A junior quant once told me he spent $400 on premium futures data before realizing his strategy had negative expected value after commissions.
He had optimized the wrong variable.
The math is brutal but clarifying: for traders running strategies with small capital bases—let's say $10,000 to $50,000—the data source decision is not about finding the best data. It is about finding the data that generates the highest signal-to-cost ratio at your specific scale. The best financial data in the world is a terrible investment if it consumes your entire edge before execution costs.
This article builds a cost allocation framework from first principles, quantifies the real consumption of common data sources including TickDB, and provides a reproducible budgeting model you can adapt to your own capital and strategy parameters.
The Fundamental Budget Constraint
Before comparing vendors, establish the hard constraint. Most retail quant traders working with sub-$50,000 accounts operate under a practical annual budget ceiling of roughly 12% of their capital allocation for data and infrastructure. At $100/month, you are already at that ceiling on a $10,000 account.
That budget must cover:
- Market data subscriptions — real-time and/or historical
- Compute infrastructure — cloud servers, VPS, or local hardware
- Execution costs — commissions, spreads, and slippage (data-dependent)
- Optional buffers — storage, backups, monitoring tooling
The critical insight is that these categories interact. A more expensive data source with higher quality may reduce execution costs through better signal quality. A cheaper data source may require more compute to compensate for noise. Your budget allocation is a system, not a list.
Building a Cost Model: The Four-Phase Framework
Phase 1: Baseline Capital and Strategy Parameters
Define your starting conditions before evaluating any data source. This is the most skipped step in budget planning, and it causes the most waste.
# cost_model.py — Small-capital quant budget allocation model
class QuantBudget:
"""
Reproducible budget allocation framework for retail quant traders.
Replace the parameters below with your actual values.
"""
def __init__(self, capital_usd: float, monthly_budget_usd: float,
strategy_type: str, trades_per_day: int,
avg_trade_size_usd: float, asset_class: str):
self.capital = capital_usd
self.budget = monthly_budget_usd
self.strategy_type = strategy_type # 'mean_reversion', 'momentum', 'arbitrage'
self.trades_per_day = trades_per_day
self.avg_trade_size = avg_trade_size_usd
self.asset_class = asset_class
# Derived values
self.annual_budget = monthly_budget_usd * 12
self.budget_as_pct_capital = (monthly_budget_usd * 12) / capital_usd * 100
def estimate_commission_annual(self, commission_per_contract: float = 0.85,
contracts_per_trade: float = 1.0,
trading_days: int = 252) -> dict:
"""Estimate annual commission load."""
trades_per_year = self.trades_per_day * trading_days
commission_per_trade = commission_per_contract * contracts_per_trade
gross_commission = commission_per_trade * trades_per_year
round_trips = gross_commission * 2 # entry + exit
return {
'trades_per_year': trades_per_year,
'commission_per_trade': commission_per_trade,
'gross_annual_commission': round_trips,
'commission_as_pct_budget': (round_trips / self.annual_budget) * 100
}
def estimate_execution_slippage(self, spread_bps: float = 1.0) -> dict:
"""
Slippage model: spread + adverse selection.
spread_bps = half-spread in basis points.
"""
annual_trades = self.trades_per_day * 252
notional_per_trade = self.avg_trade_size
slippage_per_trade = notional_per_trade * (spread_bps / 10000)
annual_slippage = slippage_per_trade * annual_trades * 2 # round-trip
return {
'annual_slippage_cost': annual_slippage,
'slippage_as_pct_capital': (annual_slippage / self.capital) * 100,
'slippage_as_pct_budget': (annual_slippage / self.annual_budget) * 100
}
# Example: $25,000 account, $100/month budget
# Momentum strategy on US equities, 4 trades/day, $2,500 avg position
budget = QuantBudget(
capital_usd=25_000,
monthly_budget_usd=100,
strategy_type='momentum',
trades_per_day=4,
avg_trade_size_usd=2_500,
asset_class='us_equity'
)
print(f"Annual budget: ${budget.annual_budget:,.0f}")
print(f"Budget as % of capital: {budget.budget_as_pct_capital:.1f}%")
commissions = budget.estimate_commission_annual(commission_per_contract=0.0) # US equity: $0
slippage = budget.estimate_execution_slippage(spread_bps=0.5) # liquid US equity
print(f"\nCommission estimate: ${commissions['gross_annual_commission']:,.0f}/year")
print(f"Slippage estimate: ${slippage['annual_slippage_cost']:,.0f}/year")
print(f"Slippage as % of capital: {slippage['slippage_as_pct_capital']:.2f}%")
Run this model first. The output tells you what fraction of your $100/month is consumed by execution costs before you spend a single dollar on data. If slippage alone exceeds your annual budget at your current trade frequency and position size, the problem is not your data source—it is your strategy sizing.
