df_py.volume.calc_rewards.get_df_week_number", return_value=DF_week
+    ), patch(
+        "df_py.volume.calc_rewards.wait_to_latest_block"
+    ), patch(
+        "web3.main.Web3.to_checksum_address"
+    ) as mock:
+        mock.side_effect = lambda value: value
+
+        calc_volume_rewards_from_csvs(
+            tmp_path, datetime.now(), OCEAN_reward, True, False
+        )
+
+        rewards_per_lp = csvs.load_volume_rewards_csv(str(tmp_path))
+
+        assert rewards_per_lp[chain_id][LP1] == approx(expected_rewards, 1e-6)