r"""
Xu and Livshits: the anatomy of a cryptocurrency pump-and-dump (2019)
=====================================================================

Telegram groups with tens of thousands of members announce a coin at a set
minute; members rush to buy it, and the price jumps within seconds. The
organizers bought it quietly beforehand, and sell into the rush. Xu and
Livshits studied 412 such pumps from 2018 and 2019, and predicted which
coin would be pumped from its market's features. On a constant-product
market the organizers' advantage is arithmetic: each buyer of
:math:`\Delta y` gets :math:`x \Delta y / (y + \Delta y)` coins at a
higher price than the one before, so those who buy first and sell first win
what the late buyers lose,

.. math::

   \pi_{\text{organizers}} + \sum_i \pi_i \approx 0 .

Pumps leave a trace in the public price and volume: a spike in both, far
above their recent averages.
"""

# %%
import matplotlib.pyplot as plt

import blockchainkit as bk
from blockchainkit.fraud.visualizers import plot_price_spikes

# %%
# One pump, hour by hour
# ----------------------

run = bk.fraud.simulate_pump_and_dump(hours=48, pump_hour=24, participants=40, seed=1)
print(f"organizers made {run.organizer_profit:+.2f} BTC")
print(f"{run.losing_participants} of {len(run.participant_profits)} participants lost money")
flagged = bk.fraud.detect_pumps(run.prices, run.volumes)
print("hours flagged:", flagged)
assert run.organizer_profit > 0 and run.losing_participants > 20
assert flagged == (run.pump_hour,)

fig, axes = plt.subplots(1, 2, figsize=(11, 4))
plot_price_spikes(run.prices, flagged, ax=axes[0])
axes[0].set(xlabel="hour", title="Price spike at the announcement")
axes[1].bar(range(len(run.participant_profits)), run.participant_profits, color="#dc2626")
axes[1].axhline(0, color="black", linewidth=0.8)
axes[1].set(xlabel="participant", ylabel="profit (BTC)")
axes[1].set_title("Most of the crowd pays for the organizers")
fig.tight_layout()

# %%
# When the organizers dump
# ------------------------

timings = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
profits = [bk.fraud.simulate_pump_and_dump(dump_after=t, seed=1).organizer_profit for t in timings]
assert profits[0] < profits[2]

fig, ax = plt.subplots(figsize=(6, 4))
ax.plot(timings, profits, "o-", color="#9333ea")
ax.set(xlabel="fraction of the crowd that has bought", ylabel="organizer profit (BTC)")
ax.set_title("Selling into the rush pays")
fig.tight_layout()

plt.show()

# %%
# Exercise
# --------
# Lower ``window`` and ``volume_ratio`` in
# :func:`~blockchainkit.fraud.systems.manipulation.detect_pumps` until hours
# without a pump are flagged too. How would you choose the thresholds when
# watching hundreds of coins at once?