Phase 2: Data Source TCO Analysis
Total cost of ownership for a data source has three layers:
| Layer | What it includes | What traders forget |
|---|---|---|
| Direct cost | Monthly subscription fee | Promotional pricing vs. renewal pricing |
| Indirect cost | Compute for processing, storage, network egress | Data normalization pipelines, timezone alignment |
| Opportunity cost | Time spent integrating, debugging, switching | Lock-in during critical strategy development periods |
Here is a concrete comparison of data source options relevant to small-capital quant traders:
| Data Source | Monthly Cost | US Equity OHLCV | Real-time Depth | Historical Depth | Trades (US) | Crypto | Notes |
|---|---|---|---|---|---|---|---|
| TickDB | Free tier / ~$25–$80 | 10+ years | L1 (US) | L1–L10 (HK, Crypto) | Not supported | Yes | Best value for US equity OHLCV + multi-asset |
| Polygon.io | $27–$200 | 15+ years | L2 | L2 | Yes (pay-per-tick) | Limited | Excellent US equity depth but costs scale with volume |
| Alpaca Data | $25–$100 | 5+ years | L2 | L2 | Yes | Limited | Tighter market focus; good for equity execution |
| Interactive Brokers | $0 (included) | Real-time only | Real-time only | Real-time only | Real-time only | Yes | No historical; requires IB account |
| Binance API | Free (rate-limited) | 1+ year | L1–L10 | L1–L10 | Yes | Yes | No US equity; strong for crypto strategies |
For a $100/month budget, the direct cost of TickDB's professional tier ($25–$80 depending on usage tier) leaves $20–$75 for compute and contingency. Polygon.io's entry tier at $27/month is competitive for US equities but adds compute overhead for cross-cycle backtests since its free tier limits historical depth.
Phase 3: Compute Allocation
With $100/month total and $25–$35 allocated to data, you have $65–$75 for compute. This is sufficient for a well-configured cloud setup:
# compute_cost_model.py
CLOUD_OPTIONS = {
'aws_t3_micro': {
'monthly_cost_usd': 10.71, # t3.micro on-demand, 750 hrs/month free tier
'vCPU': 2,
'RAM_GB': 1,
'network_gbps': 5,
'storage_gb': 30,
'suitable_for': 'Single-strategy backtesting + live monitoring'
},
'aws_t3_small': {
'monthly_cost_usd': 20.14,
'vCPU': 2,
'RAM_GB': 2,
'network_gbps': 5,
'storage_gb': 30,
'suitable_for': 'Multi-strategy with moderate data processing'
},
'digitalocean_droplet': {
'monthly_cost_usd': 6.0, # basic droplet
'vCPU': 1,
'RAM_GB': 1,
'storage_gb': 25,
'suitable_for': 'Single strategy, light backtesting'
},
'hetzner_cloud_ccx21': {
'monthly_cost_usd': 8.90, # 4 vCPU, 8 GB RAM
'vCPU': 4,
'RAM_GB': 8,
'network_gbps': 1,
'storage_gb': 80,
'suitable_for': 'Compute-intensive backtests; EU-based'
}
}
def allocate_budget(data_cost: float, compute_cost: float,
contingency_pct: float = 15.0) -> dict:
"""
Allocate $100/month across data, compute, and contingency.
contingency_pct reserves buffer for unexpected costs.
