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545 strategy backtests add financial metrics #548

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merged 15 commits into from
Nov 21, 2024
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545 strategy backtests add financial metrics #548

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90168ca
Added block_time fetching for more accurate outcome tokens received.
gabrielfior Nov 5, 2024
2e0ac96
Financial metrics being published
gabrielfior Nov 7, 2024
ed67ff4
Reverted caching to diskcache
gabrielfior Nov 7, 2024
0535a86
Fixing CI
gabrielfior Nov 7, 2024
56b6141
Merge remote-tracking branch 'refs/remotes/origin/main' into 545-stra…
gabrielfior Nov 7, 2024
05c7142
lock file
gabrielfior Nov 7, 2024
e6f2349
Updated network foundry
gabrielfior Nov 7, 2024
3c4ba38
Updated RPC to gateway
gabrielfior Nov 7, 2024
b8cbc51
Moved local_chain test to proper folder
gabrielfior Nov 7, 2024
f0511ff
Fixed black
gabrielfior Nov 7, 2024
d9a1466
Applied PR comments
gabrielfior Nov 11, 2024
66b4331
Small fixes after PR review
gabrielfior Nov 13, 2024
77815e7
Small refactoring to address PR comments
gabrielfior Nov 14, 2024
06aae1d
Added gnosis_rpc_config, google_credentials_filename and chain_id to …
gabrielfior Nov 19, 2024
93d3ebe
Merge remote-tracking branch 'refs/remotes/origin/main' into 545-stra…
gabrielfior Nov 19, 2024
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206 changes: 126 additions & 80 deletions examples/monitor/match_bets_with_langfuse_traces.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@
from typing import Any

import pandas as pd
from eth_typing import HexAddress, HexStr
from langfuse import Langfuse
from pydantic import BaseModel

Expand All @@ -10,25 +11,37 @@
BettingStrategy,
GuaranteedLossError,
KellyBettingStrategy,
MaxAccuracyBettingStrategy,
MaxAccuracyWithKellyScaledBetsStrategy,
MaxExpectedValueBettingStrategy,
ProbabilisticAnswer,
TradeType,
)
from prediction_market_agent_tooling.markets.data_models import ResolvedBet
from prediction_market_agent_tooling.markets.data_models import (
ResolvedBet,
SimulationDetail,
)
from prediction_market_agent_tooling.markets.omen.omen import OmenAgentMarket
from prediction_market_agent_tooling.markets.omen.omen_contracts import (
OmenConditionalTokenContract,
)
from prediction_market_agent_tooling.markets.omen.omen_subgraph_handler import (
OmenSubgraphHandler,
)
from prediction_market_agent_tooling.monitor.financial_metrics.financial_metrics import (
SharpeRatioCalculator,
)
from prediction_market_agent_tooling.tools.google_utils import (
get_private_key_from_gcp_secret,
)
from prediction_market_agent_tooling.tools.httpx_cached_client import HttpxCachedClient
from prediction_market_agent_tooling.tools.langfuse_client_utils import (
ProcessMarketTrace,
ResolvedBetWithTrace,
get_trace_for_bet,
get_traces_for_agent,
)
from prediction_market_agent_tooling.tools.utils import (
get_private_key_from_gcp_secret,
utc_datetime,
from prediction_market_agent_tooling.tools.transaction_cache import (
TransactionBlockCache,
)
from prediction_market_agent_tooling.tools.utils import utc_datetime


class SimulatedOutcome(BaseModel):
Expand All @@ -47,6 +60,8 @@ def get_outcome_for_trace(
strategy: BettingStrategy,
trace: ProcessMarketTrace,
market_outcome: bool,
actual_placed_bet: ResolvedBet,
tx_block_cache: TransactionBlockCache,
) -> SimulatedOutcome | None:
market = trace.market
answer = trace.answer
Expand All @@ -72,18 +87,25 @@ def get_outcome_for_trace(
trades[0].trade_type == TradeType.BUY
), "Can only buy without previous position."
buy_trade = trades[0]

