Choosing a prop trading firm is a decision that should be based on data, not marketing. Let's build a scoring framework in Python that objectively evaluates and ranks prop firms based on measurable criteria.
The Evaluation Criteria
After analyzing dozens of firms, these are the metrics that matter most:
from dataclasses import dataclass
from typing import Optional
import json
@dataclass
class PropFirmProfile:
name: str
# Cost
eval_price_50k: float # Price for $50K evaluation
monthly_fee: float # Recurring monthly fee
reset_fee: float # Fee to reset after failure
# Rules
profit_target_pct: float # Phase 1 profit target %
max_daily_dd_pct: float # Max daily drawdown %
max_total_dd_pct: float # Max trailing/total drawdown %
drawdown_type: str # "eod_trailing", "realtime_trailing", "static"
min_trading_days: int # Minimum days to pass
# Payout
profit_split_pct: float # Your share of profits
payout_frequency: str # "weekly", "biweekly", "monthly"
first_payout_days: int # Days until first payout eligible
# Platform
platforms: list[str]
instruments: list[str]
# Trust
years_operating: float
trustpilot_rating: float
payout_proof_count: int # Verified payout proofs
class PropFirmScorer:
"""Weighted scoring system for prop firm evaluation."""
WEIGHTS = {
"cost": 0.20,
"rules": 0.25,
"payout": 0.25,
"platform": 0.10,
"trust": 0.20,
}
def __init__(self, firms: list[PropFirmProfile]):
self.firms = firms
def _normalize(self, values: list[float], invert: bool = False) -> list[float]:
min_v, max_v = min(values), max(values)
if max_v == min_v:
return [0.5] * len(values)
normalized = [(v - min_v) / (max_v - min_v) for v in values]
return [1 - n for n in normalized] if invert else normalized
def score_cost(self) -> list[float]:
# Lower cost = better score
total_costs = [f.eval_price_50k + f.monthly_fee * 3 for f in self.firms]
return self._normalize(total_costs, invert=True)
def score_rules(self) -> list[float]:
scores = []
for f in self.firms:
s = 0
# Higher drawdown allowance = better
s += f.max_total_dd_pct / 12 * 30
s += f.max_daily_dd_pct / 5 * 20
# EOD trailing is more forgiving
s += {"eod_trailing": 25, "static": 30, "realtime_trailing": 10}[f.drawdown_type]
# Lower profit target = easier
s += (15 - f.profit_target_pct) * 2
# Fewer min days = more flexible
s += max(0, 10 - f.min_trading_days) * 2
scores.append(s)
return self._normalize(scores)
def score_payout(self) -> list[float]:
scores = []
for f in self.firms:
s = f.profit_split_pct # Direct percentage
s += {"weekly": 15, "biweekly": 10, "monthly": 5}[f.payout_frequency]
s -= f.first_payout_days * 0.3
scores.append(s)
return self._normalize(scores)
def score_platform(self) -> list[float]:
scores = [len(f.platforms) * 10 + len(f.instruments) * 5 for f in self.firms]
return self._normalize(scores)
def score_trust(self) -> list[float]:
scores = []
for f in self.firms:
s = f.trustpilot_rating * 20
s += min(f.years_operating * 10, 50)
s += min(f.payout_proof_count * 0.1, 30)
scores.append(s)
return self._normalize(scores)
def rank(self) -> list[dict]:
cost_scores = self.score_cost()
rules_scores = self.score_rules()
payout_scores = self.score_payout()
platform_scores = self.score_platform()
trust_scores = self.score_trust()
results = []
for i, firm in enumerate(self.firms):
total = (
cost_scores[i] * self.WEIGHTS["cost"] +
rules_scores[i] * self.WEIGHTS["rules"] +
payout_scores[i] * self.WEIGHTS["payout"] +
platform_scores[i] * self.WEIGHTS["platform"] +
trust_scores[i] * self.WEIGHTS["trust"]
)
results.append({
"name": firm.name,
"total_score": round(total * 100, 1),
"cost": round(cost_scores[i] * 100, 1),
"rules": round(rules_scores[i] * 100, 1),
"payout": round(payout_scores[i] * 100, 1),
"platform": round(platform_scores[i] * 100, 1),
"trust": round(trust_scores[i] * 100, 1),
})
return sorted(results, key=lambda x: x["total_score"], reverse=True)
Sample Comparison
firms = [
PropFirmProfile(
name="Firm A", eval_price_50k=299, monthly_fee=0, reset_fee=99,
profit_target_pct=8, max_daily_dd_pct=3, max_total_dd_pct=6,
drawdown_type="eod_trailing", min_trading_days=1,
profit_split_pct=90, payout_frequency="weekly", first_payout_days=14,
platforms=["NinjaTrader", "Tradovate", "TradingView"],
instruments=["ES", "NQ", "YM", "RTY", "CL", "GC"],
years_operating=3.0, trustpilot_rating=4.5, payout_proof_count=500
),
PropFirmProfile(
name="Firm B", eval_price_50k=165, monthly_fee=0, reset_fee=80,
profit_target_pct=6, max_daily_dd_pct=2, max_total_dd_pct=4,
drawdown_type="realtime_trailing", min_trading_days=3,
profit_split_pct=80, payout_frequency="biweekly", first_payout_days=30,
platforms=["NinjaTrader", "Tradovate"],
instruments=["ES", "NQ", "CL"],
years_operating=5.0, trustpilot_rating=4.2, payout_proof_count=1200
),
PropFirmProfile(
name="Firm C", eval_price_50k=375, monthly_fee=0, reset_fee=150,
profit_target_pct=10, max_daily_dd_pct=4, max_total_dd_pct=8,
drawdown_type="static", min_trading_days=0,
profit_split_pct=95, payout_frequency="weekly", first_payout_days=7,
platforms=["NinjaTrader", "Tradovate", "Rithmic", "TradingView"],
instruments=["ES", "NQ", "YM", "RTY", "CL", "GC", "ZB", "ZN"],
years_operating=2.0, trustpilot_rating=4.7, payout_proof_count=300
),
]
scorer = PropFirmScorer(firms)
rankings = scorer.rank()
print(f"{'Rank':<5} {'Firm':<10} {'Score':<8} {'Cost':<8} {'Rules':<8} {'Payout':<8} {'Trust':<8}")
print("-" * 55)
for i, r in enumerate(rankings):
print(f"{i+1:<5} {r['name']:<10} {r['total_score']:<8} {r['cost']:<8} {r['rules']:<8} {r['payout']:<8} {r['trust']:<8}")
Making It Your Own
The weights are customizable. If cost matters most to you, increase that weight. If you're experienced and want maximum payouts, weight payout higher.
# Custom weights for different trader profiles
CONSERVATIVE = {"cost": 0.10, "rules": 0.35, "payout": 0.15, "platform": 0.10, "trust": 0.30}
AGGRESSIVE = {"cost": 0.15, "rules": 0.15, "payout": 0.40, "platform": 0.10, "trust": 0.20}
BUDGET = {"cost": 0.40, "rules": 0.20, "payout": 0.15, "platform": 0.05, "trust": 0.20}
Conclusion
Don't choose a prop firm based on social media hype. Run the numbers. This framework gives you a systematic way to evaluate any firm on objective criteria.
For real-world data to feed into this model, PropFirmKey maintains up-to-date comparisons of futures prop firms including Alpha Futures, complete with pricing, rules, and user reviews that map directly to our scoring criteria.
Fork this code and customize it for your needs!
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