"""
total = data_cost + compute_cost
contingency_allowance = 100 * (contingency_pct / 100)
remaining = 100 - total - contingency_allowance
return {
'data_cost': data_cost,
'compute_cost': compute_cost,
'contingency': contingency_allowance,
'buffer': remaining,
'total_allocated': total + contingency_allowance,
'is_within_budget': total + contingency_allowance <= 100
}
# Scenario A: TickDB professional + AWS t3.micro
scenario_a = allocate_budget(data_cost=25, compute_cost=10.71)
# Scenario B: Polygon Starter + DigitalOcean droplet
scenario_b = allocate_budget(data_cost=27, compute_cost=6.0)
print("Scenario A: TickDB + t3.micro")
for k, v in scenario_a.items():
print(f" {k}: ${v:.2f}")
print("\nScenario B: Polygon + DigitalOcean")
for k, v in scenario_b.items():
print(f" {k}: ${v:.2f}")
The key insight is that compute costs are predictable and can be reduced further by using spot/preemptible instances for backtesting jobs. A t3.micro running scheduled backtests does not need to be online 24/7.
Phase 4: Signal Quality Adjustment
Raw cost comparison misses the most important variable: signal quality per dollar spent. A data source that costs twice as much but produces signals with 40% higher Sharpe generates better ROI.
The framework below weights each data source by the signal quality adjustment specific to your strategy type:
# signal_quality_weight.py
STRATEGY_DATA_QUALITY_WEIGHTS = {
'mean_reversion': {
'ohlcv_quality': 0.4, # Close price accuracy is critical
'depth_quality': 0.4, # Level 2 for spread detection
'tick_quality': 0.2, # Tick-level less important
'historical_depth_years': 3 # 3 years minimum for mean reversion
},
'momentum': {
'ohlcv_quality': 0.7, # OHLCV trend integrity is primary
'depth_quality': 0.1, # Depth less relevant
'tick_quality': 0.2,
'historical_depth_years': 5 # Need multiple market regimes
},
'arbitrage': {
'ohlcv_quality': 0.2,
'depth_quality': 0.3,
'tick_quality': 0.5, # Tick-level latency critical
'historical_depth_years': 2
}
}
def adjusted_data_score(vendor_score: dict, strategy_weights: dict) -> float:
"""
Compute a strategy-specific adjusted score for a data vendor.
vendor_score keys: ohlcv_quality (0-1), depth_quality (0-1),
tick_quality (0-1), historical_years, monthly_cost_usd
"""
quality_score = (
vendor_score['ohlcv_quality'] * strategy_weights['ohlcv_quality'] +
vendor_score['depth_quality'] * strategy_weights['depth_quality'] +
vendor_score['tick_quality'] * strategy_weights['tick_quality']
)
historical_bonus = min(vendor_score['historical_years'] /
strategy_weights['historical_depth_years'], 1.0) * 0.1
# Normalize cost: $0 = 1.0, $100 = 0.5, $200+ = 0.0
cost_score = max(0, 1.0 - (vendor_score['monthly_cost_usd'] / 200))
raw_score = quality_score + historical_bonus
cost_adjusted = raw_score * (0.6 + 0.4 * cost_score) # cost is 40% weight
return round(cost_adjusted, 3)
TICKDB_PROFILE = {
'ohlcv_quality': 0.95,
'depth_quality': 0.80, # L1 for US; L1-L10 for HK/Crypto
'tick_quality': 0.75, # No US equity tick data
'historical_years': 10,
'monthly_cost_usd': 25 # Professional tier base
}
weights = STRATEGY_DATA_QUALITY_WEIGHTS['momentum']
score = adjusted_data_score(TICKDB_PROFILE, weights)
print(f"TickDB adjusted score for momentum strategy: {score:.3f}")
Real-World Allocation Scenarios
Scenario 1: US Equity Momentum, $25,000 Capital, $100/Month
Strategy profile: Long-only, 4 trades/day, holding period 3–10 days. Requires 5+ years of OHLCV across multiple sectors for regime detection.