received_outcome_tokens = market.get_buy_token_amount(
bet_amount=market.get_bet_amount(buy_trade.amount.amount),
direction=buy_trade.outcome,
).amount

correct = buy_trade.outcome == market_outcome
profit = (
received_outcome_tokens - buy_trade.amount.amount
if correct
else -buy_trade.amount.amount
)
# If not correct, stop early because profit is known.
if not correct:
profit = -buy_trade.amount.amount
else:
# We use a historical state (by passing in a block_number as arg) to get the correct outcome token balances.
tx_block_number = tx_block_cache.get_block_number(actual_placed_bet.id)
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Oooh, so if we used Market at state of latest block, get_buy_token_amount produced rubbish? 😱

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get_buy_token_amount is block-dependent, since the price of the token is dictated by the size of the yes_outcome_pool_size and no_outcome_pool_size

market_at_block = OmenSubgraphHandler().get_omen_market_by_market_id(
HexAddress(HexStr(market.id)), block_number=tx_block_number
)
omen_agent_market_at_block = OmenAgentMarket.from_data_model(market_at_block)

received_outcome_tokens = omen_agent_market_at_block.get_buy_token_amount(
bet_amount=omen_agent_market_at_block.get_bet_amount(
buy_trade.amount.amount
),
direction=buy_trade.outcome,
).amount
profit = received_outcome_tokens - buy_trade.amount.amount
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return SimulatedOutcome(
size=buy_trade.amount.amount,
Expand All @@ -100,14 +122,14 @@ def get_outcome_for_trace(
# Get the private keys for the agents from GCP Secret Manager
agent_gcp_secret_map = {
"DeployablePredictionProphetGPT4TurboFinalAgent": "pma-prophetgpt4turbo-final",
"DeployablePredictionProphetGPT4TurboPreviewAgent": "pma-prophetgpt4",
"DeployablePredictionProphetGPT4oAgent": "pma-prophetgpt3",
"DeployablePredictionProphetGPTo1PreviewAgent": "pma-prophet-o1-preview",
"DeployablePredictionProphetGPTo1MiniAgent": "pma-prophet-o1-mini",
"DeployableOlasEmbeddingOAAgent": "pma-evo-olas-embeddingoa",
"DeployableThinkThoroughlyAgent": "pma-think-thoroughly",
"DeployableThinkThoroughlyProphetResearchAgent": "pma-think-thoroughly-prophet-research",
"DeployableKnownOutcomeAgent": "pma-knownoutcome",
# "DeployablePredictionProphetGPT4TurboPreviewAgent": "pma-prophetgpt4",
# "DeployablePredictionProphetGPT4oAgent": "pma-prophetgpt3",
# "DeployablePredictionProphetGPTo1PreviewAgent": "pma-prophet-o1-preview",
# "DeployablePredictionProphetGPTo1MiniAgent": "pma-prophet-o1-mini",
# "DeployableOlasEmbeddingOAAgent": "pma-evo-olas-embeddingoa",
# "DeployableThinkThoroughlyAgent": "pma-think-thoroughly",
# "DeployableThinkThoroughlyProphetResearchAgent": "pma-think-thoroughly-prophet-research",
# "DeployableKnownOutcomeAgent": "pma-knownoutcome",
}