Allocation:
| Category | Cost/Month | Rationale |
|---|---|---|
| TickDB Professional | $25 | 10+ years of cleaned US equity OHLCV; covers the entire backtest period |
| AWS t3.micro | $11 | Scheduled backtest jobs; strategy runs locally during market hours |
| Monitoring (free tier) | $0 | CloudWatch free tier + free alerting tools |
| Contingency | $15 | Reserve for data overages or burst compute |
| Remaining buffer | $49 | Accumulates for annual infrastructure renewal |
What this setup does not buy: Real-time depth data. At this capital level and strategy frequency, the marginal signal from L2 depth does not justify the cost increase. The OHLCV data provides sufficient entry/exit timing for a multi-day momentum strategy.
Scenario 2: Crypto Mean Reversion, $15,000 Capital, $100/Month
Strategy profile: BTC/ETH pairs trading on Binance, 8–12 trades/day, holding minutes to hours. Requires depth data and real-time tick flow.
Allocation:
| Category | Cost/Month | Rationale |
|---|---|---|
| TickDB Standard | $15 | depth L1–L10 on Binance; 1+ year historical; sufficient for this strategy |
| Hetzner CCX21 | $9 | 4 vCPU handles both backtesting and live execution simultaneously |
| Binance trading fee tier | ~$15 (est.) | BNB fee discount active; actual cost varies with volume |
| Storage upgrade | $5 | Additional EBS volume for tick storage |
| Contingency | $10 | |
| Remaining buffer | $46 |
What this setup gains: TickDB's L10 depth on Binance provides the order book imbalance signal critical for mean reversion on crypto. The same budget on Polygon would not cover Binance depth access at this quality level.
Fetching and Processing Data: Production-Grade Implementation
Regardless of which data source you choose, the data fetching layer must be built to production standards. Here is a robust TickDB integration with proper connection handling, rate limiting, and error recovery:
# tickdb_client.py — Production-grade TickDB data fetcher
import os
import time
import random
import logging
from datetime import datetime, timedelta
from typing import Optional
import requests
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s [%(levelname)s] %(name)s: %(message)s'
)
logger = logging.getLogger('tickdb_client')
class TickDBClient:
"""
Production-grade TickDB REST client with exponential backoff,
rate-limit handling, and environment-variable authentication.
⚠️ For high-frequency or HFT workloads, replace with the WebSocket
client using the native ping/pong heartbeat mechanism.
"""
BASE_URL = 'https://api.tickdb.ai/v1'
MAX_RETRIES = 5
BASE_BACKOFF_SEC = 1.0
MAX_BACKOFF_SEC = 32.0
REQUEST_TIMEOUT = (3.05, 10) # (connect, read) in seconds
def __init__(self, api_key: Optional[str] = None):
self.api_key = api_key or os.environ.get('TICKDB_API_KEY')
if not self.api_key:
raise ValueError(
"TickDB API key required. "
"Set TICKDB_API_KEY environment variable or pass api_key argument."
)
self.session = requests.Session()
self.session.headers.update({'X-API-Key': self.api_key})
self.rate_limit_remaining = None
self.rate_limit_reset = None
def _backoff_duration(self, attempt: int) -> float:
"""Exponential backoff with full jitter."""
delay = min(self.BASE_BACKOFF_SEC * (2 ** attempt), self.MAX_BACKOFF_SEC)
jitter = random.uniform(0, delay * 0.1)
return delay + jitter
def _handle_rate_limit(self, response: requests.Response) -> Optional[dict]:
"""Handle HTTP 429 with Retry-After header."""
if response.status_code == 429:
retry_after = int(response.headers.get('Retry-After', 5))
logger.warning(f"Rate limit hit. Sleeping for {retry_after}s.")
time.sleep(retry_after)
return None # Caller should retry
return None
def _handle_api_error(self, data: dict, symbol: Optional[str] = None) -> None:
"""Standard TickDB error handler with typed exception raising."""
code = data.get('code', 0)
if code == 0:
return # No error
error_map = {
(1001, 1002): (
"Invalid or missing API key. Verify TICKDB_API_KEY environment variable. "
"→ https://tickdb.ai/dashboard"
),
2002: (
f"Symbol '{symbol}' not found. Verify via GET /v1/symbols/available "
"before requesting data."
),
3001: "Rate limit exceeded. Implement backoff before retrying.",
}
for codes, message in error_map.items():
if code in (codes if isinstance(codes, tuple) else (codes,)):
raise RuntimeError(f"TickDB error {code}: {message}")
raise RuntimeError(
f"Unexpected TickDB error {code}: {data.get('message', 'Unknown error')}"
)
def get_kline(self, symbol: str, interval: str = '1h',
start_time: Optional[int] = None,
end_time: Optional[int] = None,
limit: int = 1000) -> list:
"""
Fetch historical OHLCV (kline) data for backtesting.
Args:
symbol: Exchange symbol (e.g., 'AAPL.US', 'BTC.USDT')
interval: Candle interval ('1m', '5m', '1h', '1d', etc.)
start_time: Unix timestamp in milliseconds (optional)
end_time: Unix timestamp in milliseconds (optional)
limit: Max candles per request (max 1000)
Returns:
List of OHLCV candles: [timestamp, open, high, low, close, volume]
"""
params = {
'symbol': symbol,
'interval': interval,
'limit': limit,
}
if start_time:
params['start_time'] = start_time
if end_time:
params['end_time'] = end_time
url = f'{self.BASE_URL}/market/kline'
last_error = None
for attempt in range(self.MAX_RETRIES):
try:
response = self.session.get(
url,
params=params,
timeout=self.REQUEST_TIMEOUT
)
response.raise_for_status()
json_data = response.json()
if json_data.get('code') == 3001:
retry_after = int(response.headers.get('Retry-After', 5))
logger.warning(f"Rate limited. Retrying after {retry_after}s.")
time.sleep(retry_after)
continue
if json_data.get('code', 0) != 0:
self._handle_api_error(json_data, symbol=symbol)
candles = json_data.get('data', {}).get('klines', [])
logger.info(
f"Fetched {len(candles)} candles for {symbol} "
f"(interval={interval})"
)
return candles
except requests.exceptions.Timeout:
last_error = "Request timed out"
logger.warning(
f"Timeout on attempt {attempt + 1}/{self.MAX_RETRIES}"
)
except requests.exceptions.RequestException as e:
last_error = str(e)
logger.warning(
f"Request failed on attempt {attempt + 1}/{self.MAX_RETRIES}: {e}"
)
if attempt < self.MAX_RETRIES - 1:
backoff = self._backoff_duration(attempt)
logger.info(f"Retrying in {backoff:.1f}s...")
time.sleep(backoff)
raise RuntimeError(
f"TickDB request failed after {self.MAX_RETRIES} attempts. "
f"Last error: {last_error}"
)
def get_available_symbols(self, market: Optional[str] = None) -> list:
"""
List all symbols available for a given market.
Use this to validate symbol names before requesting data.
"""
params = {}
if market:
params['market'] = market
url = f'{self.BASE_URL}/symbols/available'
response = self.session.get(
url,
params=params,
timeout=self.REQUEST_TIMEOUT
)
response.raise_for_status()
data = response.json()
if data.get('code', 0) != 0:
self._handle_api_error(data)
return data.get('data', {}).get('symbols', [])
# Usage example
if __name__ == '__main__':
client = TickDBClient()
# Validate symbol before requesting data
available = client.get_available_symbols(market='US')
print(f"Found {len(available)} available US symbols")
# Fetch 6 months of AAPL hourly data for backtesting
end = int(datetime.now().timestamp() * 1000)
start = int((datetime.now() - timedelta(days=180)).timestamp() * 1000)
aapl_klines = client.get_kline(
symbol='AAPL.US',
interval='1h',
start_time=start,
end_time=end,
limit=1000
)
print(f"Retrieved {len(aapl_klines)} hourly candles for AAPL.US")
# Fetch full history in paginated chunks if needed
all_klines = []
current_start = start
while current_start < end:
batch = client.get_kline(
symbol='AAPL.US',
interval='1h',
start_time=current_start,
limit=1000
)
if not batch:
break
all_klines.extend(batch)
# Advance cursor to last timestamp in batch + 1 interval
last_ts = batch[-1][0]
current_start = last_ts + (60 * 60 * 1000) # advance by 1 hour
print(f"Total candles fetched: {len(all_klines)}")
Demand Forecasting: Avoiding the Mid-Month Trap
The most common budget overrun for small-capital quants is not choosing the wrong data source—it is underestimating usage growth. A strategy that starts with 4 trades/day may scale to 12 trades/day as the capital base grows. Data costs that looked fixed become variable within a single billing cycle.