agent_pkey_map = {
Expand All @@ -116,44 +138,44 @@ def get_outcome_for_trace(

# Define strategies we want to test out
strategies = [
MaxAccuracyBettingStrategy(bet_amount=1),
MaxAccuracyBettingStrategy(bet_amount=2),
MaxAccuracyBettingStrategy(bet_amount=25),
KellyBettingStrategy(max_bet_amount=1),
KellyBettingStrategy(max_bet_amount=2),
# MaxAccuracyBettingStrategy(bet_amount=1),
# MaxAccuracyBettingStrategy(bet_amount=2),
# MaxAccuracyBettingStrategy(bet_amount=25),
# KellyBettingStrategy(max_bet_amount=1),
# KellyBettingStrategy(max_bet_amount=2),
KellyBettingStrategy(max_bet_amount=5),
KellyBettingStrategy(max_bet_amount=25),
MaxAccuracyWithKellyScaledBetsStrategy(max_bet_amount=1),
MaxAccuracyWithKellyScaledBetsStrategy(max_bet_amount=2),
MaxAccuracyWithKellyScaledBetsStrategy(max_bet_amount=25),
MaxExpectedValueBettingStrategy(bet_amount=1),
MaxExpectedValueBettingStrategy(bet_amount=2),
MaxExpectedValueBettingStrategy(bet_amount=5),
MaxExpectedValueBettingStrategy(bet_amount=25),
KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.01),
KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.05),
KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.1),
KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.15),
KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.2),
KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.25),
KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.3),
KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.4),
KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.5),
KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.6),
KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.7),
KellyBettingStrategy(max_bet_amount=5, max_price_impact=0.1),
KellyBettingStrategy(max_bet_amount=5, max_price_impact=0.15),
KellyBettingStrategy(max_bet_amount=5, max_price_impact=0.2),
KellyBettingStrategy(max_bet_amount=5, max_price_impact=0.3),
KellyBettingStrategy(max_bet_amount=5, max_price_impact=0.4),
KellyBettingStrategy(max_bet_amount=5, max_price_impact=0.5),
KellyBettingStrategy(max_bet_amount=5, max_price_impact=0.6),
KellyBettingStrategy(max_bet_amount=5, max_price_impact=0.7),
# KellyBettingStrategy(max_bet_amount=25),
# MaxAccuracyWithKellyScaledBetsStrategy(max_bet_amount=1),
# MaxAccuracyWithKellyScaledBetsStrategy(max_bet_amount=2),
# MaxAccuracyWithKellyScaledBetsStrategy(max_bet_amount=25),
# MaxExpectedValueBettingStrategy(bet_amount=1),
# MaxExpectedValueBettingStrategy(bet_amount=2),
# MaxExpectedValueBettingStrategy(bet_amount=5),
# MaxExpectedValueBettingStrategy(bet_amount=25),
# KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.01),
# KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.05),
# KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.1),
# KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.15),
# KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.2),
# KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.25),
# KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.3),
# KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.4),
# KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.5),
# KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.6),
# KellyBettingStrategy(max_bet_amount=2, max_price_impact=0.7),
# KellyBettingStrategy(max_bet_amount=5, max_price_impact=0.1),
# KellyBettingStrategy(max_bet_amount=5, max_price_impact=0.15),
# KellyBettingStrategy(max_bet_amount=5, max_price_impact=0.2),
# KellyBettingStrategy(max_bet_amount=5, max_price_impact=0.3),
# KellyBettingStrategy(max_bet_amount=5, max_price_impact=0.4),
# KellyBettingStrategy(max_bet_amount=5, max_price_impact=0.5),
# KellyBettingStrategy(max_bet_amount=5, max_price_impact=0.6),
# KellyBettingStrategy(max_bet_amount=5, max_price_impact=0.7),
KellyBettingStrategy(max_bet_amount=25, max_price_impact=0.1),
KellyBettingStrategy(max_bet_amount=25, max_price_impact=0.2),
KellyBettingStrategy(max_bet_amount=25, max_price_impact=0.3),
KellyBettingStrategy(max_bet_amount=25, max_price_impact=0.5),
KellyBettingStrategy(max_bet_amount=25, max_price_impact=0.7),
# KellyBettingStrategy(max_bet_amount=25, max_price_impact=0.2),
# KellyBettingStrategy(max_bet_amount=25, max_price_impact=0.3),
# KellyBettingStrategy(max_bet_amount=25, max_price_impact=0.5),
# KellyBettingStrategy(max_bet_amount=25, max_price_impact=0.7),
]

httpx_client = HttpxCachedClient().get_client()
Expand All @@ -165,6 +187,11 @@ def get_outcome_for_trace(
strat_mse_profits[repr(strategy)] = MSEProfit(p_yes_mse=[], total_profit=[])

print("# Agent Bet vs Simulated Bet Comparison")

tx_block_cache = TransactionBlockCache(
web3=OmenConditionalTokenContract().get_web3()
)