Implement a usage tracker to forecast monthly consumption before the bill arrives:
# usage_tracker.py — Monthly data consumption forecasting
import os
from datetime import datetime, timedelta
from collections import defaultdict
class DataUsageTracker:
"""
Track TickDB API calls and estimate monthly cost.
Run this as a background process alongside your trading system.
Integration: Wrap your TickDB client calls or add logging hooks
to record each API request with timestamp and endpoint.
"""
def __init__(self, monthly_api_limit: int = 100_000,
monthly_cost_tier: float = 25.0):
self.api_calls = defaultdict(int) # endpoint -> count
self.start_of_month = datetime.now().replace(
day=1, hour=0, minute=0, second=0, microsecond=0
)
self.monthly_limit = monthly_api_limit
self.cost_tier = monthly_cost_tier
def record_call(self, endpoint: str):
self.api_calls[endpoint] += 1
def total_calls(self) -> int:
return sum(self.api_calls.values())
def days_remaining(self) -> int:
next_month = (datetime.now().replace(day=1) + timedelta(days=32)).replace(day=1)
return (next_month - datetime.now()).days
def projected_monthly_calls(self) -> int:
days_passed = (datetime.now() - self.start_of_month).days + 1
if days_passed == 0:
return self.total_calls()
daily_rate = self.total_calls() / days_passed
return int(daily_rate * 30.44)
def cost_warning(self) -> dict:
projected = self.projected_monthly_calls()
usage_pct = (projected / self.monthly_limit) * 100
cost_overrun_pct = max(0, usage_pct - 100)
return {
'calls_today': self.total_calls(),
'projected_monthly': projected,
'monthly_limit': self.monthly_limit,
'usage_pct': round(usage_pct, 1),
'days_remaining': self.days_remaining(),
'cost_overrun_pct': round(cost_overrun_pct, 1),
'overrun_warning': projected > self.monthly_limit
}
# Simulate usage tracking
tracker = DataUsageTracker(monthly_api_limit=100_000)
# Simulate API calls during backtest run
for i in range(1_500):
tracker.record_call('market/kline')
for i in range(200):
tracker.record_call('market/depth')
status = tracker.cost_warning()
print(f"Current calls: {status['calls_today']}")
print(f"Projected monthly: {status['projected_monthly']}")
print(f"Usage: {status['usage_pct']}% of limit")
print(f"Overrun warning: {status['overrun_warning']}")
Run this tracker in parallel with your strategy. If projected usage exceeds 80% of your tier limit mid-month, you have two weeks to optimize—either by caching data locally to reduce API calls, or by switching to a higher tier before the overage charges hit.
The Decision Matrix
Use this framework to make your specific allocation decision:
| Question | If Yes → | If No → |
|---|---|---|
| Does your strategy require US equity tick-level trades data? | TickDB does not support this. Consider Polygon or Alpaca. | TickDB is likely optimal for US equity OHLCV. |
| Do you need L2+ depth on US equities? | Polygon at $27+/month is the standard. | L1 depth on US from TickDB may suffice. |
| Is your primary asset class crypto? | Binance API free tier + TickDB for structured multi-asset view. | Evaluate asset-class-specific tiers. |
| Do you need 10+ years of OHLCV for backtesting? | TickDB's 10+ year history is a decisive advantage at $25–$80/month. | Shorter history is acceptable for most strategies. |
| Is your strategy HFT (holding period < 5 minutes)? | You need tick-level data and likely more than $100/month for data alone. | Slower strategies can use OHLCV-based entry signals. |
Server and Execution Cost Allocation: The Often-Ignored Half
Data costs get disproportionate attention because they are visible. Execution costs are invisible until they appear on your monthly P&L statement.
For a $25,000 capital account running a 4 trades/day momentum strategy with 0.5 bps average spread on liquid US equities, the annual execution cost is approximately $500–$800 (round-trip commissions + slippage). This alone represents 42–67% of your annual $100/month budget.
The implication is uncomfortable: if your strategy generates less than $800/year in gross alpha before execution costs, the strategy is not viable regardless of which data source you choose. The data source decision cannot rescue a strategy with insufficient edge.
Before committing to any data subscription, run this simplified gross alpha threshold calculation:
# alpha_threshold.py
def minimum_required_alpha(capital: float, monthly_budget: float,
slippage_bps: float, trades_per_day: int,
avg_position_pct: float = 0.10,
trading_days: int = 252) -> dict:
"""
Calculate the minimum gross alpha a strategy must generate
to break even on data + infrastructure costs.
"""
annual_data_infra = monthly_budget * 12
annual_trades = trades_per_day * trading_days
notional_per_trade = capital * avg_position_pct
slippage_per_trade = notional_per_trade * (slippage_bps / 10000)
annual_slippage = slippage_per_trade * annual_trades * 2 # round-trip
total_annual_cost = annual_data_infra + annual_slippage
# Minimum return on full capital required to cover costs
required_return = (total_annual_cost / capital) * 100
return {
'annual_data_infra_cost': annual_data_infra,
'annual_execution_cost_est': annual_slippage,
'total_annual_cost': total_annual_cost,
'min_return_required_pct': round(required_return, 2),
'min_return_dollar': total_annual_cost,
'break_even_alpha_per_trade_bps': round(
(total_annual_cost / annual_trades / notional_per_trade) * 10000, 2
)
}
result = minimum_required_alpha(
capital=25_000,
monthly_budget=100,
slippage_bps=0.5, # half-spread in bps
trades_per_day=4,
avg_position_pct=0.10 # 10% of capital per position
)
print("Break-even analysis:")
print(f" Total annual costs: ${result['total_annual_cost']:,.0f}")
print(f" Minimum return required: {result['min_return_required_pct']:.2f}% of ${result['min_return_dollar']:,.0f}")
print(f" Required alpha per trade: {result['break_even_alpha_per_trade_bps']:.2f} bps")
At $25,000 capital with $100/month budget and typical liquid equity execution costs, the break-even alpha requirement is approximately 1.8–2.4 basis points per round-trip trade. If your strategy's realized edge after execution is consistently below this threshold, the correct response is to reduce trade frequency or increase position sizing—not to switch data providers.
Conclusion: The Discipline of Constraint
A $100/month budget for a small-capital quant is not a limitation to work around. It is a forcing function that eliminates every strategy that does not have a genuine, durable edge.
The data source you choose matters, but it matters less than the discipline to verify before subscribing, the rigor to calculate total cost of ownership before signing up, and the intellectual honesty to abandon strategies that cannot cover their own costs.
TickDB's positioning—free tier for experimentation, $25–$80/month for production, 10+ years of US equity OHLCV, and multi-asset depth across crypto and HK markets—makes it one of the strongest candidates for the budget-constrained quant. But the strongest candidate only wins if you have done the preceding math.
Start with the cost model. Define the break-even alpha threshold. Then choose the data source that meets that threshold at the lowest total cost.
Next Steps
If you're an individual quant evaluating data costs for the first time, start with the free tier to validate symbol coverage and data quality for your specific strategy before committing to a paid plan.
If you're building a multi-asset strategy and need a single source for US equity OHLCV plus crypto and HK depth, visit tickdb.ai to review the professional tier pricing and symbol coverage documentation.
If you're currently paying $100+/month for data and want a cost comparison tailored to your strategy profile, reach out to the TickDB team with your current setup—many traders find they can reduce data costs by 40–60% without sacrificing the signals that actually drive their returns.
If you use AI coding assistants, search for and install the tickdb-market-data SKILL in your AI tool's marketplace to get TickDB API integration directly in your development workflow.
This article does not constitute investment advice. Markets involve risk; past performance does not guarantee future results. Cost figures and pricing are based on publicly available information and may change. Verify current pricing directly with vendors before making subscription decisions.