for agent_name, private_key in agent_pkey_map.items():
print(f"\n## {agent_name}\n")
api_keys = APIKeys(BET_FROM_PRIVATE_KEY=private_key)
Expand Down Expand Up @@ -211,51 +238,68 @@ def get_outcome_for_trace(
continue

if len(bets_with_traces) != len(bets):
pct_bets_without_traces = (len(bets) - len(bets_with_traces)) / len(bets)
print(
f"{len(bets) - len(bets_with_traces)} bets do not have a corresponding trace, ignoring them."
f"{len(bets) - len(bets_with_traces)} bets do not have a corresponding trace ({pct_bets_without_traces * 100:.2f}%), ignoring them."
)

simulations: list[dict[str, Any]] = []
details = []
details: list[SimulationDetail] = []

for strategy_idx, strategy in enumerate(strategies):
# "Born" agent with initial funding, simulate as if he was doing bets one by one.
starting_balance = 50.0
agent_balance = starting_balance
simulated_outcomes: list[SimulatedOutcome] = []

# ToDo - Can we add the value of tokens that weren't redeemed yet?
# Like a portfolio tracking.
for bet_with_trace in bets_with_traces:
bet = bet_with_trace.bet
trace = bet_with_trace.trace
simulated_outcome = get_outcome_for_trace(
strategy=strategy, trace=trace, market_outcome=bet.market_outcome
strategy=strategy,
trace=trace,
market_outcome=bet.market_outcome,
actual_placed_bet=bet,
tx_block_cache=tx_block_cache,
)
if simulated_outcome is None:
continue

simulated_outcomes.append(simulated_outcome)
agent_balance += simulated_outcome.profit

details.append(
{
"url": trace.market.url,
"market_p_yes": round(trace.market.current_p_yes, 4),
"agent_p_yes": round(trace.answer.p_yes, 4),
"agent_conf": round(trace.answer.confidence, 4),
"org_bet": round(bet.amount.amount, 4),
"sim_bet": round(simulated_outcome.size, 4),
"org_dir": bet.outcome,
"sim_dir": simulated_outcome.direction,
"org_profit": round(bet.profit.amount, 4),
"sim_profit": round(simulated_outcome.profit, 4),
}
simulation_detail = SimulationDetail(
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strategy=repr(strategy),
url=trace.market.url,
market_p_yes=round(trace.market.current_p_yes, 4),
agent_p_yes=round(trace.answer.p_yes, 4),
agent_conf=round(trace.answer.confidence, 4),
org_bet=round(bet.amount.amount, 4),
sim_bet=round(simulated_outcome.size, 4),
org_dir=bet.outcome,
sim_dir=simulated_outcome.direction,
org_profit=round(bet.profit.amount, 4),
sim_profit=round(simulated_outcome.profit, 4),
timestamp=bet_with_trace.trace.timestamp_datetime,
)
details.append(simulation_detail)

details.sort(key=lambda x: x["sim_profit"], reverse=True)
pd.DataFrame.from_records(details).to_csv(
details.sort(key=lambda x: x.sim_profit, reverse=True)
details_df = pd.DataFrame.from_records([d.model_dump() for d in details])
details_df.to_csv(
output_directory / f"{agent_name} - {strategy} - all bets.csv",
index=False,
)

# Financial analysis
calc = SharpeRatioCalculator(details=details)
sharpe_output_simulation = calc.calculate_annual_sharpe_ratio()
sharpe_output_original = calc.calculate_annual_sharpe_ratio(
profit_col_name="org_profit"
)

sum_squared_errors = 0.0
for bet_with_trace in bets_with_traces:
bet = bet_with_trace.bet
Expand Down Expand Up @@ -283,6 +327,7 @@ def get_outcome_for_trace(
# We don't know these for the original run.
"start_balance": None,
"end_balance": None,
**sharpe_output_original.model_dump(),
}
)
else:
Expand All @@ -300,6 +345,7 @@ def get_outcome_for_trace(
"p_yes mse": p_yes_mse,
"start_balance": starting_balance,
"end_balance": agent_balance,
**sharpe_output_simulation.model_dump(),
}
)

Expand Down
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